A Multi-Feature Based Approach Incorporating Variable Thresholds for Detecting Price Spikes in the National Electricity Market of Australia

Abstract

Detecting electricity price spikes is crucial as it helps market participants to gain confidence and formulate appropriate strategy to maximize their benefits. In this paper, a multi-feature based approach with the incorporation of variable thresholds is developed to detect electricity price spikes in the national electricity market of Australia. The variable thresholds, which are determined using a weighted sliding window average and an adjusted standard deviation, help to segregate spikes from normal price variations. Also, significant features are extracted from the market after thoroughly analyzing the underlying causes resulting into the price spikes. These features are employed as inputs to a support vector machine to classify electricity prices as spikes or non-spikes. A case study is conducted using a dataset acquired from the state of New South Wales, Australia. The results show that the proposed method can successfully detect the price spikes with high accuracy and confidence.

Keywords

• Electricity price
• feature selection
• kernel regression
• price spikes
• support vector machine
• variable threshold