Sam Daniel Solhpour
8th Grade, Corona Del Mar Middle School
Newport Beach, CA
Forecasting Renewable Energy Production Based on Historical Weather Data in California: Predictive Analysis Using Regression Modeling
Project Background
In August of 2024, California asked residents to save power at night so the electric grid wouldn鈥檛 fail when solar panels stopped producing energy after dark. 鈥淢y tennis practice was canceled, our house went half-dark, and I heard a radio reporter say, 鈥楴o one knows exactly how much renewable power will be available each hour,鈥欌�� Sam says. 鈥淭hat really stuck with me. If we can predict the weather, why can鈥檛 we predict tomorrow鈥檚 solar and wind power?鈥� He decided to see if he could create more accurate weather forecasts to better predict energy availability.
Tactics and Results
Sam gathered solar and wind energy data in California from 2022 and 2023. He used a linear regression model to align the renewable energy data with weather factors from the same days, including temperature, cloud cover, solar radiation, wind speed, and day length. He then looked for correlations between the weather and the output of solar and wind energy and developed a predictive model that could use the weather to predict energy output. His model explained 97 percent of variation in solar energy, with an error of around 10,780 Megawatt hours per day, about 39 percent of daily solar energy production. His model explained 89 percent of variability in wind energy production, with error of around 15,793 Megawatt hours per day, or 42 percent of wind energy production. He suggests that his more precise predictions could help cut 9,100 metric tons of CO鈧� emissions daily.
Beyond the Project
Sam loves both tennis and basketball. He also likes biking, it 鈥渉elps me clear my mind and appreciate nature,鈥� he says. Sam volunteers to help people who are unhoused, doing food drives and community events. He would like to be a computer scientist. 鈥淚 love using data and code to solve real-world problems,鈥� he says. 鈥淚 want to use these skills to build smarter systems that help people, protect the environment, and make the world more efficient and sustainable.鈥�
