Short-term Forecasting of Anomalous Load Using Rule-based Triple Seasonal Methods

TL;DR

This paper introduces a rule-based framework that adapts advanced seasonal forecasting methods to model both normal and anomalous load patterns, especially on special days like public holidays, using nine years of data.

Contribution

It demonstrates how existing seasonal load forecasting methods can be integrated with rule-based approaches to effectively model anomalous load conditions.

Findings

Combination of two rule-based methods yields the most accurate forecasts.

The approach successfully models load on special days with significant deviations.

Methods are validated on nine years of half-hourly load data from Great Britain.

Abstract

Numerous methods have been proposed for forecasting load for normal days. Modeling of anomalous load, however, has often been ignored in the research literature. Occurring on special days, such as public holidays, anomalous load conditions pose considerable modeling challenges due to their infrequent occurrence and significant deviation from normal load. To overcome these limitations, we adopt a rule-based approach, which allows incorporation of prior expert knowledge of load profiles into the statistical model. We use triple seasonal Holt-Winters-Taylor (HWT) exponential smoothing, triple seasonal autoregressive moving average (ARMA), artificial neural networks (ANNs), and triple seasonal intraweek singular value decomposition (SVD) based exponential smoothing. These methods have been shown to be competitive for modeling load for normal days. The methodological contribution of this paper is to demonstrate how these methods can be adapted to model load for special days, when used in conjunction with a rule-based approach. The proposed rule-based method is able to model normal and anomalous load in a unified framework. Using nine years of half-hourly load for Great Britain, we evaluate point forecasts, for lead times from one half-hour up to a day ahead. A combination of two rule-based methods generated the most accurate forecasts.