Development of building energy saving advisory: A data mining approach

Occupants’ behavior and their interaction with home appliances are crucial for assessing building energy consumption. This study proposes a new methodology for monitoring the energy consumed in building end-use loads to build an advisory system. The built system alerts occupants to take certain measures (prioritized recommendations) to reduce energy consumption of end-use loads. The quantification of potential savings is also provided upon following said measures. The proposed methodology is also capable of evaluating the energy savings performed by the occupants. The system works based on the analysis of historical data generated by occupants using data mining techniques to output highly feasible recommendations. For demonstration purposes, the methodology was tested on the real dataset of a building in Japan. The dataset includes detailed energy consumption of end-use loads, categorized as hot water supply, lighting, kitchen, refrigerator, entertainment & information, housework & sanitary, and others. Results suggest that the developed models are accurate, and that it is possible to save up to 21% of total energy consumption by only changing occupants’ energy use habits.