The Impact of Machine Learning on Renewable Energy

Machine learning, as well as its endgame, artificial intelligence, is proving its value in a wide variety of industries. Renewable energy is yet another sector that can benefit from machine learning’s smart data analysis, pattern recognition and other abilities. Here’s a look at why the two are a perfect match.

Predicting and Fine-Tuning Energy Production

One of the biggest misconceptions about solar power is that it’s only realistic in parts of the world known for year-round heat and intense sunshine. According to Google, around 80% of rooftops they’ve analyzed through their Sunroof mapping system “are technically viable for solar.” They define “viable” as having “enough unshaded area for solar panels.”

Even with this widespread viability, it’s useful to be able to predict and model the energy yield of a renewable energy project before work begins. This is where machine learning enters the equation.

Based on the season and time of day, machine learning can produce realistic and useful predictions for when a residence or building will be able to generate power and when it will have to draw power from the grid. This may prove even more useful over time as a budgeting tool as accuracy improves further. IBM says their forecasting system, powered by deep learning, can predict solar and wind yield up to 30 days in advance.

Machine learning also helps in the creation of solar installations with physical tracking systems, which intelligently follow the sun and angle the solar panels in order to maximize the amount of power they generate throughout the day.

Balancing the Smart Energy Grid

Predicting production is the first step in realizing other advantages of machine learning in clean energy. Next comes the construction of smart grids. A smart grid is a power delivery network that:

  • Is fully automated and requires little human intervention over time
  • Monitors the energy generation of every node and the flow of power to each client
  • Provides two-way energy and data mobility between energy producers and clients

A smart grid isn’t a “nice to have” — it’s necessary. The “traditional” approach to building energy grids doesn’t take into account the diversification of modern energy generation sources, including geothermal, wind, solar and hydroelectric. Tomorrow’s electric grid will feature thousands and millions of individual energy-generating nodes like solar-equipped homes and buildings. It will also, at least for a while, contain coal and natural gas power plants and homes powered by heating oil.

Machine learning provides an “intelligence” to sit at the heart of this diversified energy grid to balance supply and demand. In a smart grid, each energy producer and client is a node in the network, and each one produces a wealth of data that can help the entire system work together more harmoniously.

Together with energy yield predictions, machine learning can determine:

  • Where energy is needed most and where it is not
  • Where supply is booming and where it’s likely to fall short
  • Where blackouts are happening and where they are likely
  • When to supplement supplies by activating additional energy-generating infrastructure

Putting machine learning in the mix can also yield insights and actionable takeaways based on a client’s energy usage. Advanced metering tools help pinpoint which processes or appliances are drawing more power than they should. This helps energy clients make equipment upgrades and other changes to improve their own energy efficiency and further balance demand across the grid.

Automating Commercial and Residential Systems

The ability to re-balance the energy grid and respond more quickly to blackouts cannot be undersold. But machine learning is an ideal companion to renewable energy on the individual level as well. Machine learning is the underlying technology behind smart thermostats and automated climate control and lighting systems.

Achieving a sustainable future means we have to electrify everything and cut the fossil fuels cord once and for all. Electrifying everything means we need to make renewable energy products more accessible. More accessible renewable energy products means we need to make commercial and residential locations more energy-efficient than ever.

Machine learning gives us thermostats, lighting, and other products that learn from user preferences and patterns and fine-tune their own operation automatically. Smart home and automation products like these might seem like gimmicks at first, but they’re actually an incredibly important part of our renewable future. They help ensure we’re not burning through our generated power, renewable or otherwise, when we don’t need to be.

Bottom Line

To summarize all this, machine learning offers a way to analyze and draw actionable conclusions from energy sector data. It brings other gifts, too. Inspections powered by machine learning are substantially more accurate than inspections performed by hand, which is critical for timely maintenance and avoiding downtime at power-generating facilities.

Machine learning also helps us predict and identify factors that could result in blackouts and respond more quickly (and with pinpoint accuracy) to storm damage.

Given that the demand for energy is only expected to rise across the globe in the coming years, now is an ideal time to use every tool at our disposal to make our energy grids more resilient, productive and cost-effective. Machine learning provides the means to do it.

About Emily Folk

Emily Folk is freelance writer and blogger on topics of renewable energy, environment and conservation. You may read more of her work on http://www.conservationfolks.com. Follow her on Twitter @EmilySFolk
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