Novel hybrid electric load forecasting model using arima model and discrete wavelet transform

Abstract

Energy is the first and foremost part of the socio-economic and political world in which we newlinelive. The most important of the various forms of energy is electricity. There is always a gap newlinebetween the supply and demand for the electric energy. To meet the ever increasing demand newlineof the electricity consumption there is a dire need for an accurate prediction model that can newlineprove useful. In the present work, electricity consumption forecasting model is designed for newlinethe State of Punjab, India, in which the dynamic relationship among the time series entities is newlineexplored. The time-series data comprises a variety of information in their samples consisting newlineof both linear and nonlinear data. Based on the type of Time Series Data, models such as newlinelinear and nonlinear can be applied. In this work, the direct model techniques used are Auto- newlineRegressive Integrated Moving Average (ARIMA), whereas nonlinear model techniques used newlineare optimization algorithms, i.e., Cuckoo Search (CS), and Artificial Neural Network (ANN). newlineTo achieve optimum accuracy, instead of using these techniques individually, a hybrid model newlinehas been developed which was further applied on the Time Series Data of electricity newlineconsumption in Punjab. Initially, the data is decomposed into two levels using the Discrete newlineWavelet Transform (DWT) method. DWT is used to decompose the Punjab State Power newlineCorporation Limited (PSPCL) data into two parts based on electricity consumption, which newlinehelps to determine the highest and the lowest electricity consumption. On each decomposed newlinedata ARIMA technique is applied individually to obtain a Time Series Data. Then, Inverse newlineDiscrete Wavelet Transform (IDWT) is applied to combine the data, which is further newlineoptimized using a nature-inspired CSA technique. The Artificial Neural Network (ANN) newlinealgorithm is used to train the designed model by passing the optimized data to its input layers, newlinewhich helps for the prediction of electricity consumption in the future. After applying newlineARIMA, the accuracy of the forecasting model is 83.53% and ARIMA w