In early 2019, we built a deep learning model that predicted electric consumption on an hour-by-hour basis. Because the smallest error can cost an electric utility tens of thousands of dollars, we explored a number of more complex forecasters. In the end we discovered that a simple day-long approach is the most effective, often cutting error in half.

THE STRUCTURE

The Weakest Link has a group of people answer questions in a circle. For each correct answer, the team goes up the totem pole of values they can “bank.” After one correct answer they can bank £250, after two correct answers they can bank £500, and so on. Once a player gets a question wrong, however, the amount returns to £0. What is the optimal stopping strategy? Should players hedge their bets after reaching £500? £4000? What if they have low accuracy?

A Monte Carlo simulation allowed me to calculate average earnings when comparing stopping strategy to percent accuracy. Here is the link to the Jupyter notebook that runs these simulations. I estimated 100 questions per game. I averaged over 500 simulated games. …

Almost every Princeton graduate — from Senator Ted Cruz to Supreme Court Justice Elena Kagan, actress Brooke Shields to Chair of the Federal Reserve Jerome Powell — has written a senior thesis. All these graduates have also used a titular idiom that plagues nearly half of Princeton theses: the colon.

• Cruz ’92 — “Clipping the Wings of Angels: The History and Theory behind the Ninth and Tenth Amendments of the United States Constitution”
• Kagan ’81 — “To the Final Conflict: Socialism in New York City, 1900–1933”
• Shields ’87 — “The Initiation: From Innocence to Experience: The Pre-Adolescent/Adolescent Journey in the Films of Louis Malle, ‘Pretty Baby’ and ‘Lacombe…

A 99-point error spread

Fake-follower calculators, especially a platform called Twitter Audit, have been cited by a number of news outlets, including The Telegraph, Vanity Fair, and the Columbia Journalism Review. But these platforms’ statistical techniques are far from rigorous for large accounts.

One fake-follower calculator created by SparkToro, a startup claiming approximately $1.8 million in funding, even uses machine learning algorithms to separate real accounts from fake. But a bottleneck in how much data Twitter allows third-party developers to access in a given time has forced these web apps to use a tenuous definition of “random sample” for accounts with millions of followers.

The math is wrong.

When a programmer requests Twitter for data, Twitter returns a maximum of 75k follower IDs in a 15-minute period. If the IDs that Twitter returned were random, there wouldn’t be a problem. But these IDs are in order of who followed a user most recently. …

Research in coordination with the Open Modeling Framework.

Forecasting technology has given utilities an opportunity to flatten their load curves, raising a whole new family of questions. Below are solutions to important questions that can save utilities a good deal of money by reducing capital and operating expenses from peaking power plants. All testing can be found here.

This research can also be viewed on my website:

Part I: What’s tomorrow’s load?

Main takeaways:

• To get any kind of useful energy consumption forecast, simple machine learning isn’t appropriate. Deep learning, however, can get us the accuracy we need.
• Given historical load and temperature data, a straightforward neural network can give a 24-hour forecast with about 97 percent accuracy. …

Peak Shaving with Neural Networks: Part III

Research in partnership with the Open Modeling Framework.

This is the third in a three-part series about peak shaving with neural networks. Consider checking out the other two:

Even the best models for predicting energy consumption aren’t good enough to capture a majority of the possible value of peak shaving. When a forecast has just 3% error, it’s not unusual to lose half of possible savings as a consequence. Consider how the smallest inaccuracies dramatically affect these utilities’ expected savings from peak shaving (testing here):

Peak Shaving with Neural Networks: Part II

Research in partnership with the Open Modeling Framework.

This is the second in a three-part series about peak shaving with neural networks. Consider checking out the other two:

For electric utilities, reducing monthly demand charge can be hugely profitable. Implementing a peak shaving strategy every day, however, could be costly. If a utility is using direct load control (paying customers to turn off air conditioners, water heaters, etc.), they may frustrate customers if they do so too frequently. If a utility uses storage, overuse can force them to replace expensive batteries more often than necessary. Therefore, it’s not only important to predict the load shape for the next day, but also to predict the month’s peak. …

The “aww” subreddit is arguably the cuteness singularity of the internet. With more than 20 million members, usually tens of thousands of users judge the cuteness of every post. We can leverage Twitter and Reddit’s developer APIs to share the best content of r/aww to Twitter.

How it works.

Here’s the Twitter account @reddit_says_aww. And here is a link to the code. Naturally, I’ve removed the public and private keys for my Reddit and Twitter accounts, but if you apply for a developer account at both companies, they can generate authorization tokens for you. …