A machine-learning approach for regional photovoltaic power forecasting

Abstract

This paper presents a machine-learning approach for regional photovoltaic (PV) power forecasting of up to 2 days ahead with hourly resolution. Physical PV power model is aggregated by geographical clusters and then summed for an entire ISO load zone. Numerical weather prediction (NWP) forecasts provide parameters, such as irradiance, temperature, barometric pressure, and wind speed, which are used as inputs to calculate plane of array (POA) irradiance and PV output power. A machine-learning approach is then developed. Bias correction for calculated power is conducted using linear regression method. During this procedure, categorization in accordance to critical parameters is employed to obtain a fine approximation. With optimized blending coefficients, adaptive mixture of correction results following different NWP methods is introduced to obtain an intelligent and adaptable output power PV forecast. A case study for the period from June 12, 2014 to January 24, 2015 of Southeastern Massachusetts (SEMA) load zone is carried out. Normalized Root Mean Square Error (NRMSE) is 5.28% for day-ahead forecast horizon, which is reduced by 30.6% compared to the baseline that the best individual model is used.