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Energy performance tracking is becoming increasingly significant in the building industry as a means of improving energy efficiency. This thesis provides answers to the questions related to improving energy tracking system in general, including its potentials, problems and challenges. The implementation produces a sample dashboard in Google Looker Studio where user can inspect their property’s electricity, heat, and cooling usage from yearly down to 15-minute frequency. Additionally, user can even check the precise meters source in the dashboard. In the classification ML process, Random Forest proved to be the best model used to predict the energy class of an existing building with the accuracy of 94%. Furthermore, Explainable Artificial Intelligent (XAI) techniques, including SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME) were also utilised to address the black-box concerns by providing insights into the important factors such as building type codes, heating area, and energy consumption patterns that affect energy consumption and allowing for more transparent decision-making. Moreover, the study delves into systematic literature review to discover the current development of energy monitor system of Eros in Sweden, EM3 in Ireland, and HVAC (Heat, ventilation, and air condition) in China. It shed light into the potentials saving cost in energy, sustainable reporting, and predictive analysis. Nevertheless, the review uncovers challenges which are the skeptical of users, mismatch values, and missing actionable recommendation. Beyond the technical exploration, this paper also raises crucial ethical issues regarding privacy and data ownership by systematic literature review, questionnaires, and interview with energy experts. Overall, the finding aims to provide valuables insights into challenges and opportunities associated with property’s energy performance using dataset in Sweden. |
