Data Mining Techniques in Efficiency Analysis of Wholesale Electricity Market: A Case Study of Iran

Rostam Niakan Kalhori, Masoumeh; Taheri Emami, Iman; Hasani Marzooni, Masoud

doi:10.30503/jeedev.2022.278926.1010

Data Mining Techniques in Efficiency Analysis of Wholesale Electricity Market: A Case Study of Iran

Document Type : Original Article

Authors

Masoumeh Rostam Niakan Kalhori ¹

Iman Taheri Emami ²

Masoud Hasani Marzooni ³

¹ Assistant Professor, Department of Energy Economics, Niroo Research Institute, Iran

² Ph.D. Candidate, Department of Electrical Engineering, Amirkabir University of Technology, Iran

³ Assistant Professor, Department of Energy Economics, Niroo research institute, Iran

10.30503/jeedev.2022.278926.1010

Abstract

In this paper, predictive data mining models are employed to get insights into the efficiency of a deregulated electricity market. The bidding data of Iranian generation units in a two-phase approach are classified. Firstly, common factors that could contribute to investigating the efficiency of generation units’ bidding behavior are identified by feature selection algorithms. Then, classification rule mining algorithms are applied to extract if-then rules related to bidding blocks of generation units. The three most-applicable algorithms for classification rule mining are compared statistically. The two first algorithms are decision trees based on a direct approach. Finally, the third algorithm is the sequential covering method, perceived as an indirect approach to classification rule mining. The extracted rules are of significant importance for wholesale electricity market monitoring units (MMUs) to evaluate the market and its players thoroughly. The experimental results indicate that the partial decision tree outperforms other investigated methods.

Keywords

Deregulated Electricity Markets

Market Monitoring

Data Mining

Machine Learning

Classification Rule Mining

Subjects