Energy consumption prediction for buildings is important in cutting costs of operation, enhancing energy efficiency, and supporting smart grid management. Reliable predictions of energy demand can assist in creating energy-efficient plans and sustainable urban planning. The present work covers diverse methodologies and models used to predict energy consumption in buildings, such as statistical, machine learning, and deep learning methods. It further investigates the role of external considerations including weather conditions, occupancy profiles, and building design. It touches on real-time data relevance and the influence of the Internet of Things (IoT) on energy consumption prediction. Ultimately, the article wraps up by examining future direction and challenges within energy consumption prediction in buildings.
python, ml, deep learning, time series analysis, smarts buildings, energy forecasting
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