Multi-Target Learning Algorithm for Solar Radiation Components Forecasting Based on the Desired Tilt Angle of a Solar Energy System

Abstract

Solar radiation components (SRC) forecasting with different tilt angles plays a key role for planning, managing, and controlling the solar energy system production. To overcome the gaps related to the intermittence and to the absence of SRC data, an accurate predictive model needs to be established. The main goal of the present work is to develop for solar system engineers and grid operators a precise predictive approach based on multi-target learning algorithm to forecast the hourly SRC measurements that is related to the city of Marrakesh (latitude 31°37′N, longitude 08°01′W, elevation 466m), Morocco, received by different inclined solar panels’ surfaces. For this purpose, eight training algorithms (Resilient back Propagation (Rp), One step secant (OSS), Levenberg-Marquardt (LM) Algorithm, Fletcher-Reeves algorithm (Cgf), Polak-Ribiere algorithm (Cgp), Powell-Beale algorithm (Cgb), gradient descent (Gd) algorithm and scaled conjugate gradient algorithm (Scg)) are tested to optimize the developed approach’s parameters. The forecasting results were performed based on the angle of inclination desired by the operator and some accessible meteorological measurements that are recorded at each hour, comprising time variables. The achieved performance demonstrates the stability and the accuracy of the established approach to estimate the hourly SRC time series compared to several recent literature studies.