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, 0.9 MWh/year for a hybrid lighting system in a bank branch, and 0.9 MWh/year for a residential house. The results underscore the model's effectiveness in promoting energy efficiency and sustainability, highlighting its transformative potential in adapting to the evolving energy needs of various applications while facilitating substantial cost savings.