Weather corrected electricity demand forecasting

Abstract

Electricity load forecasts now form an essential part of the routine operations of electricity companies. The complexity of the short-term load forecasting (STLF) problem arises from the multiple seasonal components, the change in consumer behaviour during holiday seasons and other social and religious events that affect electricity consumption. The aim of this research is to produce models for electricity demand that can be used to further the understanding of the dynamics of electricity consumption in South Wales. These models can also be used to produce weather corrected forecasts, and to provide short-term load forecasts. Two novel time series modelling approaches were introduced and developed. Profiles ARIMA (PARIMA) and the Variability Decomposition Method (VDM). PARIMA is a univariate modelling approach that is based on the hierarchical modelling of the different components of the electricity demand series as deterministic profiles, and modelling the remainder stochastic component as ARIMA, serving as a simple yet versatile signal extraction procedure and as a powerful prewhitening technique. The VDM is a robust transfer function modelling approach that is based on decomposing the variability in time series data to that of inherent and external. It focuses the transfer function model building on explaining the external variability of the data and produces models with parameters that are pertinent to the components of the series. Several candidate input variables for the VDM models for electricity demand were investigated, and a novel collective measure of temperature the Fair Temperature Value (FTV) was introduced. The FTV takes into account the changes in variance of the daily maximum and minimum temperatures with time, making it a more suitable explanatory variable for the VDM model. The novel PARIMA and VDM approaches were used to model the quarterly, monthly, weekly, and daily demand series. Both approaches succeeded where existing approaches were unsuccessful and, where comparisons are possible, produced models that were superior in performance. The VDM model with the FTV as its explanatory variable was the best performing model in the analysis and was used for weather correction. Here, weather corrected forecasts were produced using the weather sensitive components of the PARIMA models and the FTV transfer function component of the VDM model.