Extracting Influential Factors for Building Energy Consumption via Data Mining Approaches

Seung-Bok Leigh,Deuk-won Kim,Jihoon Jang,Min-Hwi Kim,Jinmog Han

doi:10.3390/en14248505

Seung-Bok Leigh, Deuk-won Kim + Show 3 more

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https://doi.org/10.3390/en14248505

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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.

Highlights

The importance of total energy management is evolving as the need for energy saving is being increasingly emphasized worldwide
In order to figure out influential factors, both empirical sensor data and building design conditions were utilized to construct machine learning models
This study investigates which characteristics of space have a significant effect on the heating and cooling energy consumption using outdoor and indoor environmental data collected from buildings

Summary

Introduction

The importance of total energy management is evolving as the need for energy saving is being increasingly emphasized worldwide. With the development of new technologies such as information and communication technology and big data, energy management systems (EMSs) that comprehensively manage energy have drawn attention. These systems efficiently manage energy consumption, maintaining a comfortable indoor environment by collecting and analyzing data in real time using sensors installed throughout the building [3]. To receive zero-energy building certification, buildings must satisfy various criteria and conditions, one of which is to install a BEMS [5] Owing to such efforts, the application of EMSs to buildings is on the increase

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