| Abstract: | This paper presents the results of the Electric Power Research Institute Short Range Forecasting Project (EPRI-SRF) performed by the Load Forecasts Department, Economics and Forecasts Division of Ontario Hydro, Ontario, Canada. In this study a variety of short-range forecasting techniques are applied to Ontario Hydro monthly data on total system energy demand. These techniques are available in a software package (FORECAST MASTER) developed for EPRI by two consultants—Scientific Systems, Inc. (SSI) and Quantitativ Economic Research, Inc. (QUERI). The methods used for this study were the univariate Box-Jenkins method, the multivariate state-space method, Bayesian vector autoregression and autoregress ve econometric regression. A comparison of the models developed show that the econometric models perform the best overall. The state-space models are more suitable for very short-term (one-step ahead) forecasts. Although the Box-Jenkins method has the advantage of simplicity in terms of estimation and data requirement, its performance was not as good as that of the others. Bayesian vector autoregresson results indicate that this program needs some modification for monthly data. |
