Energy Demand Modelling and ANN Based Forecasting using MATLAB/Simulink

Due to the rapid population growth, the load of electricity has increased sharply, but in comparison the assets used in the power generation are decreasing day by day. So there is a need to correctly predict the load and make a proper plan to handle the situation. It can be expected that the power management structure can be used to improve the stability of power supply and demand. The proposed flow regulator is mainly based on the rules of demand supply modeling of energy. In order to manage energy, this paper provides a method for predicting future power use of residential structures. Load forecasting allows an electric powered application to make crucial choices on buying and producing electric power, load switching and infrastructure development. However, with the deregulation of the power, load forecasting is even extra crucial. Short time load forecasting (STLF) can assist to estimate load flows and to make choices which could save overloading. MATLAB Simulation for the purpose of forecasting, using Artificial Neural Network (ANN) is performed for the assessment of real and forecasted load. The predicted performance is calculated with root mean square error (RMSE) and mean absolute percentage error (MAPE) in this paper. At the end, the predicted performance is also compared with the regression methods using neural regression.