Industrial park electric power load pattern recognition: An ensemble clustering-based framework

Abstract

Electric power load pattern recognition from various accumulated load data is performed for energy efficiency improvement, power system operation support, and demand side management. Industrial park is a typical energy consumption scenario, in which there are many different types of energy users. In this study, a framework based on ensemble clustering is proposed for electric power load pattern recognition in industrial parks. The proposed framework can not only accelerate the ensemble clustering process but also improve the clustering performance. Specifically, we focus on the electric power load pattern recognition of business office industrial parks with air conditioners as the main energy-consuming device. The hourly air conditioning load data and total electric power load data of eight buildings in an industrial park in Suzhou, China, from January 1, 2019, to December 31, 2019, were used in the experiment. The results showed the effectiveness of the proposed framework in recognizing the electric power load patterns in industrial parks. The identified electric power load patterns could be potentially used to support more efficient and targeted industrial park energy management decision-making.