Short Term Load Forecasting under extensive Power Outages using Domestic Energy Meter Load Profile; A case study

Abstract

Short-term load forecasting (STLF) is an obligatory and vibrant part of power system planning and dispatching. It is utilized for short and running targets in power system planning. Electricity consumption has nonlinear patterns due to its reliance on factors like time, weather, geography, culture, and some random and individual events. This research work emphasizes STLF through utilized load profile data from domestic energy meter and forecasts it by Multiple Linear Regression (MLR) and Cascaded Forward Back Propagation Neural Network (CFBP) techniques. First, simple regression statistical calculations were used for prediction, later the model was improved by using a neural network tool. The performance of both models compared with Mean Absolute Percent Error (MAPE). The MAPE error for MLR was observed as 47% and it was reduced to 8.9% for CFBP.