Short-term Forecasting of Nodal Electricity Demand in New Zealand

This work is part of a Honours dissertation written by Michael Simpson under the supervision of Erwann Sbaï. This paper compares the accuracy of several models for short-term electricity load forecasting for nodes in New Zealand. The literature on short-term electricity load forecasting is reviewed, resulting in the selection of the seasonal ARIMA model and the double seasonal Holt-Winters exponential smoothing model for empirical testing. We also estimate a one season ARIMA model with some double seasonal aspects. A multivariate regression model is also developed for comparison, utilising aspects of several multivariate models used in other short-term electricity load forecasting papers. A time series of half-hourly electricity demand in the Hayward node in New Zealand is used for estimation and forecasting. The data clearly indicates a daily and a weekly seasonal pattern. The seasonal ARIMA model outperforms the Holt-Winters and multivariate regression models in forecasting electricity demand from half an hour to three hours ahead. The superiority of the seasonal ARIMA model increases as the forecasting length increases. Significant autocorrelation of residuals in the Holt-Winters and multivariate regression models leads to an error adjustment term being included, which improves the forecasting accuracy of the Holt-Winters models but not the multivariate regression model