Analysis and Evaluation of Five Short-Term Load Forecasting Techniques

Load forecast has been a central and an integral process in the planning and operation of electric utilities. Many techniques and approaches have been investigated to tackle this problem in the last two decades. These are often different in nature and apply different engineering considerations and economic analyses. In this paper a comparative evaluation of five short-term load forecasting techniques is presented. These techniques are: 1. Multiple Linear Regression; 2. Stochastic Time Series; 3. General Exponential Smoothing; 4. State Space Method; and 5. Knowledge-Based Approach. The authors have applied these algorithms to obtain hourly load forecasts (for up to 24 hours) during the winter and summer peaking seasons. Thus the five forecasting methodologies have been applied to the same database and their performances are directly compared. The forecast error analyses are provided in Tables 1 and 2 for the winter and summer days respectively. As these results are based on forecasts of two single days, these should be used for comparative purposes only. Some interesting observations are made about the results presented in Tables 1 and 2. For example, for the peak summer day the transfer function (TF) approach gave the best result, whereas for the peak winter day the TF approach resulted in the next to the worst accuracy. During the peak summer day the temperature profile was typical whereas during the peak winter day the profile was unseasonal.