A hybrid Decision Support System for Generation of Holistic Renovation Scenarios—Cases of Energy Consumption, Investment Cost, and Thermal Indoor Comfort

Abstract

Future building renovations must rely on a holistic perspective in relation to sustainability. This paper presents a Decision Support Systems (DSS) that can be used by architects and engineering consultants to generate and evaluate the sustainability of renovation scenarios in a holistic manner during the early design stage of renovation projects. Firstly, this paper discusses both the notion of a sustainable renovation, together with various renovation approaches, towards the appreciation of the developing DSS for the generation of holistic scenarios. Secondly, it provides details about the mechanism and types of Multiple Criteria Decision Making methods to be exploited in the main body of the DSS. As such, a hybrid approach including a search algorithm with the Genetic Algorithm is used to combine and develop countless optimal scenarios. The performance of the generated scenarios is simulated and evaluated in terms criteria for Energy Consumption, Investment Cost, and Thermal Indoor Comfort. The trade-off between the criteria is addressed using the Pareto-front approach, and subsequently, the optimal scenarios are determined and selected using MCDM-based rating methods. The outcome is verified discussing a case study about an actual [recently] renovated dwelling and the top ranked generated scenarios using the DSS in this paper.

Renovation Construction Process Scheduling for Long-Term Performance of Buildings: An Application Case of University Campus

“…The optimization algorithms were also employed for the study issues of building renovation. For example, Kamari et al [22] present a decision support systems (DSS) by applying the genetic algorithm to generate and evaluate renovation scenarios especially for the early design stage of renovation projects. These scenarios are afterward selected in fitness using a crossover scheme, in which a mutation can be applied by applying random gene or renovation alternatives [22].…”

“…For example, Kamari et al [22] present a decision support systems (DSS) by applying the genetic algorithm to generate and evaluate renovation scenarios especially for the early design stage of renovation projects. These scenarios are afterward selected in fitness using a crossover scheme, in which a mutation can be applied by applying random gene or renovation alternatives [22]. The genetic algorithm process begins by determining the initial population, which consists of several chromosomes arranged by genes that are representations of solution candidates.…”

“…Hence, the planner should provide several renovation scenarios as strategic management tools in building maintenance. To obtain renovation scenarios from a wider perspective, previous research by Kamari et al [22,29] identified a total of 139 renovation alternatives and classified them into 26 approaches as shown in Table 4. In this case, these approaches [22,29] will be divided into three renovation levels [13] corresponding to the cost (see Table 5).…”

“…To obtain renovation scenarios from a wider perspective, previous research by Kamari et al [22,29] identified a total of 139 renovation alternatives and classified them into 26 approaches as shown in Table 4. In this case, these approaches [22,29] will be divided into three renovation levels [13] corresponding to the cost (see Table 5). Furthermore, the building planner can judge which level is suitable for certain building renovation project.…”

Artificial Neural Network-Based Decision Support System for Development of an Energy-Efficient Built Environment

“…A summary of the state-of-the-art proposed decision support systems is presented in Table 1. It is observed that decision support systems perform many energy-related functions; namely, selection of scenarios for buildings' retrofit in terms of energy savings and carbon emissions [18,19,21,[23][24][25]27,28,42], energy efficiency optimization of street lighting [20], assessment of energy performance of buildings [22,31], selection of energy management actions [29] and low-cost green building materials and components [30], spatial environmental assessment [32,33], energy demand prediction and optimization [34][35][36][37], assessment of energy generation technologies [38] and construction projects [40], designing of the most efficient versions of a passive house [39] and intelligent built environment [41].…”