Data Mining for Electricity Price Classification and the Application to Demand-Side Management

TLDR

Forecasting electricity prices is crucial for optimal scheduling in competitive markets, yet most approaches focus on point forecasts while demand‑side management requires price thresholds. This study aims to cast future electricity prices as a classification problem to support demand‑side management. We evaluate several data‑mining techniques and introduce a novel data model to construct the initial dataset for price classification. Simulations on New York, Ontario, and Alberta markets show the effectiveness of the classification methods, and the results are applied to a demand‑side management case study.

Abstract

Forecasting electricity prices plays a significant role in making optimal scheduling decisions in competitive electricity markets. Predominantly, price forecasting is performed from a “point forecasting” perspective, i.e., forecasting the exact values of future prices. However, in some applications, such as demand-side management, operation decisions are made based on certain price thresholds. It is, hence, desirable to obtain the “classes” of future prices, which can be cast as an electricity price classification problem. In this paper, we investigate the application and effectiveness of several data mining approaches for electricity market price classification. In addition, we propose a new data model for forming the initial data set for price classification. Simulation results for New York, Ontario, and Alberta electricity market prices are provided. Finally, the application of the generated numerical results to a demand-side management case study is demonstrated.