Improved energy performance evaluating and ranking approach for office buildings using Simple-normalization, Entropy-based TOPSIS and K-means method

Abstract

Reasonable evaluation of building energy performance can provide great benefit for government (policy makers) to put forward effective energy conservation policies, and building regulators to make suitable energy-saving strategies. Traditional approaches employ the annual total energy consumption (or the annual total energy use intensity) to evaluate and rank the energy performance of buildings. However, these approaches ignore the energy consumption fluctuation of buildings in different seasons. As a consequence, this paper proposes an improved data-driven-based building energy performance evaluating and ranking approach for office building in city scale using the Simple-normalization, Entropy-based TOPSIS and K-means method. The proposed approach chooses the monthly energy use intensity in single year of office buildings as evaluation indicators, and consists of three steps: (1) The building energy use intensity are calculated by Simple-normalization method. (2) The Entropy-based TOPSIS method is applied to score and evaluate the building energy performance. (3) Finally, the K-means method is employed to rank the evaluated buildings. To validate the proposed approach, 24 office buildings in Tongling city of China are served as case study to present the evaluating and ranking procedure. Simultaneously, the comparison analysis between the proposed approach and two traditional approaches are conducted considering consistency and rationality synchronously. The comparison results demonstrate the proposed approach can more effectively score the energy performance of office buildings, and achieve the reasonable order and grade. In addition, to demonstrate the application, the proposed approach is also applied on Urban-area Building Energy Consumption Monitoring System of Tongling city in China. As a result, this study provides convenient and effective tool to present the energy efficiency gap and energy-saving potential between different buildings in city scale.