Artificial Intelligence Approaches for Energy Efficiency: A Review
Summary
A review of AI techniques for improving energy efficiency in buildings, focusing on Big Data, multi-agent systems, anomaly detection, and direct vs. indirect control systems.
Key quotes
The most energy-consuming part of a building is the Heating, Ventilation, and Air Conditioning (HVAC) system
MASs have been proven to be promising as heuristic techniques for solving such problems whose domains are distributed, complex, and heterogeneous.
Direct Control IEMS (DCIEMS) includes all systems with the ability to modify the environment using actuators
Indirect Control IEMS (ICIEMS) instead puts humans in the position of the actuator, forming a human-in-the-loop architecture.
The paper explores the intersection of AI, IoT, and Big Data to reduce energy waste in smart buildings. It specifically analyzes the utility of Multi-Agent Systems (MAS) and provides a taxonomy of Intelligent Energy Management Systems (IEMS).