Short-term Electric Power Forecast Using Autoregressive Integrated Moving-Average (ARIMA)

ABSTRACT Demand planning for electricity consumption is a key development of any country. The aim of this project is the study of short-term electric power forecasting in Oleh, Delta State-Nigeria power sub-station however, this can only be achieved if the demand is forecasted accurately. In this research, autoregressive integrated moving average (ARIMA) was utilized to formulate prediction models of the electricity demand in Oleh. The objective was use the empirical data obtained for historical data regarding the electricity demand in Oleh from June to November 2016. The results showed that the ARIMA model reduced the mean absolute percentage error (MAPE) to 3.36%. Key words: Autoregressive integrated moving average (ARIMA), Moving Average (MA), Load Forecasting, Multiple Linear Regressions, Autocorrelation Function (ACF), DigDB Toolbox, Mean Absolute Percentage Error (MAPE)