Integrating willingness analysis into investment prediction model for large scale building energy saving retrofit: Using fuzzy multiple attribute decision making method with Monte Carlo simulation. (January 2019) - Zheng, Donglin
Highlights: An investment prediction model for large scale building energy saving retrofit (LSBESR) was proposed. Fuzzy Multiple Attribute Decision Making (FMADM) and Monte Carlo (MC) analysis were integrated in the investment prediction model. A MC prediction method for estimating investment of LSBESR by a probability matrix of individual building retrofit scheme was proposed. The Analytical Hierarchy Process (AHP) of building owners' willingness for building retrofit was established. The simulation analysis of the investment distribution for large-scale energy retrofit in 100 buildings in Shanghai was carried out. Abstract: Nowadays, it has emerged a trend to carry out large scale building energy saving retrofit (LSBESR) in China. LSBESR is determined by a range of endogenous and exogenous factors. In particular, uncertainties concerning owners' willingness of retrofit exacerbate the difficulty of investment prediction. In this paper, an investment prediction model (IPM) for LSBESR is proposed by considering owners' willingness factors that possess fuzzy attributes and random characteristics. By definition, IPM integrates fuzzy multiple attribute decision making (FMADM) and Monte Carlo (MC) analysis. First, this paper proposes analytic hierarchy process (AHP) of retrofit willingness. Second, the membership function is constructed. Third, the authors suggest using MC simulation to predict investment. Meanwhile, 100 public buildings of LSBESR in Shanghai are investigated, based on which the authors obtained double peak distribution of LSBESR investment. Compared with the high investment scheme ($154 million), the expected value of investment (EVI) is shown to be $89 million and the probability of greater than EVI is 60.5%. Moreover, the authors revealed a strong logarithmic relationship between willingness and EVI. Relying on investigation willingness factor, people can quickly get the EVI of LSBESR. In summary, this paper is capable of providing a new perspective to the decision maker.