How Artificial Intelligence will Incredibly Lower Your Energy Bill

Yassine Landa
Apr 29 · 4 min read

The world’s transition to clean renewable energy, such as solar, wind, and biofuels, relies on our actions. We must engage in this change by optimizing the power consumed by our homes, buildings, and machines.

Indeed, studies have shown that continuously adjusting the latter’s operations, and implementing energy-efficiency strategies, could be very beneficial. Energy use may be reduced by 30%. But how can it be achieved? Leveraging artificial intelligence is the answer.

When people hear Artificial Intelligence (AI), they generally think of Hollywood movies (Skynet, Matrix, Robocop, etc.). However, in real life, it only refers to computational systems that behave intelligently, applying advanced algorithms on large sets of data.

These smart devices embody AI’s predictive power and flexibility to changing environments and changing goals. Indeed, AI can help reduce energy consumption in several ways:

  • Catch operational strays:

Catching operational strays or energy leaks quickly is the aim of efficient real-time energy management. AI systems can predict when these leaks happen, especially for large office buildings where equipment might deviate from optimum settings. This would hence reduce energy-waste, save money for owners and tenants, protect equipment from wear and tear, and maintain better buildings.

  • Breakdown the energy consumption:

Machine Learning techniques can detect consumption behaviors and energy use patterns. Such meaningful insights can, therefore, help utility companies build better client segments, and personalize customer offers with relevant information. Additionally, households can directly benefit from these insights -if well presented to them, by regaining control after understanding their own consumption (and electricity bill!).

  • Find the best place for solar energy placement:

The crucial part about the deployment of solar energy solutions is the cost related to their installation. However, these costs will be drastically reduced if businesses target the most interesting prospects and areas. Using machine learning along with data relevant to tree shading, income, home size, energy costs, rebates, incentives, and roof-orientation is key to cost and risk control. Indeed, leveraging this information helps locate the best area in terms of energy performance.

AI and machine learning enable various business opportunities. They empowered the first waves of startups to develop new products meant to save more energy. Nevertheless, there are some standing issues to be tackled:

  • Data acquisition:

Governments and utilities are deploying smart meters all over the world as these lines are written*. This will permit access to more interesting data in the future. However, considerable efforts are required to cover wider areas and develop specific machine-learning algorithms. An example of the latter would be non-supervised & semi-supervised learning, which can predict energy-consumption levels even for the homes lacking smart meters.

  • The reluctance of the industry:

The energy industry is very conservative. Its business model did not change since Thomas Edison –as Alex Laskey, co-founder of Opower, asserted in a Ted Talk! “Utilities are still rewarded when their customers waste energy”, he said. To use AI applications and aim for energy efficiency, utilities must become more than energy providers. They must be convinced that being user-centric and “smart” is where profitability lies in the “smart grid” era.

  • The arrival of electric vehicles:

As more electric vehicles enter the market (their number is doubling each year!), questions about managing their battery charging cycles arise. Considered as the largest home appliance ever, their identification while plugged in the grid is crucial to optimally manage energy demand and supply.


Governments showed their commitment to reducing carbon emissions during the COP21 in Paris last year. This cannot be done by producing green energy only. We will need to optimally manage the energy distribution, and drive real behavior change in the consumers’ mind. Both goals could be achieved if AI systems were used to regain control over energy leaks in buildings, show households where they can do better, and encourage green energy generation. Nevertheless, the issues related to data acquisition, the reluctance of the industry, and the arriving of hybrid electric vehicles might challenge the adoption of these new technologies.

*: Utilities deployed 50 million meters at homes across the U.S., reaching 43 percent of homes overall, according to the Edison Foundation’s Institute for Electric Innovation. Europe is committed to installing by 2020 close to 200 million smart electricity meters for an investment of around €45 billion, according to a 2014 report by the European Commission.

Data Driven Investor

from confusion to clarity, not insanity

Yassine Landa

Written by

Data Scientist with an entrepreneurial mindset, passionate about AI and building Machine Learning products.

Data Driven Investor

from confusion to clarity, not insanity

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