Towards autonomous energy management: machine learning for effective auditing and optimization.

ABSTRACT: This study presents a fully automated procedure for energy management and auditing, applicable to a diverse range of residential and commercial loads, leveraging machine learning techniques across three key phases: load classification, benchmarking, and smart monitoring. The model effectively categorizes energy loads based on consumption patterns, establishes performance benchmarks through historical data analysis, and employs real-time monitoring to identify inefficiencies and predict future energy usage. Evaluating the model through four distinct case studies demonstrates its capability to optimize energy consumption in a techno-economic manner, achieving significant energy savings of 34.73 MWh/year for essential loads in Egypt, 215.67 MWh/year for HVAC systems in a university building,

SUBMITTER: Ashraf S 

PROVIDER: S-EPMC12603143 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature