Artificial Intelligence Approaches for Energy Efficiency: A Review
Alberto Pasqualetto, Lorenzo Serafini, Michele Sprocatti
TL;DR
The paper surveys AI approaches for energy efficiency with emphasis on multi-agent systems in smart buildings, leveraging Big Data and IoT to enable DSM and anomaly detection. It analyzes Big Data characteristics, data sources, and real-time processing, and reviews MAS architectures, communication standards, and learning-based optimization. Key contributions include a classification of IEMS into direct vs indirect control, a review of anomaly detection approaches, and identification of future research directions such as combining Deep Learning with Reinforcement Learning and privacy-preserving strategies. The work highlights potential energy and health benefits while acknowledging privacy and security challenges that must be addressed for real-world deployment.
Abstract
United Nations set Sustainable Development Goals and this paper focuses on 7th (Affordable and Clean Energy), 9th (Industries, Innovation and Infrastructure), and 13th (Climate Action) goals. Climate change is a major concern in our society; for this reason, a current global objective is to reduce energy waste. This work summarizes all main approaches towards energy efficiency using Artificial Intelligence with a particular focus on multi-agent systems to create smart buildings. It mentions the tight relationship between AI, especially IoT, and Big Data. It explains the application of AI to anomaly detection in smart buildings and a possible classification of Intelligent Energy Management Systems: Direct and Indirect. Finally, some drawbacks of AI approaches and some possible future research focuses are proposed.