Applications of Data Mining in Industrial Tariff Design and Energy Management

This chapter explores the complexities and challenges of industrial energy management and tariff regulation, with a focus on demand-side management as a solution to address the imbalance between energy generation and consumption in power systems. The chapter outlines fundamental procedures, methodologies, and tools, underscoring the significance of data mining and its myriad applications in contemporary life. Through data-driven analysis and strategic decision-making, the chapter aims to understand consumption patterns, propose classification criteria to categorize different industries, propose efficient programs for pattern modification, and propose the procedure to design tailored tariff structures. Case studies of two industries demonstrate the application of data mining in energy management, revealing insights into consumption patterns and the need for tailored strategies based on industry-specific characteristics. Ultimately, the chapter emphasizes the critical importance of informed decision-making, stakeholder consultation, and risk assessment in effectively implementing demand-side management programs. Finally, a comprehensive discussion is provided to elucidate the extensive dimensions and existing challenges from multiple perspectives, underlining the necessity of considering them alongside the foundational framework established in this chapter through the proposed data mining-oriented approach. Indeed, in addition to proposing a data mining-based approach and analyzing the load data, comprehensive discussions are provided that encompass the consideration of both technical and nontechnical limitations. This includes factors such as social, legal, and economic considerations, as well as the potential consequences of tariff modifications within industries. Furthermore, the discussions extend to the broader impact on the entire electric grid and beyond, addressing societal implications.