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

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