A Sequential Multi-Staged Approach for Developing Digital One-Stop Shops to Support Energy Renovations of Residential Buildings

Abstract

Buildings account for 40% of the European Union’s energy consumption. Deep energy renovation of residential buildings is key for decarbonization and energy poverty alleviation. However, renovation is occurring at far below the needed pace and depth. In this context, building renovation one-stop shops, which bring all project phases under one roof and provide advice, support, and finance to households, are highlighted as a promising solution. Nevertheless, this model is still absent or under-developed in most European countries and remains understudied in the scientific literature. Therefore, the present research goals are as follows: (i) to provide a critical review of emerging one-stop shop models; (ii) to streamline the deployment of building renovation digital one-stop shops by piloting a sequential multi-staged approach for Portuguese households and proposing it for replication elsewhere; and (iii) to compare case-study insights with other one-stop shops and discuss the notion in the context of the European Renovation Wave. In total, for the Portuguese case-study, five steps were conducted. The first three—stakeholder mapping, expert interviews, and customer journey—aimed to gather intel on the local energy renovation market. The results from these stages informed the design of the platform (fourth step). Finally, a post-launch market consultation survey gathered user feedback (fifth step). Insights from this study suggest that digital one-stop shops, while providing a helpful tool to close information gaps and activate specific audiences, may be insufficient on their own. As such, a more comprehensive set of instruments supporting households is needed to accelerate building renovation.

Combining CAD Modeling and Simulation of Energy Performance Data for the Retrofit of Public Buildings

“…Thus, the optimization procedure we propose here consists first in selecting, simulating and evaluating the most efficient interventions in terms of energy costs, while minimizing the optimization procedure we propose here consists first in selecting, simulating and evaluating the most efficient interventions in terms of energy costs, while minimizing the intrusion and interruption of the building operation. According to numerous in-depth studies on the key phases of building renovation [8][9][10], and as shown in Figure 1, there are five stages of intervention in the renovation of an existing building. The first phase consists of a preliminary approach to the project.…”

“…The third phase is the evaluation of the potential of each intervention, while the fourth phase is dedicated to the implementation of the renovation solutions, which are finally validated by the results in the fifth phase [11]. Numerous studies already provide tools and context for reducing energy consumption around the world [3][4][5][6][7][8][9][10][11][12][13][14]. Retrofitting existing structures can significantly reduce energy consumption and greenhouse gas emissions worldwide.…”

“…Therefore, they propose a machine learning approach to indicate optional interventions. Numerous studies already provide tools and context for reducing energy consumption around the world [3][4][5][6][7][8][9][10][11][12][13][14]. Retrofitting existing structures can significantly reduce energy consumption and greenhouse gas emissions worldwide.…”