As a Gen-Z student myself, I sometimes feel that many people don’t understand what secondary education is like in the digital age. This article is a way for me to share my perspective on how it’s affected me, and while I know the sentiments expressed may not be universal, I feel that the media could always use more firsthand accounts of important issues like this.

The Good

Before I go any further, let me mention that I’ve never used AI to complete assignments for me, as academic integrity is something I feel really passionate about. That being said, I’ve found generative AI to be a very useful supplement to my education. If I’m trying to study for a test, I can have ChatGPT quiz me on content and create flashcard templates for me to review. Likewise, if I’m stuck on a math problem, I can have it walk me through the steps to solving it without giving the answer away. AI can serve as an extension of my teachers, and I know plenty of peers who use it for the same purpose.

AI has also proven to increase productivity in the workplace. According to a study done by the Federal Reserve Bank of St. Louis, about 20.5 % of workers who have used generative AI said it saved them four hours or more per week. These platforms are turning into tools for the next wave of careers, and that’s why I believe AI education is so important during high school and college. My high school encourages AI use for studying and completing some assignments, and I think that it’s made a huge impact on my productivity as a student. I have learned to use it for basic tasks that would otherwise take hours, giving me more time to enjoy hobbies or spend time with family and friends.

The Bad

There are definitely downsides to the accessibility of AI in this generation, and one of the main ones is the ability to cheat or plagrarize using it. I’ve watched plenty of classmates turn in entire essays and assignments that they put zero effort into, and it’s really sad to see how often my peers give up learning opportunities just to save a couple minutes.

Teachers have taken notice of this, and they’ve had to implement solutions to combat the prevalent AI plagiarism that occurs. At my school, ZeroGPT and TurnItIn are the main platforms used to detect generative AI usage. The problem? These sites often get it wrong, punishing students who honestly complete their work. My teachers have previously called me in over assignments that were flagged as AI, where I then had to show them my edit history and vouch for full credit. The technology is still very new, and as a result, educational systems don’t have accurate ways to prevent and detect AI usage in a way that’s fair for everyone involved.

We can’t finish this article without mentioning the risk for false information. Despite having warnings at the bottom of almost every generative AI program, people often use ChatGPT as a search engine and take the information it returns very seriously. I personally have seen these platforms mess up math problems and spit out fake facts, which can be very detrimental to learning as a whole.

While others may have different experiences with AI and ChatGPT, this is how I’ve recognized its usage in my own life and in the lives of my peers. As the technology continues to develop and evolve, my opinion might change, but I believe that AI can be a very useful tool when utilized for the right reasons in education.

Interested in AI and technology related articles? Check out the other stories in my bio

While starting your own shop can be an exciting process, there’s no doubt that it can daunting at points. As someone who’s done it themselves, there were definitely some times where I felt like I was on top of the world, and others where I wanted to throw in the towel. Not to worry, because in this article I am going to give you a couple tips for starting and maintaining a successful shop. Let’s get into it!

Tip #1. Stay Organized

Being organized is one of the most important things for new shop owners, and I cannot stress how crucial it is at every step of the process. If you haven’t already, I would highly recommend getting a notebook to keep track of everything, from materials purchased to sales and everything in between. While Etsy does keep track of some of this stuff for you, it’s a good idea to write everything down just in case. When you need to price your goods or figure out your total profit, having all of the numbers right in front of you becomes super helpful.

Another thing that is a must for Etsy shop owners is to keep the recipts of every purchase you make for your shop. This can include the obvious things like materials and packaging, but also keep track of items like printer ink, because while you may also use it for other things, your Etsy shop is one of them. When tax season rolls around, you’ll be super grateful that you kept your recipts for tax writeoffs.

Tip #2. Listing Photos are Key

As a shop owner, your main goal is to attract potential customers and convince them that they need what you are selling. The most important way to do this is to take good quality listing photos that best display the product you are trying to sell.

Now don’t think that you need a professional setup to do this effectively. Pictured above is one of the photos for my best selling listing. My goal was to capture the whimsy of my frog, and by walking through my local park and taking pictures with my phone, I was able to get some good photos that make my listing look attractive to potential customers.

Tip #3. Customer Service & Boundaries

Etsy is a platform designed for customers to interact directly with sellers, and in order to build a strong reputation for your shop, you have to learn the basics of customer service. While the customer isn’t always right, it’s important to try and help them the best that you can. Make sure to take the time to respond to their messages and work with them to solve an issues that arrive.

Going along with this, don’t let customers take advantage of you by being too nice. It’s completely okay to say no to a custom order if it’s too much of a hassle. It’s important to uphold your boundaries, even if that means loosing a customer or two. If a buyer is being aggressive, there’s always an option to flag a message and Etsy will step in to help you.

Tip #4. The Little Things

Like mentioned before, customer service is the name of the game when it comes to running a successful Etsy shop. The little things matter a lot for the customer experience, especially when it comes to your packaging. Whether that means buying tissue paper that matches your brand, or including a small surprise with every order, the tinniest detail is what will stand out to your customers. For example, I like to include a handwritten note and a couple of stickers with all of my packages, but it’s completely up to personal preference. Just keep in mind what would make your target customer happy when they open up their order.

Tip #5. Sticky. Labels.

I know what you’re probably thinking — sticky labels? But yes, sticky labels are SO important that they qualify to be their own tip. Let me tell you a quick story: Back when I used to use printer paper and packing tape to secure my labels to my boxes, I prepared an order to be shipped to Oregon. Unfortunately, the package never made it there because the label fell off, and I never recieved my product back because the return address was also on the label. Not only did I have to send out another package, but I lost over $40 worth of profit because of it.

If you don’t want to be like me, I highly reccomend getting sticky labels. Not only do they make it harder for them to fall off, but they also make your life easier because you can just peel and stick. Here is one my favorite brands from Amazon that can be printed through a regular printer. For extra security, I reccommend putting packing tape on the border of the label.

Other Questions

While there are so many other things that go into having a good shop, these are just some of my favorite tips that I like to give newcomers to the Etsy business. If you have any further questions, feel free to reach out through my Etsy store at LavenderHazeKnits. Have a great day!

Ever since its launch in late 2022, OpenAI’s ChatGPT, a chatbot powered by generative AI, has become a worldwide phenomenon. Over 200 million people across the world use the platform on a weekly basis, and it’s very easy to understand why. From writing professional cover letters and resumes to proofreading code and essays, ChatGPT can do a multitude of human tasks. Just ask the 36% of US teens who actively use it to complete homework assignments according to the Pew Research Center. No matter your stance on the platform, you have to agree that ChatGPT has exploded in a way that has and will continue to infiltrate our daily lives.

