Extracting Influential Factors for Building Energy Consumption via Data Mining Approaches

Jihoon Jang, Jinmog Han, Min-Hwi Kim, Deuk-won Kim and Seung-Bok Leigh
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Jihoon Jang: Department of Architectural Engineering, Yonsei University, Seodaemun-gu, Seoul 03722, Korea
Jinmog Han: Lyles School of Civil Engineering, Purdue University, 550 Stadium Mall Dr., West Lafayette, IN 47907, USA
Min-Hwi Kim: Solar Thermal Convergence Laboratory, Korea Institute of Energy Research, 152 Gajeong-ro, Yuseong-gu, Daejeon 34129, Korea
Deuk-won Kim: Solar Thermal Convergence Laboratory, Korea Institute of Energy Research, 152 Gajeong-ro, Yuseong-gu, Daejeon 34129, Korea
Seung-Bok Leigh: Department of Architectural Engineering, Yonsei University, Seodaemun-gu, Seoul 03722, Korea

Energies, 2021, vol. 14, issue 24, 1-19

Abstract: To effectively analyze building energy, it is important to utilize the environmental data that influence building energy consumption. This study analyzed outdoor and indoor data collected from buildings to find out the conditions of rooms that had a significant effect on heating and cooling energy consumption. To examine the conditions of the rooms in each building, the energy consumption importance priority was derived using the Gini importance of the random forest algorithm on external and internal environmental data. The conditions that had a significant effect on energy consumption were analyzed to be: (i) conditions related to the building design—wall, floor, and window area ratio, the window-to-wall ratio (WWR), the window-to-floor area ratio (WFR), and the azimuth, and (ii) the internal conditions of the building—the illuminance, occupancy density, plug load, and frequency of room utilization. The room conditions derived through analysis were considered in each sample, and the final influential building energy consumption factors were derived by using them in a decision tree as being the WFR, window area ratio, floor area ratio, wall area ratio, and frequency of use. Furthermore, four room types were classified by combining the room conditions obtained from the key factor classifications derived in this study.

Keywords: heating and cooling energy consumption; influential factors; random forest; decision tree; data mining (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
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