Short-term load forecasting using an Artificial Neural Network for Battery Energy Storage System
DOI:
https://doi.org/10.24084/repqj14.313Keywords:
Load forecasting, Artificial Neural Network, Smart grid, Back propagation, Multi layer perceptronAbstract
Load levelling that use recent spotlighted the Battery Energy Storage System (BESS) saves spare electricity or low cost electricity. The saved electricity can be used for peak power demand time. In this paper, load is predicted for purpose of operating the BESS effectively. By analysing pattern of industrial load, it is classified into workdays, Saturday and holidays. Then apply ANN on workdays load pattern from classified load patterns to predict load. To increase accuracy, number of neurons in hidden layer and learning data period are changed for estimation of load. After that, error rate was calculated for comparison and analysis.
