Correlation between building characteristics and associated energy consumption: Prototyping low-rise office buildings in Shanghai

Abstract

The enormous building energy consumption in Shanghai necessitates the identification of standard buildings to offer guidance for the adequate design of retrofitting strategies in order to promote a sustainable built and architectural environment. In this regard, this study develops a methodological approach to establish prototypical buildings using performance index system (PIS) founded on an on-site survey. Emphasis is focused on low-rise office buildings in Shanghai. A total of 10 office parks containing 136 single low-rise office buildings in Min Hang District were systemically selected for survey and data collection. The proposed PIS includes building orientation, number of floors, window/wall ratio, heat and cold source type, plan form, and construction year. Using cluster and correlation analysis, the surveyed buildings are classified based on the impact of each PIS on the annual building energy use intensity. Based on this approach, the most influencing indexes are construction year, the number of floors, window-wall ratio and building orientation. This result refines the surveyed building samples to four prototypical buildings as representative standards for low-rise office buildings. Subsequently, typical buildings representing each of the prototypical buildings were defined. The stipulated approach provides a systematic framework for building classification, characteristic-based evaluation of building energy performance and identification of key performance index for building retrofit purposes.

Coordinated energy-environmental-economic optimisation of building retrofits for optimal energy performance on a macro-scale: A life-cycle cost-based evaluation

“…The archetypes/prototypes are established using top-down macro-economic and statistic tools to analyse the empirical building data obtained from a large-scale on-site survey and surrogate sources. A detailed description of the methods under this step and the prototypes obtained thereafter are presented in the literature [18]. Each prototype represents a group of buildings and can be employed as the foundation for assessing the retrofit measures and to extrapolate the energy performance for the represented buildings.…”

A target-driven decision-making multi-layered approach for optimal building retrofits via agglomerative hierarchical clustering: A case study in China

“…The detailed description of this methodology is presented in our previous study [19]. Due to the lack of sufficient aggregated data, this methodology uses empirical databases generated from a large-scale survey of building samples and top-down macro-economic and statistic tools.…”

“…In this study, the city of Shanghai, which is one of the most industrial and populous city in the hot-summer-cold-winter (HSCW) climate zone of China [19] is selected. The city has a large share of old low-rise office buildings (LOB) (50% of the commercial building blocks) [23,24].…”

“…The city has a large share of old low-rise office buildings (LOB) (50% of the commercial building blocks) [23,24]. For demonstration, Minhang district is selected as the territorial boundary given that it has the largest non-residential buildings (6,414 m 2 building area) with a vast age distribution [19]. To promote sustainable urban planning in this city, the old existing LOB requires retrofitting to meet the set energy performance target stipulated in China's regulation.…”

“…Here, a survey of 10 randomly selected office parks with 136 LOB is conducted. The on-site and surrogate data collected are presented in our previous study [19]. Also, the study describes the proposed prototypical LOB used in this present study.…”