The Time Series Approach to Short Term Load Forecasting

TLDR

Time‑series analysis, especially Box‑and‑Jenkins models, is widely used for load forecasting because of its systematic autocorrelation‑based procedures, but it struggles to capture nonlinear load‑temperature relationships. The study introduces a simple procedure to address the nonlinear load‑temperature limitation and compares several Box‑and‑Jenkins models with a utility’s existing forecasting method. The authors develop a straightforward method that incorporates nonlinear adjustments and evaluate multiple Box‑and‑Jenkins models against the utility’s current forecasting procedure. The results demonstrate that Box‑and‑Jenkins time‑series models are well suited for short‑term load forecasting.

Abstract

The application of time series analysis methods to load forecasting is reviewed. It is shown than Box and Jenkins time series models, in particular, are well suited to this application. The logical and organized procedures for model development using the autocorrelation function and the partial autocorrelation function make these models particularly attractive. One of the drawbacks of these models is the inability to accurately represent the nonlinear relationship between load and temperature. A simple procedure for overcoming this difficulty is introduced, and several Box and Jenkins models are compared with a forecasting procedure currently used by a utility company.