Like any artificial intelligence platform with a massive user base, ChatGPT requires very large computers, called graphics processing units (GPUs), to store user data and generate outputs. While a GPU only takes up about 3,120 cubed centimeters of space, OpenAI has at least 250,000 of them that they use on a daily basis. These, alongside the other hardware necessary to run ChatGPT, proves that the platform uses more computers than you may have previously thought.

Problem

If you’ve ever used a laptop or other computer for too long, you’ve heard an internal fan turn on at some point. This fan plays a very important role, as it helps to prevent the overheating of hardware inside the computer. Without one, the computer could suffer from performance delays and even internal damage.

Like I mentioned previously, OpenAI uses a ton of computers to run ChatGPT, and just like any other computer, they run the risk of overheating if used for too long. With servers that operate 24/7, overheating is definite possibility unless the right steps are taken. Like I also mentioned earlier, these computers can be massive, so small internal fans would not be able to effectively cool them down. If traditional fans don’t cut it, then what is used to prevent overheating?

The answer is extreme amounts of electricity and water. Combined with the energy used to actually power ChatGPT’s servers and other computers, the company uses around 340 million watt-hours per day. On top of that, 85,000 gallons of water are used daily to combat the high temperatures that are created by the platform’s power consumption. To put this in more tangible terms, one prompt made to ChatGPT uses the amount of energy in a AA battery and almost 3 bottles of water.

Knowing that millions of people use the platform everyday, it’s easy to see how ChatGPT could be having a negative environmental impact. Many data centers like the ones that OpenAI uses still rely on fossil fuels, and because of this, ChatGPT is most likely a contributor to the growing CO2 emissions epidemic. Not to mention that our planet is desperate for water, and the water going towards cooling down GPUs could most definitely be used to help aid droughts and wildfires across the globe.

Potential Solutions

Just because this article only talks about ChatGPT and it’s impact, it doesn’t mean that OpenAI is the only company with this problem. In fact, almost every single AI company across the world deals with energy and water consumption issues like the ones previously mentioned. Despite this sounding concerning for the health of our planet, there are a few steps that can be taken to ensure this impact is not as extreme.

The most obvious of these solutions is to prioritize renewable energy when looking to power data centers and their cooling systems. Solar, wind, hydroelectric, and many other forms of clean energy provide power while producing almost no CO2, which is significantly better for our atmosphere.

Another option is for companies to invest in closed-loop water systems, essentially meaning that water gets recycled throughout the cooling process. That way, less water overall is used while data centers can continue to effectively cool down their computers.

The Future

As citizens of planet Earth, it is our responsibility to keep our planet safe for generations to come. While Chatbots and other AI platforms have made our lives significantly easier, they also have their faults, especially when it comes to our environment. We as a society need to find a balance between innovation and conservation, as it will allow us to progress without destroying the place we call home.

If you’re interested in similar articles about AI and our climate, be sure to check out some of my other stories in my bio :)

The answer to this is a very complicated one, as there are many pros and cons to clean energy that we as society often forget to consider. In this article, we’re going to look at some of these strengths and weaknesses to better understand why this topic is so complex.

Fossil Fuels

Fossil fuels have been used for the past 4,000 years to power elements of everyday life, from lanterns to steam engines and even entire factories. These fuel sources, which include coal, oil, and natural gas, are all very cost effective due to their high availability. This, along with the fact that humans tend to oppose change, are why fossil fuels have become so ingrained into our society as power sources.

Despite this, the use of fossil fuels is one of the most harmful things for climate change and our planet as a whole. In 2024 alone, carbon dioxide (CO2) emissions from fossil fuels totaled over 41.6 billion tonnes. This weight is the equivalent of 22.88 billion medium size cars floating through Earth’s atmosphere.

This extreme amount of CO2, which is increasing significantly year after year, is known to be one of the main causes of global warming. Like the name “greenhouse gas” suggests, carbon dioxide absorbs heat from the sun and traps it in the atmosphere, similar to the heat trapped in a greenhouse. The more CO2, the more heat that gets stuck, and the higher Earth’s temperatures increase. This is bad for a multitude of reasons, including rising sea levels and droughts, both of which have the potential to destroy thriving ecosystems and ways of life.

Clean Energy

The idea of clean energy, or using renewable resources with little carbon footprint, has existed since around 200 BC. Many ancient civilizations utilized the power of water, wind, and solar rays to complete everyday tasks, such as water wheels to crush grain and windmills to power irrigation. Despite the decline in these systems due to industrialization and the discovery of fossil fuels, clean energy forms have become of interest in recent years as the world faces the current climate crisis.

Some of the most common clean energy sources, such as solar and wind, may be very familiar, but there are many others that are being developed for widespread use. While sources of energy like geothermal and biomass might be less well known, they also offer a potential alternative to fossil fuels. Looking at both the upsides and downsides of these technologies can help us to form more educated opinions about clean energy, so let’s dive in!

Environmental Impact

As stated earlier, one of the biggest appeals of clean energy is its low environmental impact. According to a study done by NREL, if wind and solar energy made up at least 35% of all energy worldwide, CO2 emissions would be reduced by 25% — 40% . Knowing how high CO2 emissions are in this day and age, even a 5% reduction would have significant effects on our climate.

Although this may make clean energy seem like the ultimate solution for climate change, it’s important to note that these sources do not come without their own environmental challenges. Some clean energy technologies, including solar panels, wind turbines, and hydroelectric dams, require parts or materials that are created with fossil fuels. Even though the actual production of energy may be low-emissions, the processes before and after production can have the same harmful effects as fossil fuels.

Size & Location

It’s safe to say that clean energy production can take up a lot of space. The average solar farm stretches about 10 acres, while wind turbines can stand up to 415 feet tall. Not to mention systems like hydroelectric dams, which can stretch over entire bodies of water and turn previously viable land into reservoirs.

In cities like Beijing and Tokyo, which have some of the highest electricity consumption in the world, space for these large machines isn’t readily available. In most cases, large-scale clean energy systems need to be installed in remote locations, which makes it difficult to transfer the energy to places that need it the most.

Some forms of clean energy, however, have been adapted to work in areas with less space. Solar panels, as an example, have been developed to sit on the roofs of buildings and homes across the world. This not only provides a way to make clean energy accessible to everyone everywhere, but it also reduces the distance that generated electricity needs to travel. In a lot of cases, solar energy generated on a rooftop is used in the building below or somewhere close by. Because produced electricity does not have to be transported elsewhere, less fossil fuels are used overall.

