Renewable energy is the need of the hour, but harnessing it quite challenging. With big data and machine learning, it is possible to reap the benefits of renewable energy.
FREMONT, CA: As the climate has changed drastically over the years, the awareness to use renewable sources of energy has increased as well. Energy sources such as wind and solar are increasingly valuable in this effort; however, they can be unpredictable and intermittent. It is incredibly tricky to answer how much energy a single panel or solar plant can contribute to the grid on any particular day as sometimes it is cloudy, and sometimes the wind does not blow. To make solar and wind energy an essential part of the grid, the companies must understand the amount of power available, its demand, and then using the energy to meet consumer needs as efficiently as possible.
Big data can play a significant role in understanding these aspects. By combining weather data, predictive analytics, satellite feeds, and machine learning, renewable energy can reach the grid consistently and efficiently. For instance, IBMs Hybrid Renewable Energy Forecasting solutions or HyREF can predict the weather up to a month in advance by using cloud-imaging technology and sky-facing cameras. Using such advanced technologies can lead to an increase in the generation of renewable power by 10 percent, which is stored or delivered to the grid—enough to power 14,000 homes.
Big Data and forecasting technology can help in monitoring solar farms with hundreds or even thousands of panels spread across large regions. Companies use big data analytics to detect underperformance and identify inefficiencies. They use Virtual Irradiance (VI) that collects ground level sunlight-intensity data to determine which panels are underperforming and alerts the staff for required maintenance.
The analysis of the data collected can help communities suggest where solar panels will be the most efficient. For instance, South Australia collaborated with Tesla has built the world’s largest virtual power plant by connecting 50,000 Tesla batteries with panels reducing the cost of stabilizing the grid by $28.9 million. Better evaluation of energy supply-and-demand models can lead to a better understanding of a community's energy needs. Consistent fulfillment of energy demands by renewable sources can reduce a company’s safety margins translating into a cheaper, more efficient power for both consumers and the company.
Big data and machine learning have already revolutionized many industries, and now the energy industry is next in line. By harnessing the technological advances, the companies and can become a prominent part of the solution.