Exploring the power of machine learning to drive energy efficiency in Halifax, West Yorkshire, England: A predictive energy efficiency model for sustainable and resilient buildings and households

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Authors
Garg, Ayush
Issue Date
2023-08
Degree
Msc in Business Analytics
Publisher
Dublin Business School
Rights
Items in eSource are protected by copyright. Previously published items are made available in accordance with the copyright policy of the publisher/copyright holder.
Abstract
Population growth and urbanization have increased building energy demand over the past few decades, which has become linked to environmental issues like climate change, air pollution, and thermal imbalances, which have serious health consequences. Halifax, West Yorkshire, has many homes with energy ratings of D and E, which increases CO2 emissions and depletes energy resources. This study examines energy efficiency in buildings by studying climatic, energy usage, and structural elements that affect energy ratings. The effective analysis and administration of this region remain unknown despite earlier studies. This research develops and assesses six machine learning classification models—SVM, RF, GB, XGBoost, KNN, and ET—to forecast energy ratings in the UK's Energy Performance Certificate (EPC) standard rating scale. Model parameters are optimized, important aspects are prioritized, and computational efficiency is being assessed.Sensitivity and correlation analysis illuminate key factors. Ensemble learning can accurately estimate energy performance, which is promising. This study improves Halifax's building energy efficiency image by suggesting greener energy management practices.