Availability

From the sunny Pacific coast to the hot Sahara Desert and even the middle of Antarctica, elements of nature, such as solar rays and wind, can be found anywhere on planet Earth. This makes them extremely convenient as a source of energy, and that is exactly what clean energy aims to capitalize on. While fossil fuels can only be found and processed in specific locations, solar panels and wind turbines can be set up in any region of the world as long as there is enough space.

Not only this, but many of the elements used for clean energy are known as renewable resources. Unlike fossil fuels, which take millions of years to regenerate, natural phenomena like wind, moving water, and geothermal energy are almost always available. Because it is almost impossible to run out of these resources, it makes them a very dependable source of energy.

Despite this, clean energy still has its downsides in relation to reliability. For about twelve hours every day, the sun does not shine, and sometimes places with turbines have little to no wind. The unpredictability of nature matters a lot when it comes to clean energy, as energy output is completely contingent on something that is not in our control. In this regard, fossil fuels are a much more dependable source of energy because they can be burned whenever they are needed.

Economic Impact

Throughout the world, energy sectors play a large role in economies both big and small. Clean energy sectors are no different, and the increased need for climate action has only skyrocketed their impact. In many fields relating to clean energy, labor is required to install and maintain important pieces of infrastructure. New jobs worldwide are created to help fulfill this need, which contributes to economies across the globe.

On a more local scale, clean energy production also benefits smaller communities through initiatives such as government land-lease agreements and tax payments. Money generated through clean energy programs goes directly to the people and towns supporting them, boosting economies while also aiding individual citizens.

Clean energy, however, is still a newer energy form, which means that a significant amount of research must go into developing and perfecting the technology. Research and production can cost a great deal of money, which comes out of the pockets of governments and private companies. Instead of funding a concept that has not fully proven itself as being the most effective energy source, these organizations could use their money to support more trustworthy sources or issues that need more attention, such as war and poverty.

The Future

There’s no denying that global warming and CO2 emissions are going to get worse without immediate climate action, but the type of action we need to take is still subjective. Whether you support the increased use of clean energy, or believe that there’s a better alternative, steps need to be taken to preserve Earth for generations to come.

If you are interested in learning more about clean energy and how technology impacts it, be sure to check out my other articles :)

Are Wind Turbines Actually Beneficial? An Unbiased Look Into the Clean Energy Debate was originally published in Insights of Nature on Medium, where people are continuing the conversation by highlighting and responding to this story.

Java programs are everywhere. You may not even realize it, but some of your favorite online platforms, such as Spotify and Netflix, are built using Java, a versatile computer language created in 1995 by James Gosling. Java isn’t just used by big-name corporations, however. Anyone can learn Java, and while it takes time and practice, the language can be picked up relatively quickly.

Hi there! My name is Parker, and I’m here to help you understand the basics of Java as a part of my series on computer languages. To start building programs of your own, all you need to know is a few simple Java concepts. Let’s get started!

Data Types

In all programming languages, different data types exist in order to let the computer know what kind of data it’s dealing with so it can act accordingly. Here are some of the major data types in Java, and while there are many others, these are what you should know to start.

• Int: Ints, or integers, represent whole numbers that can be both positive or negative. Some examples include 10000 or -4.
• Double: Unlike ints, doubles store positive and negative numbers with decimal digits. Some examples include 3.14 or -9.0.
• String: Defined as a sequence of characters, Strings are textual data that include letters, symbols, and whitespace. Anything can be a String, including ints and doubles, as long as they’re wrapped in double quotes. Some examples include “Apple” or “I love Java!”.
• Boolean: Booleans are a data type that can only be one of two values — true or false. When used, they must be lowercase.

It’s also important to note that almost all lines in Java, besides a few exceptions mentioned later, must end with a semicolon. Without one, you’ll get an error and your program won’t run.

Print Statements

Print statements are sometimes known as the most crucial part of a program, because without them, nothing would be outputted to the user. In Java, there are two types of print statements that you can use, depending on how you want your information outputted — System.out.println() and System.out.print().

System.out.println()

System.out.println() outputs whatever is inside its parentheses to the console, whether it’s a String, int, double, boolean, or variable (more on those later!). With this type, the following print statement gets printed on the next line. In the example below, apple, 6, and 7.6 all get printed on separate lines

System.out.print()

System.out.print() is very similar to System.out.println(), except it prints the following statement on the same line. In the example below, apple67.6 would be printed to the console.

Make sure to note that what statement you choose doesn’t affect that line itself, but instead the line that comes after it!

Declaring and Assigning Variables

Printing is very useful, but what happens if you want to store a value to use later in your program? This is what variables are designed to do, as they can store any type of data, including the ones we didn’t mention. There are a few rules for naming them, however, so let’s break them down:

• Variable names in Java are camel-case, meaning that instead of whitespace a capital letter is used to indicate a break in between words. The first word remains entirely lowercase, but from then on any new word must start with a capital letter.
• Variable names must start with a letter, but can contain numbers and symbols after.
• Variables are case-sensitive, for example NAME, Name, and name are not the same as each other.

To declare a variable, you must write the type of data you intend on storing, the variable name, and an equal sign followed by the value.

int numOfApples = 6;

String greeting = “hello!”;

In the first example above, we must write out int to indicate that we want to assign an integer value to the variable numOfApples. Don’t forget a semicolon!

Numerical Operations

Math plays a big role in the world of coding. Whether you need to perform basic arithmetic or track how many times the letter “a” appears in “aardvark”, math and numerical operators come up frequently in Java and all other programming languages. Here are the main ones and their corresponding symbols:

• Addition (+)
• Subtraction (-)
• Multiplication (*)
• Division (/)
• Exponents (**)
• Modulus (%)

Most of these operators should be familiar, but modulus probably isn’t. Modulus, represented by %, is used to find the remainder of a number divided by another number. If we solved the problem 22 % 3, as an example, the answer would be 1. This is super useful in many situations, including identifying whether a number is even or odd. If the number % 2 equals 0, it’s even, and if it equals 1, it’s odd!

Conditional Statements

In real life, we often make decisions based on outside circumstances, with “I will fly my kite if it’s windy out” and “Sally will get an ice cream cone if she cleans her room” being two examples. What if we want to do the same thing in our code? That’s where conditional statements come in.

If statements

Like in the instances mentioned above, the word “if” is often a word used to describe a conditional scenario. If statements in Java are designed with the exact same logic, using the keyword if and a conditional statement wrapped in parentheses. This is followed by a set of curly braces, and whatever is inside is called the conditional body. The first line of conditional statements are one of the exceptions to the semicolon rule, but the lines inside of the conditional body must have them.

