Ultra-short-term exogenous forecasting of photovoltaic power production using genetically optimized non-linear auto-regressive recurrent neural networks

Abstract Accurate and credible ultra-short-term photovoltaic (PV) power production prediction is very important in short-term resource planning, electric power dispatching, and operational security for the solar power system. This study proposes a novel approach of using genetically optimized non-linear auto-regressive recurrent neural networks (NARX) for ultra-short-term forecasting of PV power output. Hence, the high prediction accuracy of static multi-layered perceptron neural networks can be extended to dynamic (time-series) models with a more stable learning process. Exogenous models with different commonly available meteorological input parameters are developed and tested at five different locations in Algeria and Australia, as case studies of the arid desert climate. The prediction capabilities of the models are quantified as functions of the forecasting horizon (5, 15, 30, and 60 minutes) and the number of meteorological inputs using various statistical measures. It was found that the proposed models offer very good estimates of output power, with relative root mean square errors ranging between ∼10 and ∼20% and coefficients of determination higher than 91%, while improving the accuracy of corresponding endogenous models by up to 22.3% by only considering the day number and local time as external variables. Unlike the persistent model, the proposed NARX-GA models perform better as the forecasting horizon narrows down, with improvements of up to 58.4%.