Edge-Based Distributed Energy Analytics for Multi-tenant Buildings

Justin Pradipta, Edi Leksono, Bayu Samudiyo

Abstract


Abstract: Multi-tenant buildings present unique challenges for energy analytics due to diverse energy usage patterns and privacy concerns. This paper proposes an edge-based distributed energy analytics approach tailored for multi-tenant buildings. By deploying edge devices within each tenant's premises, we enable localized data collection, processing, and analysis while respecting data privacy. Our framework facilitates tenant-specific energy insights, anomaly detection, and load forecasting, allowing for targeted energy management strategies. Through extensive simulations and case studies, we evaluate the performance and scalability of our approach. We also address the challenges of data synchronization, privacy preservation, and system integration. Our research contributes to the development of efficient energy analytics solutions in the context of multi-tenant buildings, paving the way for improved energy efficiency and tenant satisfaction.

References


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