Forecasting IRAN’s max daily demand for electricity in different weather types using deep learning approach

Abstract

Electricity network management is a complicated issue that needs a forecast of consumption. This predication is generally hard and a challenging topic due to nonlinearity and permanent fluctuation of load in the network. Although linearities of time-series load may give an estimation, linearization of nonlinear data to foresee removes the nature of information which shows anomalies in electricity demand. New approaches of (ANN) help to make a forecast model with keeping information properties. In this paper, we categorized max daily load data in IRAN based on climate zone to figure the following 2 months. Long Short-Term Memory (LSTM) from deep learning approach picks and yields contrasted with different steps of epochs. The outcomes appear epochs do not necessarily improve the accuracy of prediction and best MADP for cold, tropical and temperament zone, are 1.26%, 2.5%, and 10.49%, respectively.