While our example uses greater than to compare num1 and num2, any of the conditional operators can be used to determine whether a statement is true or false. Here’s what they look like in code:

• Greater than (>)
• Less than (>)
• Greater than or equal to (>=)
• Less than or equal to (<=)
• Equal to (==)
• Not equal to (!=)

With any conditional statement, the lines within the curly braces only occur if the conditional returns true. If num1 was equal to 7 and num2 was equal to 3, the print statement below it would run. If they were 2 and 9, respectively, however, the line “num1 is larger” would not print.

Else if and else

An else if statement is fairly similar to an if statement, as they both check to see if a conditional is true before executing a block of code. However, else if statements only run if the preceding statements have returned false. They are formatted the same as if statements, using the keyword else if, a conditional wrapped in parentheses, and curly braces. As displayed below, the program would only check to see if num1 is less than num2 if the previous statement returned false. One if statement can be followed by as many else if statements as necessary.

Else, on the other hand, functions as a catch all if all other statements return false. Unlike if and else if, else statements do not have a conditional, and are instead followed by curly braces. In our example, the only way the else statement would run is if num1 was equal to num2. With an else statement, we can ensure that every situation is taken care of and something will happen when we run our code.

If, else if, and else statements can be used in many different combinations. If, if-else, and if-else if-else are all possible ways that you can implement conditionals in your code depending on what you need to check for.

For and While Loops

For basic Java programs, everything we’ve covered so far is all you really need. To do something like printing the numbers 0 through 5, we could just use six System.out.println() statements, each printing a different number on a different line. What if we increased that number to 50 or 100? Using individual print statements would take us a long time to type and would clutter our code.

For loops and while loops in Java are used for this exact purpose, helping us to repeat certain pieces of code that we want to execute over and over again. Not only do these loops save a significant amount of time, but they also simplify our code.

For Loops

For loops work by repeating a piece of code a set amount of times, depending on what you need to achieve. If you look at the example below, you’ll see that they’re written out by using the word for followed by a set of parentheses. Inside, the for loop header consists of the loop variable, which keeps track of the loop’s iterations, a loop condition, and an incrementor for the loop variable, all separated by a semicolon.

Before the loop runs, it checks to make sure that the loop variable meets the loop condition, and then executes whatever is in the curly braces below it. Afterwards, the loop variable is incremented based on the loop incrementor, and the process repeats itself over and over again.

While Loops

Another form of iteration is the while loop, which unlike a for loop, runs as long as a set condition is true. As shown in the example below, these loops are written using the word while, the while loop condition, and a set of curly braces. While loops execute whatever is inside those braces until their condition is false.

In the example above, the while loop prints out the numbers 0 through 5 because of our condition, i < 6. In each iteration, i increases by 1, and when i reaches 6, the loop breaks because the condition is made false.

Variables in while loops are much different from for loops, as they need to be declared, assigned and incremented by the programmer. The letter i is a common variable name for while loops, but similar to for loops, it can be called whatever you want it to be. In our example, the variable i is assigned to 0 using an equal sign.

Making sure to increment the variable inside the loop is crucial, as if you don’t, you run the risk of creating an infinite loop. If the condition inside of the while loop is never made false, the loop will never end, eventually leading to your code crashing. Infinite loops should always be avoided, so look out for them while coding.

Methods

If you’ve seen a Java program or maybe have even built your own, you’ve probably noticed that every program starts off with something like this:

public static void main(String[] args){

But what exactly does this mean? In Java, all code belongs to methods, or blocks of code designed to perform certain tasks. Statements like the one above are called method headers, and their job is to outline to the computer what exactly the method is going to do.

Every program in Java must contain a main method, where the majority of code is written. Inside, you can use any of the elements mentioned throughout this article, and even call other methods. The statement mentioned above is called the main method header, and the image below displays what code in the main method would look like.

It’s also possible to make methods of your own, and while this can be useful for repetitive tasks, they’re not all that necessary to know when you’re just starting out. If you’re interested in how to build your own methods, this article is great :)

How to Get Started

While there’s so much more to learn about Java, the concepts mentioned above are a good starting point for building basic code. Understanding print statements, conditionals, and iteration are crucial to learn more complex concepts later on. The best way to learn about any coding language, however, is by practicing through building your own programs. Here’s some tips for getting started with coding Java:

• Some free compilers, or places to code Java, include Intellij or Visual Studio Code. If you can’t download programs onto your computer, I highly recommend using OneCompiler (linked here), which runs on an internet browser.
• If you ever get an error while coding or are confused about what to do next, google it! Stack overflow and reddit are two good places to look if you encounter an issue.

If you’re interested in learning about other coding languages and technology as a whole, check out some of my other stories in my profile :) Happy coding!

If you’re active on social media, chances are you’ve interacted with Python programs. Platforms such as Instagram, Facebook, Reddit, and Pinterest are written in Python, but what exactly does that mean? Created in 1991 by Guido van Rossum, Python is a computer programming language known for its readability and use of indentation. It’s become very popular amongst developers for building websites, machine learning models, and data science models since its creation, and its popularity is only increasing with time. While starting may seem intimidating, learning it is fairly simple.

Hi there! My name is Parker, and I’m here to help you understand the basics of Python as a part of my series on computer languages. As one of the most learner-friendly languages out there, all you need to know is a few simple concepts in order to start building programs of your own. Let’s get into it!

Data Types

In any programming language, different data types exist in order to help the computer identify what kind of data it’s dealing with and act accordingly. Here are some of the major data types in Python, and while there are many others, these are all you need to get started.

• Int: Ints, or integers, represent whole numbers that can be both positive or negative. Some examples include 10000 or -4.
• Float: Unlike ints, floats store positive and negative numbers with decimal digits. Some examples include 3.14 or -9.0.
• String: Defined as a sequence of characters, Strings are textual data that include letters, symbols, and whitespace. Anything can be a String, including ints and floats, as long as they’re wrapped in either single or double quotes. Some examples include “Apple” or “I love dogs!”.
• Bool: Bools are a data type that can only be one of two values — true or false. When assigned to a variable in Python, they must be capitalized.
• List: Just like any other list in the real world, lists in Python are collections of data grouped together. They can contain a combination of any of the previously mentioned data types, each separated by a comma, and the whole thing is enclosed in brackets. An example is [“strawberry”, “banana”, “grape”].

Print Statements

Arguably the simplest and most crucial part of a program, print statements allow information to be printed, or displayed, to the user interface. Using print(), as shown, you can print any data type or variable (more on this later) to the console and have it displayed for the user. When we run the code below, we get “Dog”, “True”, and “9.8” all on separate lines. Note that the quotation marks are not printed.

