Dissertation Defence: Adaptive Energy Management in Institutional Building Clusters (IBCs): A Data-driven Approach

Vipul Moudgil, supervised by Dr. Rehan Sadiq, will defend their dissertation titled “Adaptive Energy Management in Institutional Building Clusters (IBCs): A Data-driven Approach” in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Electrical Engineering.

An abstract for Vipul Moudgil’s dissertation is included below.

Examinations are open to all members of the campus community as well as the general public. Please email rehan.sadiq@ubc.ca to receive the Zoom link for this exam.

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Abstract

Despite low building density compared to urban areas, institutional building clusters (IBCs) hold substantial potential for enhancing sustainability. The global expansion of higher education has propelled IBCs to the forefront of sustainable urban planning, particularly in peak energy demand scenarios, where buildings in IBCs concurrently draw electricity at higher rates compared to their contractual demand. Peak demand charges and penalties, levied by energy providers, skyrocket to about 100 times regular electricity prices, consolidating to nearly one-fourth of the institution’s annual energy expenses. For large-scale universities these penalties may accumulate to a million-dollar value, placing a substantial economic burden on IBC facility management and operations. Hence, an adaptive energy management (AEM) framework is a requisite for empowering IBC facilities to foster energy efficiency and economic viability during peak energy demand scenarios.

To meet this imperative, this research developed a data-driven AEM framework to investigate building energy trends, determining buildings leading to peak demand scenarios, predicting their future peak instances, and optimizing peak demands considering onsite renewable energy generation and storage. The study initiates by scrutinizing state-of-the-art technologies and techniques influencing the energy and peak demand dynamics within IBCs. Further, a novel quantile-based data analysis technique was implemented to identify electrically inefficient buildings in IBCs. The results indicated that electrical fluctuations alone did not necessarily result in peak demands; a comprehensive analysis of both fluctuations and the overall impact of buildings is required for holistic comparison. Furthermore, considering climatic influences and interbuilding effects, a deep learning network was developed to forecast future demand across multiple buildings simultaneously. The network achieved a prediction accuracy improvement of 1.8% to 10.9% compared to existing methods. Lastly, an optimization strategy was implemented, conceptualizing IBC as an energy hub constituting renewable energy and energy storage technologies to optimize peak demand scenarios. The findings indicate that, given current energy pricing, as well as equipment investment, operation, and maintenance costs, achieving a positive net present value requires an interest rate below 4% and solar efficiency exceeding 16%. Furthermore, the battery energy and power costs must remain below $CAD 280 and $CAD 420, respectively, to ensure economic feasibility while optimizing peak demand over the product’s lifetime. The research outcomes carry profound implications, offering valuable insights and benefits for facility managers and policymakers to exercise sustainable operations in IBCs.