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Li, Rui

University of British Columbia

This thesis introduces an integrated approach aimed at boosting energy efficiency and advancing sustainability in buildings via innovative Demand Response (DR) programs and the intelligent management of Heating, Ventilation, and Air Conditioning (HVAC) systems. By addressing the fragmented efforts in current DR initiatives and the limitations of traditional HVAC control methods, this study introduces two groundbreaking frameworks that collectively provide an economically viable pathway towards reducing carbon emissions, elevating energy efficiency, and improving comfort for occupants in the built environment. First, I propose an integrated DR-based framework that utilizes medium-term electricity Demand Forecasting (DF) and optimal design and management of Battery Energy Storage Systems (BESS). This approach, leveraging robust 30-day ahead DF based on a state-of-the-art (SOTA) Transformer model, delivers significant electricity cost savings of C$ 311K and a reduction of 471 tonnes of CO₂-equivalent (CO₂-e) for 72 target buildings over winter months. These results underscore the framework's potential to revolutionize energy management and sustainability on an urban scale. Concurrently, I address the rigidity of traditional Rule-Based Feedback Control (RBFC) systems in HVAC control by introducing a novel Deep Reinforcement Learning (DRL) framework, which is expected to respond to unseen system dynamics effectively. This framework incorporates the same Transformer model for superior Time-Series forecasting (TF), enabling a more accurate RL training environment. The study demonstrates the framework's effectiveness in HVAC system modeling and control, achieving an average of 23.8% higher prediction accuracy of HVAC system operations over baseline models. Moreover, the proposed past observable RL agent significantly enhances performance, yielding a 44.2% and 39.6% improvement in synthetic reward metrics (corresponding to energy consumption and thermal discomfort), compared to RBFC and standard RL agents. Together, these integrated frameworks highlight the synergy between advanced DR strategies and intelligent HVAC control, facilitated by cutting-edge machine learning (ML) techniques. By combining precise DF, optimal energy storage management, and adaptive HVAC control, this thesis contributes to the fields of sustainable building design and operational optimization. The findings not only showcase the potential for substantial economic and environmental benefits, but also pave the way for future research in applying advanced computational methods for building management.

Text

2024-04-15

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/87837

Civil Engineering

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/