Declaring and Assigning Variables

Let’s go back to earlier, when we briefly mentioned variables. What exactly are they? Variables, in essence, are pieces of code that store a certain value. This value can be any of any data type, including the ones that we didn’t mention. There are certain rules for naming variables, however:

• Variables in Python are snake-case, meaning that instead of whitespace an underscore is used to indicate a break in between words.
• Variable names must start with either a letter or underscore, but can contain numbers and symbols as well.
• Variables are case-sensitive, for example NAME, Name, and name are not the same as each other.

To assign a value to a variable, use an equal sign, as displayed in the example

num_Of_Computers = 4

where num_Of_Computers is the variable name and 4 is the int value assigned to it.

Variables are extremely important for storing data that we want to use later in the program, and are mutable. For example, if we are totaling the amount of candy in a jar, we’re going to want to access that number later and add to it.

Numerical Operations

Math plays a big role in the world of coding. Whether you need to add up candy, like described above, or track the amount of “a”s that are in “abracadabra”, math and numerical operators come up frequently in Python and all other programming languages. Here are the main ones and their corresponding symbols:

• Addition (+)
• Subtraction (-)
• Multiplication (*)
• Division (/)
• Exponents (**)
• Modulus (%)

Chances are the majority of these operators sound familiar, except for modulus. Modulus, represented by %, is used to find the remainder of a number divided by another number. If we solved the problem 26 % 3, as an example, the answer would be 2. Unlike division, modulus returns what is left over after dividing two numbers. This is super useful in many situations, including identifying whether a number is even or odd. If the number % 2 equals 0, it’s even, and if it equals 1, it’s odd!

Conditional Statements

In life, we often make decisions based on outside circumstances, with “I will go for a walk if it’s sunny out” and “Thomas will go to the party if he finishes his homework” being two examples. What if we want to do this in our code? That’s where conditional statements come in.

If

Like in the instances mentioned above, the word “if” is often a word used to describe a conditional event. In Python, if statements are used to do the exact same thing in code.

As shown above, if statements are used by typing out the word “if”, the conditional, and then a colon. While our example uses greater than to compare num_1 and num_2, any of the conditional operators can be used to determine whether a statement is true or false. Here’s what they look like in Python:

• Greater than (>)
• Less than (>)
• Greater than or equal to (>=)
• Less than or equal to (<=)
• Equal to (==)
• Not equal to (!=)

With any conditional statement, the lines indented underneath it only occur if the conditional returns true. If num_1 was equal to 7 and num_2 was equal to 3, the print statement below it would run. If they were 2 and 9, respectively, however, the line “num_1 is larger” would not print.

Elif and Else

Elif statements, short for else-if, go after a single if statement and only run if the preceding statements have returned false. They are formatted the same as if statements, using the keyword elif, a conditional, and a colon. As displayed below, the program would only check to see if num_1 is less than num_2 if the previous statement was untrue. One if statement can be followed by as many elif statements as necessary.

Else, on the other hand, functions as a catch all if all other statements return false. Unlike if and elif, else statements do not have a conditional, and are instead followed by only a colon. In our example, the only way the else statement would run is if num_1 was equal to num_2. With an else, we can ensure that all of our bases are covered and something is guaranteed to be returned.

If, elif, and else can be used in many different combinations. If, if-else, and if-elif-else are all different setups that you can implement in your code depending on what you need to check for.

Iteration and Repetition

So far we’ve covered the basics of Python, and for basic programs, they’re pretty much all you need. If we wanted to print the numbers 0 through 5, we could just use six print statements, each printing a different number. What if we changed that 5 to 50 or 100, however? Using multiple print statements now seems extremely tedious and messy.

In Python, for loops, while loops, and functions help us to repeat certain pieces of code that we want to execute over and over again. Not only does this make our code cleaner, it also saves us time while programming. Let’s break down what each of these do.

For Loops

For loops work by iterating through a list or set of numbers a certain amount of times, depending on what you need to achieve. If you look at the examples below, you’ll see that they’re written out by using the word “for”, a variable name, the word in, and the group of things you want to iterate through followed by a colon. Every time the for loop runs, it executes whatever is indented below it.

Variables in for loops are defined inside of the loop, and are incremented each iteration until the loop is completed. While x is commonly used as a variable name, variables can be named anything as long as it follows the variable naming rules mentioned earlier.

Two commonly used sequences that for loops iterate through are lists and numbers. The first example above uses a list, called fruits, and the for loop prints out the name of each fruit on a separate line. The second example prints out the numbers 0 through 5, and uses a popular function called range(). In a for loop, range() is used to iterate through numbers, starting at 0 and ending with whatever number is inside of the parentheses.

While Loops

Another form of iteration is the while loop, which unlike a for loop, runs as long as a set condition is true. As shown in the example below, these loops are written using the word “while”, the condition, and a colon. While loops execute whatever is indented below them until their condition is false

In the example above, the while loop prints out the numbers 0 through 5 because of our condition, i < 6. In each iteration, i increases by 1, and when i reaches 6, the loop breaks because the condition is made false.

Variables in while loops are much different from for loops, as they need to be declared, assigned and incremented by the programmer. The letter i is a common variable name for while loops, but similar to for loops, it can be called whatever you want it to be. In our example, the variable i is assigned to 0 using an equal sign.

Making sure to increment the variable inside the loop is crucial, as if you don’t, you run the risk of creating an infinite loop. If the condition inside of the while loop is never made false, the loop will go on forever and ever, and will eventually crash your code. Infinite loops should always be avoided, so look out for them while coding.

Functions

Some of the concepts we’ve already covered, such as print() and range(), are pre-installed functions in Python that are used constantly by programmers. The question is, what exactly is a function? Functions are pieces of code that perform a specific task, but only run when called in a program. While there are plenty of built-in functions available, it’s also very important to know how to build your own.

To create a new function, as shown above, the key word “def”, the function name, and parentheses for parameters, and a colon are used. Parameters are values, imputed when the function is called later on, that are used inside of the function. If none are needed, the programmer leaves the parentheses blank.

The body of a function, or whatever is indented below the function declaration, is what occurs when the function is called. In our example, whatever String is stored in the parameter “word” is printed. Printing is not the only thing that can happen in a function, however. If we want to store or use a value from inside the function in other parts of code, the keyword return is used. In the function add_Together, num_1 and num_2 are added together and returned.

To call a function, we write out the function name and parentheses, with any parameters that the function has inside them. If we return something in our function, we must store its call inside of a variable. If it doesn’t return something, we can just call it on its own line without assigning it.

