A toolkit for multi-scale mapping of the solar energy-generation potential of buildings in urban environments under uncertainty. (October 2018)

• Record Type: Journal Article
• Authors: Peronato, G.; Rastogi, P.; Rey, E.; Andersen, M.
• Is Part Of: Solar energy. Volume 173(2018)
• Page Start: 861
• Page End: 874
• Publication Date: 2018-10
• Subjects: Urban solar potential -- Spatial decision-support systems -- 3D city model; Solar energy -- Periodicals; Solar engines -- Periodicals; 621.47
• DOI: 10.1016/j.solener.2018.08.017
• Languages: English
• ISSNs: 0038-092X

Abstract

Highlights: We assess the potential for building PV systems in urban areas. We use pairwise comparisons to calculate a solar score and rank spatial locations. The score is computed for some relevant performance indicators and spatial aggregates. The method accounts for crucial environmental uncertainties (weather and vegetation). A 3D-mapping tool overlaying the spatial aggregates supports decision-making. Abstract: Many municipalities and public authorities have supported the creation of solar cadastres to map the solar energy-generation potential of existing buildings. Despite advancements in modelling solar potential, most of these tools provide simple evaluations based on benchmarks, neglecting the effect of uncertain environmental conditions and that of the spatial aggregation of multiple buildings. We argue that including such information in the evaluation process can lead to more robust planning decisions and a fairer allocation of public subsidies. To this end, this paper presents a novel method to incorporate uncertainty in the evaluation of the solar electricity generation potential of existing buildings using a multi-scale approach. It also presents a technique to visualise the results through their integration in a 3D-mapping environment and the use of false-colour overlays at different scales. Using multiple simulation scenarios, the method is able to provide information about confidence intervals of summary statistics of production due to variation in two typical uncertain factors: vegetation and weather. The uncertainty in production introduced by these factors is taken into account through pairwise comparisons of nominal values of indicators, calculating a comprehensive ranking of the energy potential of different spatial locations and a corresponding solar score. The analysis is run at different scales, using space- and time-aggregated results, to provide results relevant to decision-makers.