Short-Term Forecasting of Load and Renewable Energy Using Artifical Neural Network

Abstract

Load forecasting is a technique used for the prediction of electrical load demands in battery management. In general, the aggregated level used for Short-Term Electrical Load Forecasting (STLF) consists of either numerical or non-numerical information collected from multiple sources, which helps in obtaining accurate data and efficient forecasting. However, the aggregated level cannot precisely forecast the validation and testing phases of numerical data, including the real-time measurements of irradiance level (W/m 2 ) and photovoltaic output power (W). Forecasting is also a challenge due to the fluctuations caused by the random usage of appliances in the existing weekly, diurnal, and annual cycle load data. In this study, we have overcome this challenge by using Artificial Neural Network (ANN) methods such as Bayesian Regularization (BR) and Levenberg-Marquardt (LM) algorithms. The STLF achieved by ANN-based methods can improve the forecast accuracy. The overall performance of the BR and LM algorithms were analyzed during the development phases of the ANN. The input layer, hidden layer and output layer used to train and test the ANN together predict the 24-hour electricity demand. The results show that utilizing the LM and BR algorithms delivers a highly efficient architecture for renewable power estimation demand.

Optimal scheduling strategy of electricity‐heat‐hydrogen integrated energy system under different operating modes

“…At present, the short-term prediction methods of renewable energy power generation include time series forecasting method, neural network method and support vector machine (SVM) method. 29,30 Support vector machine, which based on statistical learning theory and structural risk minimization principle, is of the good extension ability and the better accuracy, even though there are a few data in training sets. Therefore, based on the historical data, this paper establishes a support vector machine model for short-term prediction of renewable energy power generation.…”