Neural network forecasting of solar power for NASA Ames sustainability base

Chaitanya Poolla, Abe Ishihara, Steven Rosenberg, Rodney Martin, Alex Fong, Sreejita Ray, Chandrayee Basu

Abstract: Solar power prediction remains an important challenge for renewable energy integration primarily due to its inherent variability and intermittency. In this work, a neural network based solar power forecasting framework is developed for the NASA Ames Sustainability Base (SB) solar array using the publicly available National Oceanic and Atmospheric Administration (NOAA) weather data forecasts. The prediction inputs include temperature, irradiance and wind speed obtained through the NOAA NOMADS server in real-time. The neural network (ANN) is trained and tested on input-output data from on-site sensors. The NOAA archived forecast data is then input to the trained ANN model to predict power output spanning over nine months (June 2013-March 2014). The efficacy of the model is determined by comparing predicted power output against on-site sensor data.