How to Get Started

While there’s so much more you could learn about Python, the concepts mentioned above are a good starting point for building basic code. Understanding print statements, conditionals, iteration, and functions are crucial to learn more complex concepts later on. The best way to learn about any coding language, however, is by practicing through building your own programs. Here’s some tips for getting started with coding Python:

• Some free IDEs, or places to code Python, include PyCharm, Visual Studio Code, and Thonny. If you can’t download programs onto your computer, I highly recommend using Google Colab, which runs on an internet browser.
• If you ever get an error while coding or are confused about what to do next, google it! Stack overflow and reddit are two good places to look if you encounter an issue.

If you’re interested in learning about other coding languages and technology as a whole, check out some of my other stories in my profile :) Happy coding!

If I asked someone how they thought the human race will end, they’d probably come up with a few answers that sound like they’re straight out of a movie: world-wide famine, severe natural disaster, extinction of crucial species, or even unlivable temperatures.

What if I told you that all of these are possible, and will most likely happen by 2100? This is planet Earth’s reality, and we owe it all to climate change.

By definition, climate change refers to long-term shifts in temperatures and weather patterns. Even though these changes can be natural, for the past two centuries human activities have sped them up significantly. The number one accelerator, as most of us know, is fossil fuels and their carbon dioxide emissions.

Current Methods for Carbon Capture

With a problem as large as this, it’s easy to assume that solutions already exist to combat it. One of the most prevalent ones is direct air capture (DAC), which has been developed since 1999. This technology works to capture CO2 directly from the atmosphere, and then store it underground or reutilize it for other purposes. While this method is about 90% effective, it doesn’t come without its issues.

DAC is a very energy intensive process, with the majority of the electricity used stemming from fossil fuels. This process has been shown to emit even more CO2 than it captures in some instances, counteracting its environmental impact. Additionally, manufacturing costs and the price of electricity make it extremely expensive, reducing the incentive for corporations and individuals to buy it. What if both of these issues could be solved, all with a nanoparticle membrane?

EcoCapture and its Capabilities

Let us introduce to you EcoCapture, a gas separation membrane that filters out carbon dioxide from other elements in the atmosphere and contains it. From there, the CO2 can be stored underground or used for other purposes. With this technology, we aim to reduce the impacts of climate change and global warming worldwide.

Our technology revolves around MXene (Max — ene) membranes, which are created by etching layers out of MAX phases. Let’s dive a little bit deeper into what each of these are:

MAX Phases

MAX phases are a group of materials that have unique properties, blending characteristics of both metals and ceramics. The name “MAX” stands for the combination of three elements: M for a transition metal, A for an A-group element on the periodic table, and X for carbon and/or nitrogen. The most common MAX phase is titanium aluminum carbide (Ti3AlC2), where the M is titanium, the A is aluminum, and the X is carbon. They’re known for being strong, heat-resistant, and able to withstand extreme conditions, which makes them perfect for gas separation.

MXene Membranes

An MXene membrane is a type of membrane made from MXene, a two-dimensional material derived from MAX phases. These membranes are usually composed of stacked layers of MXene nanosheets arranged in a specific configuration. The question is, how do we get those nanosheets?

Based on the image above, here is a breakdown this process:

1. Pieces of the ‘A’ layer (aluminum in this case) are selectively removed from MAX phases using a hydrofluoric acid (HF solution). This leaves behind a stack of transition metal carbide/nitride layers.
2. Using sonication (high frequency sound waves) or handshaking causes the individual layers to swell and separate to form MXene nanosheets.

Once fully constructed, these membranes have pores similar to a sponge, allowing them to be useful in spaces like water treatment, electronics, and energy storage. This property also makes it a good candidate for gas separation.

MXene membranes for Separating Hydrogen Gas

In 2018, researchers at Drexel University discovered that MXene membranes have the ability to passively filter out and capture hydrogen gas. To do this, they used a unique type of hydrofluoric acid while chemically etching out the MXene nanosheets. This created nanopores that fit the molecular shape of hydrogen, allowing those molecules to pass through while blocking other gases. Based on this finding, what’s stopping us from using a different acid to capture other gases, like CO2? That’s our mission at EcoCapture.

Hydrogen vs. Carbon Dioxide Molecules

Hydrogen and CO2 molecules exhibit fundamental differences in their chemical compositions and structures. Hydrogen molecules consist of two hydrogen atoms and has a low molecular weight, while CO2 molecules consist of one carbon atom bonded to two oxygen atoms and a higher molecular weight. The size difference between the two is also drastic, with CO2 molecules being about 4.69 times larger than hydrogen.

Because of this, materials used for creating membranes to contain hydrogen will usually not work for carbon dioxide. While the ones used by Drexel University were chemically etched to be useful for hydrogen, gasses of different shapes and sizes need to be etched differently using different hydrofluoric acids

Modifying MXene Membranes for CO2 Capture

We’ve covered the bases of the technology involved in our solution, but how exactly do we utilize it? Eco Capture involves using these MXene membranes, with a few tweaks, to capture CO2 directly from the atmosphere. Let’s break down our solution a bit more:

Using Different Hydrofluoric Acids to Control Pore Size

As mentioned previously, pores are one of the key ways to capture CO2, as this is where all atmospheric gas attempts to enter, but only certain molecules can remain there.

The best way to explain this process is to compare it to a child’s shape sorter toy, with the square hole being our “pore”. If we tried to fit the long, rectangular peg in there, it would obviously not fit and we would put it to the side. While the circular peg would fit through that hole, it would immediately fall through and land on the floor. If we placed the square peg there, however, it would fit perfectly and stay in the hole.

By creating membranes using a unique type of hydrofluoric acid, EcoCapture’s MXene nanosheets contain nanopores that mirror the linear shape of CO2 molecules. This makes it easier for them to get “stuck” in the pores of the membrane and allows smaller molecules to pass through while preventing bigger molecules from entering altogether.

Using Amino-functionalization for Attracting CO2

In addition to pore size, one of the other main ways EcoCapture membranes attract CO2 is through amino-functionalization, or adding amino (-NH2) groups to the surface of the MXene membranes.

These amino groups attract carbon dioxide, similar to a magnetic force, and form hydrogen bonds with the molecules to ensure they stay inside the membrane. Because amino groups selectively bond with CO2 over other gasses in the atmosphere, there is a smaller chance of other gases becoming captured in the membrane.

