How AI and Machine Learning Could Impact the Future of Renewable Energy

So, we all know that renewable energies like solar power are the need of the time. But, how does Artificial Intelligence (AI) help in improving renewable energy supply? Let’s see.

Global energy demands are growing every year. And, fossil fuels won’t be able to fulfill our energy needs in the future. Carbon emissions from fossil fuels have already hit an all-time high in 2018 due to increased energy consumption. On the other hand, renewable energy is emerging as a reliable alternative to fossil fuels. It is much safer and cleaner than conventional sources. With the advancements in technology, the renewable energy sector has made significant progress in the last decade.

However, there are still a few challenges in this sector that can be addressed with the help of emerging technologies. Technologies like AI and Machine Learning can analyze the past, optimize the present, and predict the future. And, AI in the renewable energy sector can resolve most of the challenges.

How AI Technology Can Improve the Renewable Energy Sector

“AI is taking on many new roles in society — becoming our coworkers, serving as a virtual assistant in our homes, operating our cars and more”, says Rana el Kaliouby, a pioneer in AI.

The electric grid is one of the most complex machines on Earth. However, it is evolving rapidly with the addition of variable renewable energy sources. Due to the inherent variability of wind and solar, the current grid faces many challenges in accommodating the diversity of renewable energy.

The utility industry needs smart systems that can help improve the integration of renewables into the existing grid and make renewable energy an equal player in the energy supply.

Here’s how AI technology can improve the reliability of renewable energy and modernize the overall grid.

1. Smart, Centralized Control Centers

The energy grid can be interconnected with devices and sensors to collect a large amount of data. When coupled with AI, this data can give new insights to the grid operators for better control operations. It offers flexibility to the energy suppliers to cleverly adjust the supply with demand.

The advanced load control systems can be installed with the equipment, such as industrial furnaces or large AC units, which can automatically switch off when the power supply is low. Intelligent storage units can also be adjusted based on the flow of supply.

Additionally, smart machines and advanced sensors can make weather and load predictions that can overall improve the integration and efficiency of renewable energy.

2. Improved Integration of Microgrids

AI can help with the integration of microgrids and managing distributed energy. When the community-level renewable energy generation units are added to the primary grid, it becomes hard to balance the energy flow within the grid. The AI-powered control system can play a vital role in solving the quality and congestion issues.

3. Improved Safety and Reliability

While the biggest goal of AI in renewable energy is to manage the intermittency, it can also offer improved safety, efficiency, and reliability. It can help you understand the energy consumption patterns, identify the energy leakage and health of the devices.

For example, AI-powered predictive analysis can collect data from wind turbine sensors to monitor wear and tear. The system will monitor the overall health of the equipment and alert the operator when the maintenance is needed.

4. Expand the Market

The integration of AI can help renewable energy suppliers expand the marketplace by introducing new service models and encouraging higher participation. The AI-powered systems will be able to analyze the data related to energy collection and provide insights on energy consumption.

This data would help suppliers optimize the existing services and launch new service models. It can also help retail suppliers to target new consumer markets.

5. Smart Grid with Intelligent Storage

The integration of artificial intelligence with Intelligent Energy Storage (IES) can provide a sustainable and reliable solution to the renewable energy industry. This smart grid will be able to analyze a vast amount of data collected from several sensors and make timely decisions on energy allocation. This will also help microgrids to efficiently manage the local energy needs while continuing the power exchange with the main grid.

Machine Learning and the Future of Renewables

Machine learning technology — computer programs that use data sets to “learn” how to see patterns in information like wind speed and energy output — may be the answer to wind farms’ prediction problem.

By using large amounts of data about variables like wind speeds, it’s possible to build a model that more accurately predicts how much energy a wind turbine will produce. These improved models may encourage officials to become more enthusiastic about constructing wind farms.

The same technology is also being used to reduce how much energy a building or industrial complex uses. In the future, it’s likely that machine learning will be integrated into many different aspects of energy management and renewable energy production.

How AI and Machine Learning Could Impact the Future of Renewable Energy

Wind Farm Forecasting and Green Planning

One downside of renewables is how hard it can be to predict the energy they produce. Wind speeds can vary widely from hour to hour and from day to day. You can average out how much wind a certain place gets over the course of a long period of time. And you can also use that information to figure out how much energy a wind farm may produce per year. But it’s much harder to accurately predict the energy a wind farm will produce on a given day or at a certain time.

Inaccurate predictions mean it’s harder to know if construction costs will be worth it. With renewables, too much and too little are both big problems. Create too little power and you’ll need to have supplemental energy sources at the ready. Generate too much power and you’ll need to either store that energy or waste it. And battery technology is just too expensive right now to store renewable energy at any sort of useful scale. Machine learning technology — computer programs that use data sets to “learn” how to see patterns in information like wind speed and energy output — may be the answer to wind farms’ prediction problem.

The same machine learning tech, experts think, could be used to make green energy more predictable. In February 2019, Google announced that it was using DeepMind, the company’s in-house machine learning technology, to predict the energy output of wind farms.

Machine learning technology has already made wind farm predictions 20 percent more valuable, according to Google. And better value means that wind farms may be seen as a safer investment by municipal officials who control which kinds of energy projects get built. Will machine learning build better wind farms? It’s hard to say. But machine learning has been successful in related fields.

The weather is notoriously difficult to predict, for many of the same reasons that it’s hard to predict wind speeds. A good prediction needs to take into account more variables than a person can keep track of — like changing levels of humidity, pressure and temperature. Predicting the weather is so hard, in fact, that IBM acquired The Weather Company to see if machine learning could make weather predictions better. The results? According to IBM, they achieved a nearly 200 percent increase in the accuracy of forecasts.

Improved Renewable Energy Efficiency

Machine learning tech can be used to find patterns in almost any kind of data — including data about how customers use the energy that renewables produce.

By working with smart technology like thermostats and home assistants, machine learning algorithms can be used to reduce the energy needed by houses and office complexes alike. By building a record of energy needs and keeping track of when people are at home or in the office, the technology could automatically turn off lights and adjust the temperature in a way that reduces the energy used by these buildings.

DeepMind itself has been used in the past to reduce energy costs at Google’s data centers. In the future, it may become common to see the same sorts of machine learning tech — or even the same products — working to solve many different aspects of the same problem.