[M-GI26] Data-driven approaches for weather and hydrological predictions

[MGI26-P01] Real-Time Forecasting of Energy Consumption in Residential Buildings

*Jui Sheng Chou¹ (1.National Taiwan University of Science and Technology)

Keywords:energy consumption, residential building, artificial intelligence, forecasting, cloud analytics

Efficient energy use in buildings has become a significant concern for a sustainable society. This study pioneers a cloud computing-based analytics framework within the smart grid, creating a Building Energy Efficiency Monitoring (BEEM) system. This system empowers managers to enhance energy efficiency across multiple residential buildings. The comprehensive framework integrates smart meter technology, remote sensing, Bluetooth technology, big data analytics, cloud computing, optimization algorithms, web-based information technology, and electricity pricing policies. An initial experiment was conducted to validate the potential benefits of this innovative framework. Specifically, a smart grid infrastructure and sensors were installed in a building to collect data. An automated machine learning-based prediction model was developed to anticipate future building energy usage. The BEEM system facilitates managers and encourages end-users to effectively monitor daily and monthly energy usage in buildings, supporting sustainable energy practices.