Forecasting weekly electricity consumption: a case study

This paper describes the application of time-series modelling techniques to electricity consumption data for a particular power board. Modelling is performed on total consumption, the data being available on a weekly basis with exact measurements for approximately the past 11 years. Both unforced and forced models are considered. An initial data analysis is performed to ascertain the influence of temperature and rainfall inputs on the model, and later on, a spectral analysis is used to investigate the frequency components present in the time-series data. A significant component of the determination of time-series models is the selection of an appropriate model order. Both low and high order models are evaluated, and their properties compared. For the unforced case, both AR (autoregressive) and ARMA (autoregressive moving average) models are considered. For the forced case, these model structures are extended to include ARX and ARMAX models which have one or more exogenous inputs. Such models are further extended by considering the possibility of predicting the inputs to the models, when a forecasting approach is required. Simulation results are provided for all cases together with a measure of the prediction accuracy. Comparisons are made for the various model structures, as well as models based on short and long data records and models which are driven with an external noise sequence or merely released from appropriate initial conditions