Multi Time Horizon Ahead Solar Irradiation Prediction Using GRU, PCA, and GRID SEARCH Based on Multivariate Datasets

Abstract

Precise forecasting of solar irradiation helps to minimize photovoltaic power plant energy wastage, avoid system damage due to the high variability of solar irradiation, and focus the integration of power output among the various power grids. To forecast solar irradiation, it is crucial to consider the multiple dimensions of historical weather data as temperature, wind speed, and different type of irradiation, in addition to the categorical time data. The high dimensionality of these data can perturb the performance and can introduce very low calculation in a forecasting model. Hybrid combination between PCA and GRU with Grid Search hyperparameters optimization, proposed in this work to predict solar irradiation in different time horizons, using multiple variable data. Firstly, PCA changes the multiple variables to a few variables named components. Secondly, the prediction model GRU trained in optimized by using Grid search method. Finally, the optimized model predicts the solar irradiation. The proposed model compared in this study with simple GRU, LSTM, MLP and RNN models. The result of experience indicates that the PCA-GRU have a good forecasting accuracy in different time horizon, and has the better performance, and faster training compared with other models.