Data‐Driven Building Energy Management in the Smart Microgrid: A Dynamic Demand Response Programs Approach Based on NILM

ABSTRACT

This paper proposes a data‐driven building energy management (DDBEM) framework for smart microgrids that enhances economic efficiency and promotes fairness‐aware energy allocation. Utilising nonintrusive load monitoring (NILM) with a multitask gated recurrent unit (M‐GRU) model, the framework disaggregates smart metre data into appliance‐level consumption profiles to extract consumer behavioural features during peak periods. Consumers are subsequently classified using the k‐means algorithm based on appliance‐level characteristics, and a quantitative consumption score is assigned to each consumer. These scores are integrated into a dual‐objective microgrid scheduling model that minimises operational costs and energy not supplied (ENS) while dynamically adjusting demand response programs (DRPs) at the individual appliance level. Simulation results demonstrate that the proposed framework reduces total operating costs by up to 10.1% compared to the baseline scenario while enabling fairness‐oriented resource allocation. The energy supplied to energy‐efficient consumers increases by an average of 36%, reflecting the proposed fairness‐oriented allocation strategy. Additionally, the framework decreases outage frequency for low‐consumption consumers and mitigates peak demand through targeted DRPs. These findings indicate that integrating NILM‐based behavioural analysis with dynamic appliance‐level DRPs offers an effective and scalable solution for energy management in smart microgrids.