Removal and Utilization of Captured CO2 From the Membranes

With our membranes, captured carbon dioxide remains in the membrane until it is forcefully removed through a process called vacuum regeneration. During this, the membranes are subjected to a light vacuum suction, lowering the partial pressure of CO2 surrounding the membrane and driving the desorption of CO2 molecules from the membrane surface. This vacuum suction continues and guides the carbon dioxide to a storage container.

From there, the gas can be stored or used in multiple ways. The most economically feasible option is to store it underground, similar to current methods of carbon capture. It’s also possible, however, to reutilize this captured CO2 in fuels, concrete, and more. While EcoCapture focuses on the capture of carbon dioxide and not it’s use cases, in the future we may look into how we can reuse captured gas.

Membrane Placement and Their Versatility

One of the most beneficial aspects of MXene membranes is the temperatures they can effectively operate in, which range from 68–212 degrees Fahrenheit (20–100 degrees Celsius). In many regions across the world, the everyday climate fits this range, meaning that EcoCapture membranes could be used there year-round.

Additionally, our membranes can be smaller than most carbon solution and don’t require an industrial plant to operate them. Depending on the usage of captured carbon, EcoCapture membranes can be placed almost anywhere with the right climate, including major cities.

Benefits of This Over Other CO2 Extraction Methods

Unlike direct air capture, our membranes filter through air passively, meaning that no energy is required in the process of capturing the CO2. While it would take electricity to produce the membrane, transport the gas, and utilize it for other purposes, we can ensure that this would come from non-carbon emitting resources, like solar or wind.

Our Role in the Fight Against Climate Change

Don’t get us wrong — preventing carbon dioxide from ever reaching the atmosphere, whether through clean energy sources or carbon capture, is essential to ensure climate change doesn’t worsen. Without addressing the CO2 that’s currently there, however, there’s little hope of society actually improving the climate crisis.

With EcoCapture, we are able to collect carbon directly from the atmosphere, allowing the world to make a dent in the fight against rising CO2 emissions. All of the negative impacts of climate change, from rising temperatures to increased natural disasters, would be reduced, creating a brighter future for generations to come.

If you want to learn more about EcoCapture, click here to view our website :)

TLDR:

• EcoCapture utilizes MXene membranes for efficient carbon dioxide (CO2) capture.
• MXene membranes are tailored for selective CO2 filtering while allowing other gases to pass through.
• The technology operates passively, requiring no additional energy for CO2 capture.
• Captured CO2 can be stored underground or repurposed for various applications.
• EcoCapture aims to mitigate the impacts of climate change by directly addressing atmospheric CO2 levels.
• MXene membranes offer scalability and operational flexibility, making EcoCapture a transformative solution for combating climate change.

Power grids are incredibly intricate machines with many different components to take into consideration. One of these is all the different electricity sources out there, which need to be balanced for grids to function properly.

Doing this can be a challenge, as electricity demands can fluctuate and decline depending on the time and location. The same goes for clean electricity sources, while they’re the best option for slowing climate change, their production can be somewhat unpredictable.

This isn’t ideal for power grids, however, as they need to consistently match the demand for energy while taking into account the many electricity imports they receive. With artificial intelligence, predictions of both inputs and outputs can be made simultaneously and efficiently.

What is Artificial Intelligence?

Chances are, you’ve probably heard of AI before, whether about its amazing capabilities or how it’s going to take over the human race. But what exactly is it?

Artificial intelligence is a technology meant to simulate human intelligence by analyzing inputted data and recognizing patterns. When exposed to new information, it can use these patterns to make predictions. Linear regression models, as displayed in the image above, are one of the simplest forms of AI. They graph the relationship between independent and dependent variables and draw a line of regression, or line of best fit, to show their overall correlation. Using this line, they can predict what the dependent value will be based on the given independent value.

Using AI for Power Grid Prediction

Going back to the issue I described earlier, I built three linear regression models that, when put together, output how many watt-hours of fossil fuels are needed to meet electricity demands in Pennsylvania.

While each model takes the same input, a particular month and hour, they each have different outputs. The first one predicts watts of solar electricity per hour, the second one predicts watts of wind electricity per hour, and the third estimates the hourly electricity demand in PA (the second, third, and fourth blocks of code).

As shown in the image above, the outputs of model 1 and model 2 are subtracted from the output of model 3. The resulting number is an estimate of the watts of fossil fuels needed to meet the electricity demand per hour.

I coded these in Google Colab and used scikit-learn, a popular software for machine learning. The average mean squared error (MSE) was 2369.57, which is relatively high for a group of models like this. The bulk of this comes from the solar electricity model, which had an MSE of 7063.97. By testing out different types of machine learning models, I believe that I can decrease this rate in the future.

If you are interested in checking out the code itself, here is the link :)

While building, one of the many challenges I faced was trying to create my dataset. I have only ever worked with premade datasets, so it was a new experience for me to build my own. After a lot of trial and error, I was able to design a file that produced accurate predictions. As shown above, I ultimately used the categories Month and Hour to predict Solar, Wind, and Electricity.

Impact

To make a real impact in the fight against climate change, one of the best things the world can do is replace our carbon-emitting electricity sources with more environmentally friendly ones. The predictions made from a model like the one I built can help to do this, as it ensures that the least amount of fossil fuels necessary are used in PA.

While there are a few companies doing something similar to this currently, they are either predicting just energy demand or just energy production. By doing both, not only are we able to figure out the amount of fossil fuels we need, but the accuracy of these predictions will also be a lot higher.

Potential Problems

Even though technology such as this is very useful, my models are far from perfect. Here are some of the main issues they present, and how they could be resolved:

• Only takes into consideration two forms of clean energy — In reality, there are most likely more than just two clean electricity sources that play a role in the Pennsylvania power grid. I only chose to include wind, solar, and fossil fuels to keep my dataset simple, but we could expand the dataset to include others, such as hydroelectric or geothermal electricity.
• There are many different types of fossil fuels — Coal, petroleum, natural gas, and oil shale are just a few examples of the many different types of fossil fuels that exist. While my model only outputs an estimate of these things combined, with research and some updates to my dataset we could separate them based on the type.
• It assumes that weather conditions are perfect — Solar and wind electricity, while useful, can be significantly influenced by the weather. For my model, I assumed that weather conditions were about average for both forms, but that means its predictions would not be accurate on abnormal days. To fix this, we could add another section to my dataset to describe the weather and add some new data points that display energy generation in poor conditions.

Future Projects

In the future, I want to continue to look into the ways that artificial intelligence can impact clean energy and the fight against climate change. One area I am especially interested in is hydropower, as I have never worked with it and I believe it has a lot of potential.

