Forecasting of Solar Irradiance and Ambient Temperature of Site-Specific PV Applications Using Offsite Deep Neural Networks

The growing trend of solar photovoltaic (PV) adoption motivated homeowners and independent solar PV plants to assume the role of electricity generators. Utility companies need to address the changes in supply and demand to formulate effective distribution plans. Monitoring the power output of the photovoltaic array is a key task in ensuring the correct estimation of the electricity production of the system. Nevertheless, the literature recognizes the obstacle that expensive onsite monitoring sensors have on small-scale and medium-scale PV applications. Mathematical models based on the estimation of power output from historical data are the most used techniques to address the need for expensive monitoring systems. Estimating the power output of a PV array depends on the cell temperature, ambient temperature, and solar irradiance. The present thesis proposes a machine learning model to forecast site-specific ambient temperature and solar irradiance. The results contribute to the generation of low-cost data-driven models to save money by replacing the installation of on-site sensors. The methodology employed off-site publicly available weather data from neighboring weather stations as an alternative to on-site measurements. A 5-layer Deep Neural Network was trained using 5 years’ worth of historical data from a remote weather station in Green Bay, WI, to predict on-site parameters for a solar array located about 45 miles away in Keshena, WI. The model was suitable for site-specific ambient temperature prediction. The Ozone Dobson was a key parameter for solar irradiance prediction, but the proposed model was limited by the amount of available data.