Thermal Load Prediction Considering Solar Radiation and Weather

Abstract

Abstract This study is aimed at presenting solar radiation and weather dependent thermal load modeling in microgrids. This article predicts the hourly thermal load for the next 24 hours by using the Elman neural network and neural wavelet neural network (WNN). The principles of the proposed hybrid algorithm are firstly analysed. Then the impact factors of solar radiation and weather, which have important effects on thermal loads, are studied. The performance of the algorithm is analyzed and validated via real thermal load data obtained from Haidian Writers Association, Beijing. Comparisons are undertaken among four kinds of forecasting results to verify the importance of the solar radiation and weather data. The cloud theory is introduced as an effective tool to analyse the precision of the predicted results, and the errors are represented by using three numerical characteristics.