While making clean energy the world’s main source of electricity is a complicated task, the benefits will be life-changing. With the risks of fossil fuels, severe global warming, and environmental disasters minimized, society would be able to shift more of its focus toward other important issues. Technology is key to eventually reaching this goal, allowing a brighter future for all.

We can all tell if a car has not been well maintained — rust, peeling paint, and broken bumpers are all giveaways that a vehicle could use some TLC. It may still be able to make morning commutes, but its faults are telltale signs that it may be older and more prone to other issues.

One can make inferences about anything using sight, from leftovers in the fridge to cars and even wind turbines. The latter is especially important, as wind turbines are one of the most important forms of clean energy in the world. Maintaining them is crucial; when they are working at their best they are able to produce as much electricity as possible. There’s one problem with this, however — how can we see something and make sure that it is well maintained if it’s over 300 hundred feet in the air?

The answer is drones, and they have been used for years to capture pictures of turbines that can then be inspected. While very useful, the process still has some room for automation. A human is necessary to identify the difference between damaged and not damaged, and this can make it very time-consuming to check every turbine. Using image classification and AI, it can be made significantly faster and more efficient.

What is Image Classification?

Image classification is the use of computer vision, a subset of AI that replicates human vision, to categorize and classify images. In training, it is given pictures that it breaks down into pixels. These images have labels attached so the model can learn what different patterns between pixels mean, and eventually categorize new images that are fed through.

My Image Classification Model

I built an image classifier to identify the difference between damaged and non-damaged wind turbines from an image. With this, damage can be identified faster, leading to faster repairs and more overall energy being produced.

I worked in PyCharm and used scikit-learn, a popular Python library for machine learning, to construct the body. I was able to achieve an accuracy rate of almost 70%, which is relatively high for a machine-learning model.

While building this, one of the many challenges I faced was trying to find the right workspace to write my code in. I have only ever coded artificial intelligence in Google Colab, so I had to pivot when I realized I could not load my file of data onto the platform. I ultimately worked in PyCharm, where I was able to upload the images.

I had never built an image classifier before, so it was a learning experience modifying code to adapt to images instead of written data. After reading lots of articles and watching a lot of tutorials (This one in particular was super helpful), I was able to build a working model and gain skills along the way.

The Data

I trained my classifier using images similar to the ones above, which I sourced from Kaggle. For the purpose of creating the dataset, I used pictures of broken parts and rust as “damaged” turbines, and ones without as “not damaged”. While not perfect, I chose them because I think they give a good representation of what surface damage looks like on a wind turbine.

Why Should We Care About Image Classification in Wind Turbine Maintenance?

When wind turbines are damaged, the time until they are repaired is an amount of time when they are not producing as much electricity as possible. This is not good for increasing the usage of clean energy, as we can only use what is produced, and have to rely on fossil fuels for the rest.

By using AI and image classification to identify damage, repairs can be made at a much quicker rate. More electricity can be produced because of this, and as a society, we can rely on wind energy more to replace fossil fuels. Additionally, an increase in wind energy can lower the price for consumers, making clean energy more accessible and affordable.

Future Projects

For my next project, I want to use generative AI to display what different aspects of our climate will look like in the upcoming years. I have never built with generative AI before, so it may be a bit difficult to figure out what code is necessary for it to function. I am up for the challenge, however, and am very excited about what I will create next.

Wind Turbine Maintenance Can Become A Lot Easier With The Use of Image Classification was originally published in Insights of Nature on Medium, where people are continuing the conversation by highlighting and responding to this story.

The sun is one of the most accessible resources available. Across the world, 430 quintillion Joules of energy hits the Earth every hour. For comparison, all humans combined use that amount of energy yearly. That is such an impressive number, and as a society, we are underutilizing its amazing capabilities.

For obvious reasons, we cannot put solar farms everywhere and anywhere. We cannot put one in the middle of LA, for example, because while it is sunny there, there is not enough land available. We also cannot put one in the middle of Alaska, even though there is plenty of space due to the lack of light during parts of the year.

Less obvious factors also contribute to how much power is generated, however. High amounts of wind can cool down panels and help them to absorb more energy. On the other hand, water droplets formed by high humidity can reflect light off of panels, decreasing the amount of electricity produced.

Solar farm developers need to take into account all of this information to choose the best location for their panels. With the factors mentioned above and more, however, it can be hard to get a rough estimate of how much electricity each solar cell will produce. Enter AI, and this problem can become more manageable.

How AI Can Help

One of the biggest features of AI is its ability to recognize patterns and use them to make predictions. Data is inputted, analyzed, and used as a source to inference off of when exposed to new information. By doing this with data on climate conditions in a particular area, developers can get a rough estimate of how much energy will be produced from a solar cell.

My Software

I used an idea developed by Neural Designer and data from Kaggle to replicate the AI model mentioned above. It takes in the following parameters:

• Distance to solar noon, in radians
• Temperature, in Celsius
• Wind direction, in degrees
• Wind speed, in meters per second
• Sky cover, on a five-step scale from 0 to 4
• Humidity, in percentage
• Average wind speed, in meters per second
• Average pressure, in mercury inches

And ultimately gives an estimate of how many Joules of electricity each solar cell will produce within a three-hour time frame. For developers this is huge — even if it’s just an estimate, this technology can not only make their solar panels more efficient, but it can also save a significant amount of time.

The above picture shows the code I wrote to create this model. I worked in Google Collab and used TensorFlow, a popular software for AI, to create the body for my model. The accuracy rate is only forty-six percent for new data, but that is relatively high for an estimate.

While building this project, one of the major challenges I faced was figuring out the combination of dense amounts and activation shapes that led to the highest accuracy rate. This was my first time creating an AI model, so I spent a lot of time learning how to write the needed code and make it as accurate as possible.

If you are interested in checking out the code itself, here is the link :)

Potential Problems

While something like this can make a big impact, it can also lead to some issues if relied on too heavily. If data is collected on a day with unusual weather conditions for the area, the estimate may not reflect the normal amount of energy that will be collected. This could lead to financial loss and less overall electricity generated, which is negative for both the developer and the electricity consumers.

The Future

There is so much room for AI in the clean energy space, and solar power is just the start of its potential. In the future, I want to look into the impact it could have on other forms of clean energy in addition to solar.

Imagine a world where clean energy is the globe’s main source of electricity. The risks of fossil fuels, severe global warming, and environmental disasters would be minimized, allowing us to shift our focus toward other important issues. While we still have a long way to go, technology is helping society inch closer and closer to that goal.

Solar Farm Placement and How AI Can Help was originally published in Insights of Nature on Medium, where people are continuing the conversation by highlighting and responding to this story.