Energy Optimization Using a Case-Based Reasoning Strategy.

Area of Science:

• Building energy management
• Artificial intelligence in energy systems
• Smart building technology

Background:

• Domotics, or home automation, is increasingly popular for managing building energy consumption.
• Heating, Ventilation, and Air Conditioning (HVAC) systems represent the largest energy consumers in buildings.
• Existing energy optimization systems often fail due to incomplete monitoring of consumption variables.

Purpose of the Study:

• To present an agent-based approach for optimizing energy consumption in buildings.
• To leverage a Multi-Agent System (MAS) architecture in a Cloud environment.
• To integrate a Wireless Sensor Network (WSN) for comprehensive data collection.

Main Methods:

• Deployment of a Multi-Agent System (MAS) in a Cloud environment.
• Utilization of a Wireless Sensor Network (WSN) for real-time data acquisition.
• Application of an Artificial Neural Network (ANN) for agent learning and behavioral adaptation.

Main Results:

• The proposed system demonstrated significant energy savings in an office building setting.
• An average energy saving of 41% was achieved in the offices utilizing the experimental system.
• The MAS effectively learned social behaviors through data collection and ANN processing.

Conclusions:

• The agent-based approach integrated with Cloud and WSN is effective for energy management.
• Artificial intelligence, specifically ANNs, plays a crucial role in optimizing HVAC energy consumption.
• This methodology offers a promising solution for reducing energy waste in smart buildings.