The provided dataset contains data for the PV potentials on building rooftops, evaluated for 9.6 M roof surfaces in Switzerland in an hourly temporal resolution. The methodology of the generation of the dataset is described in: Walch, Alina, Roberto Castello, Nahid Mohajeri, and Jean-Louis Scartezzini. “Big Data Mining for the Estimation of Hourly Rooftop Photovoltaic Potential and Its Uncertainty.” Applied Energy 262 (March 15, 2020): 114404. In the process of generating this dataset, the following aspects were included: Meteorological conditions in Switzerland (solar radiation, temperature, snow cover) Local shading and sky coverage from surrounding buildings and trees (based on a Digital Surface Model) Obstruction of roof surface due to roof superstructures such as dormers and chimneys (estimated based on data from the canton of Geneva) The panel and inverter efficiencies, as a function of the solar radiation and temperature Several aspects were estimated and hence include some uncertainty, due to the input datasets and the modelling methodology. For details on the sources of uncertainty and the limitations, please refer to the referenced article. Estimates for these uncertainties are provided alongside the variables. A description of the metadata is provided in the document rooftop_PV_CH_metadata_V1.pdf. Data description: The rooftop PV potential data has been computed at monthly-mean-hourly temporal resolution (i.e. 24 hours for each of the 12 months) for each individual roof surface, based on a national roof surface dataset created by SwissTopo (see https://www.uvek-gis.admin.ch/BFE/sonnendach/). The data given in this dataset is aggregated, in order to make the data easier to use for studies inside as well as outside Switzerland, to reduce the file size and to respect license agreements. Two types of aggregation are provided: Aggregation per building, using the object ID of the SwissBuildings3D cadastre as identifier. Aggregation per roof type, separating between 4 categories: Tilt angle, aspect angle, roof area, altitude If a different type of aggregation or the data per individual roof surface is required, please do not hesitate to get in touch with the authors directly.

{"references": ["Walch, Alina, Roberto Castello, Nahid Mohajeri, and Jean-Louis Scartezzini. \"Big Data Mining for the Estimation of Hourly Rooftop Photovoltaic Potential and Its Uncertainty.\" Applied Energy 262 (March 15, 2020): 114404."]}

This research has been financed by the Swiss National Science Foundation (SNSF) under the National Research Program 75 (Big Data) for the HyEnergy project.