Big data mining for the estimation of hourly rooftop photovoltaic potential and its uncertainty

Alina Walch, Roberto Castello, Nahid Mohajeri and Jean-Louis Scartezzini

Applied Energy, 2020, vol. 262, issue C, No S0306261919320914

Abstract: The large-scale deployment of photovoltaics (PV) on building rooftops can play a significant role in the transition to a low-carbon energy system. To date, the lack of high-resolution building and environmental data and the large uncertainties related to existing processing methods impede the accurate estimation of large-scale rooftop PV potentials. To address this gap, we developed a methodology that combines Machine Learning algorithms, Geographic Information Systems and physical models to estimate the technical PV potential for individual roof surfaces at hourly temporal resolution. We further estimate the uncertainties related to each step of the potential assessment and combine them to quantify the uncertainty on the final PV potential. The methodology is applied to 9.6 million rooftops in Switzerland and can be transferred to any large region or country with sufficient available data. Our results suggest that 55% of the total Swiss roof surface is available for the installation of PV panels, yielding an annual technical rooftop PV potential of 24±9TWh. This could meet more than 40% of Switzerland’s current annual electricity demand. The presented method for an hourly rooftop PV potential and uncertainty estimation can be applied to the large-scale assessment of future energy systems with decentralised electricity grids. The results can be used to propose effective policies for the integration of rooftop photovoltaics in the built environment.

Keywords: Rooftop photovoltaic potential; Spatio-temporal modelling; Big data mining; Uncertainty estimation; Machine Learning (search for similar items in EconPapers)
Date: 2020
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Citations: View citations in EconPapers (36)

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DOI: 10.1016/j.apenergy.2019.114404

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