ECONOMIC PREDICTIVE CONTROL FOR SUSTAINABLE ENERGY MANAGEMENT USING MACHINE LEARNING
Keywords:
KEY WORDS: energy management, machine learning, predictive modelling, sustainabilityAbstract
The world's energy sector faces increasing challenges, including rising demand and efficiency needs, changing supply and demand patterns, and the lack of optimal management analysis. These challenges are particularly pronounced in developing countries. Utilizing machine learning (ML) to analyze energy sector data can help address these issues. ML algorithms can examine equipment data, create predictive models, and address sustainability concerns. In smart cities, ML algorithms can autonomously respond to electricity price fluctuations and manage energy consumption. ML-based systems can also assist energy suppliers in adapting to the variability of renewable energy sources. As interest in low-emission energy grows and dependence on oil decreases, the installation capacity of solar PV, wind farms, and marine energy systems is expanding globally. Therefore, artificial intelligence and machine learning are essential for effectively managing energy sector challenges. Microgrid control poses significant challenges that require advanced techniques such as model predictive control (MPC). This paper focuses on energy management in microgrids using MPC and provides an overview of the latest developments in MPC methods for sustainable energy management.
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