Dynamic Load Usage Behavior Simulation in Smart Grids: A Data-Driven Approach in Urban Buildings

Smart grids are essential for sustainable urban energy systems, improving efficiency and integrating renewable sources. Accurately forecasting load demand is key for effective management, but is challenging due to unpredictable behaviors and dynamic consumption patterns. This paper introduces a new data-driven approach using smart meter data from various buildings in Cardiff, UK to better understand electricity consumption behaviors across seasons. Our methodology combines machine learning techniques with an in-depth analysis of physical building characteristics to conduct dynamic load usage behavior simulation. We employ consensus-based clustering to identify buildings with similar consumption behaviors and track dynamic changes in load usage over time. Furthermore, we identify key load-related features that influence consumption patterns, enhancing the precision of load demand forecasting. Empirical validation of our approach underscores its effectiveness in enhancing forecast accuracy and providing robust, sustainable strategies for energy management within the smart grid paradigm.