Short Term Load Forecasting Using ANN and WNN

Abstract

The primary objective of Short-Term Load Forecasting, often known as STLF, is to forecast load with a lead time of anything from one hour to seven days. In this study, we suggest the use of AI approaches for short-term load forecasting. Some examples of these techniques are artificial neural networks (ANN) and wavelet neural networks (WNN) (STLF). A reliable prediction makes the challenge of managing generation and load much more manageable. The ANN and WNN algorithms are used in order to calculate the STLF for the next day. The comparison between the two approaches as well as their performance is outlined, and the normalized MAPE error for one day ahead is shown in the article as well. In addition to this, the Validation is performed on the TNEB testing system. This study provided an application of AI techniques, specifically ANN and WNN, for STLF applications in power systems. For hourly load forecasting, analyses of the capabilities and characteristics of ANN and WNN are done. The suggested strategies have already been tried and tested in actual world settings with great success.