Intraday forecast of global horizontal irradiance using optical flow method and long short-term memory model

Abstract

Short-term global horizontal irradiance (GHI) forecast methodologies are routinely employed to mitigate the instability of photovoltaic power and, more importantly, to secure early participation in the energy auction market. The intra-day forecast model substantially consists of the combination of a cloud motion vector (CMV) derivation and a GHI extraction model. This study utilized optical flow (OF) method to calculate CMV from two subsequent satellite images and predict cloud displacement up to 180 min ahead. Meanwhile, the conventional GHI extraction models (Hammer and the fast all-sky radiation model for solar applications (FARMS) models) were employed to extract future GHI. Moreover, to the best of our knowledge, only a few studies have been dedicated to combining OF method with deep learning model. Hence, this study proposed a novel combination based on OF method and long short-term memory model (LSTM) to enhance temporal horizon and accuracy. After all combinations of OF method and GHI models were simulated and assessed under all-sky conditions, the OF method that combined with LSTM outperformed the OF method combined with conventional models across all time horizons. For root mean square error (RMSE) and forecast skill (FS), the accuracy of OF and LSTM models for all time horizon ranges from 59.64 W/m2 to 120.63 W/m2 and 16.40 % to 54.42 %, respectively. The study findings are expected to encourage the inclusion of satellite data in GHI forecast as well as the optimization of energy management systems that integrate with a photovoltaic system.