Along with the rocket development of industry and technology, we must fully acknowledge the severe impacts of climate change. In the long run, protecting the environment and finding more sustainable energy sources is what guarantees our existence on Earth. Therefore, scientists have come back to utilize natural and renewable energy such as wind power, solar power, or tidal power. Yet, there is still a long road to fully adopt these alternatives, and machine learning can be the perfect ally to shorten the process.

Why machine learning is the perfect match for renewable energy

At the first hearing, no one could find the connection between machine learning and renewable energy. Yet, the unique characteristics of this industry are the perfect fit for the technology to showcase its full potential. 

Renewable energy comes from renewable sources, which are usually what mother nature has already offered us. Some of the most popular sources are sunlight, wind, rain, tides, waves, and geothermal heat. Although holding great possibilities, these recyclable sources are hard for humans to control due to the unpredictability of nature. Our ancestors have come up with general indicators and annual forecasts for common weathers like when the rainy season is likely to start. However, they are showing little help over time due to the negative impacts of climate change. 

In 2017, nearly 80% of global energy sources came from fossil fuels. To protect the environment as well as avoid overexploiting, governments are focusing on the long-term development of renewable energy. The first quarter of 2020 witnessed a 1.5% increase in global use of renewable energy in all sectors compared to 2019. Furthermore, countries must at least triple the production of global recyclable power, such as wind and solar, to achieve climate neutrality goals by 2050. Following this increasing adoption of renewable energy, scientists are under huge pressure to provide more accurate weather forecasts. 

Machine Learning Applications in Renewable Energy Sector

Since the beginning of time, meteorologists have collected weather data; the first meteorology document dated back to 350 BC. This data has been building up every day along with the development of mankind. To process all the information and transform them into useful insights for renewable energy implementation, machine learning is the only plausible choice with smart pattern-based prediction and huge data analysis.

More than an efficient roadmap to change the environment, machine learning is the key to open new possibilities in the power sector. Some of the most noteworthy benefits that machine learning offers to the future of the renewable energy industry include:

  • Substantial and precise inspections: Governments and organizations can now get access to flawless analysis based on a rich history source.
  • A cost-effective and resilient energy grid: With accurate forecasts, meteorologists can suggest a suitable energy plan to the authority to support particular demands. The new recyclable energy will soon be considered “dispatchable,” which means it can provide a specific amount of power when needed. 

How machine learning optimizes renewable energy production and consumption  

Weather and risks prediction

The most important factor in ensuring the success of a renewable energy plan is weather and risk prediction. Usually, scientists research the physics of the weather itself and compare them with previous phenomena. However, with machine learning, this process has been greatly simplified. 

Weather and risks prediction
Source: AccuWeather

Daily weather data is saved to a database, and then computers will run the system to create predefined models. If the same phenomenon happens in the next period, the application can automatically sort them into the correct weather model. Furthermore, each model is programmed to predict how the weather will affect the result of the renewable energy plans. The forecast will help organizations to further understand how the environment can facilitate the energy grid and focus the resource on potential time frames. Comprehensive machine learning can forecast up to 30 days prior, which can be seen in IBM’s system. 

The US Energy Department has witnessed a 30% increase in solar power predicting accuracy thanks to machine learning. This machine learning application in renewable energy has also been implemented successfully by the UK’s National Grid. Combining satellite imagery, observation, and deep learning, they have summarised the data of hundreds of power hubs throughout the country to forecast the weather accurately. “If you’ve got a cold front moving in and [a model] has seen something similar before, we can predict the likely impact of that front on solar generation.”, said James Kelloway, energy intelligence manager at the National Grid’s Electricity System Operator division.

Energy demand forecast

Not limited to only following climate changes, machine learning takes a further step into forecasting power demand. For example, summer is when the need for power climbs up rapidly, yet this is still only a general statement. To better analyze the fluctuation and prepare practical strategies, scientists rely on the power of machine learning. 

Energy demand forecast

Sharing the same methods as weather forecasting, this machine learning application in renewable energy tracks changes and behaviors of users when consuming energy on different periods then builds up a model for each phenomenon. After that, it suggests a number based on historical energy usage data. The information can tell organizations to generate more energy or cut down on unnecessary sources. 

For example, Verdigris Technologies is a technology company that provides a software platform to optimize power consumption. With an IoT hardware installation and smart sensors attached to the energy grid of the clients, Verdigris Technologies can smartly identify insufficient energy sources. One of their customers, W Hotel San Francisco, has recognized $13.000 preventable energy annual losses in their kitchen just after 3 months of using the system. 

Intelligent maintenance and infrastructure design

One factor that determines the win or loss of a renewable energy implementation is the maintenance post-installation. Unlike other sources, natural powers don’t get generated from a self-contained factory. For example, solar plants or wind turbines cost thousands of dollars to build, yet they are usually in the middle of nowhere, with little intervention from people. It will be a smart move to proactively take a closer look at the performance of these installations. When you successfully maintain the health of the input, your energy grid could easily and seamlessly cover the output demand.

Machine learning takes advantage of both past and present data to develop the future performance of the energy system. Some of the most common intelligent maintenance are anomaly detection, failure prediction, and automated monitoring. Researchers have confirmed that with AI applications, predictive maintenance is up to 25.3% more effective and 24.6% more accurate

Applications of machine learning in renewable energy also opens a new door for smart infrastructure design or smart buildings. According to GlobeNewswire’s report, the global smart building market is predicted to reach $127.09 billion by 2027. More and more architects are building houses with an intelligent energy management system as the core of the design. Appliances are powered by solar power and controlled by AI or voice command, which allows us to command them to auto-perform simple tasks remotely.

On a smaller scale, you can take a look at how Verv – an AI-based tool, supports energy management. This application records how household appliances consume energy and alerts users when a device is left unconsumed for a long time. This helps saving energy expenses as well as protecting devices not to be overused. 

Smart energy grid

Following proactive maintenance, an intelligent energy grid is what companies are looking for when applying machine learning in renewable power. One of the most challenging tasks for any energy plan is how to distribute energy to multiple locations effectively. The unpredictable traits of natural resources such as solar or wind power make this issue much more complicated. However, machine learning has the perfect answer to this riddle. 

By implementing machine-based applications, companies can avoid fraudulent actions such as electric theft and provide scalable power strategies for long-term development. A balancing and smart energy grid can help organizations to answer tricky questions, such as:

  • The most and least power-needed locations
  • The highest and lowest power supplying locations
  • The frequency of blackouts
  • The locations require additional energy-generating infrastructure
  • The abnormal power-consuming locations

Machine learning is taking step by step closer to managing the energy grid to fit human needs. Jack Kelly, co-founder of non-profit climate change research and development lab Open Climate Fix, proposed: “It is still quite manual, so it’s humans looking at what’s happening on the grid now and humans instructing generators to change their behavior to try and better match demands, over time horizons of an hour or less typically.”

A great example of how machine learning can support energy control comes from the DeepMind AI of Google. Its purpose is to cut down on energy consumption and emission from energy usage. According to Google, the application has managed to cool off Google’s data servers by 40%.

Learn more about Machine Learning at: The Comprehensive Guide to Understand Machine Learning and Deep Learning

In summary, 

Machine learning is the answer to the seemingly impossible challenge of implementing recyclable power. Thanks to technology, the distance between planning and practical usage is shortened tremendously. Meteorologists, companies, and governments now have credible evidence and analysis to create a concrete long-term plan for sustainable development. All can be achieved with machine learning applications in renewable energy.

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