Benchmarking air-conditioning energy performance of residential rooms based on regression and clustering techniques. (1st November 2019)

• Record Type: Journal Article
• Authors: Zhou, Yuren; Lork, Clement; Li, Wen-Tai; Yuen, Chau; Keow, Yeong Ming
• Is Part Of: Applied energy. Volume 253(2019)
• Publication Date: 2019-11-01
• Subjects: Energy performance benchmarking -- Air conditioning -- Data-driven approach -- Machine learning -- Predictive model -- Clustering; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2019.113548
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: A summary of the general steps and related work of developing a benchmarking approach is provided. A new approach is proposed to fairly benchmark air-conditioning energy performance of residential rooms. Different regression model structures are tested for predicting air-conditioning power consumption. K-means clustering is adopted to cluster the studied rooms based on areas and air-conditioning settings. A case study is presented to demonstrate the effectiveness of the proposed benchmarking approach. Abstract: Air conditioning (AC) accounts for a critical portion of the global energy consumption. To improve its energy performance, it is important to fairly benchmark its energy performance and provide the evaluation feedback to users. However, this task has not been well tackled in the residential sector. In this paper, we propose a data-driven approach to fairly benchmark the AC energy performance of residential rooms. First, regression model is built for each benchmarked room so that its power consumption can be predicted given different weather conditions and AC settings. Then, all the rooms are clustered based on their areas and usual AC temperature set points. Lastly, within each cluster, rooms are benchmarked based on their predicted power consumption under uniform weather conditions and AC settings. A real-world case study was conducted with data collected from 44 residential rooms. Results show that the constructed regression models have an average prediction accuracy of 85.1% in cross-validation tests, and support vector regression with Gaussian kernel is the overall most suitable model structure for building the regression model. In the clustering step, 44 rooms are successfully clustered into seven clusters. By comparing the benchmarking scores generated by the proposed approach with two sets of scores computed from historical power consumption data, we demonstrate that the proposed approach is able to eliminate the influences of room areas, weather conditions, and AC settings on the benchmarking results. Therefore, the proposed benchmarking approach is valid and fair. As a by-product, the approach is also shown to be useful to investigate how room areas, weather conditions, and AC settings affect the AC power consumption of rooms in real life.