Analyzing and Forecasting of Electricity Consumption by Integration of Autoregressive Integrated Moving Average Model with Neural Network on Smart Meter Data

Abstract

Smart metering is a recently developed research area over the globe and it appears to be a remedy for increasing prices of electricity. Electricity consumption forecasting is an essential process in offering intelligence to smart girds. Rapid and precise forecasting allows a utility provider to plan the resources and also to take control actions to balance the electricity supply and demand. The customers will advantage from the metering solutions by a greater understanding of their own energy utilization and forthcoming projections, allowing them to effectively manage the cost of their consumption. In this view, this paper presents an Integration of Autoregressive Integrated Moving Average (ARIMA) Model with Neural Network (NN) for Electricity Consumption Forecasting using Smart Meter Data. As the time series data often does not hold linear as well as nonlinear patterns, ARIMA or NN models are not enough to model and predict the time series data. The ARIMA-NN model will be trained using the data and generates a model. Afterward, the generated model can be utilized to predict the electricity consumption by the application of new building data. The proposed ARIMA-NN model is evaluated and the simulation outcome strongly pointed out its superior performance over the compared methods. The presented model has obtained effective testing performance with the MAPE of 25.53, an accuracy of 48.38, and MSE of 0.21.