The field of energy forecasting has attracted many researchers from different fields (e.g., meteorology, data sciences, mechanical or electrical engineering) over the last decade. Solar forecasting is a fast-growing subdomain of energy forecasting. Despite several previous attempts, the methods and measures used for verification of deterministic (also known as single-valued or point) solar forecasts are still far from being standardized, making forecast analysis and comparison difficult. To analyze and compare solar forecasts, the well-established Murphy-Winkler framework for distribution-oriented forecast verification is recommended as a standard practice. This framework examines aspects of forecast quality, such as reliability, resolution, association, or discrimination, and analyzes the joint distribution of forecasts and observations, which contains all time-independent information relevant to verification. To verify forecasts, one can use any graphical display or mathematical/statistical measure to provide insights and summarize the aspects of forecast quality. The majority of graphical methods and accuracy measures known to solar forecasters are specific methods under this general framework. Additionally, measuring the overall skillfulness of forecasters is also of general interest. The use of the root mean square error (RMSE) skill score based on the optimal convex combination of climatology and persistence methods is highly recommended. By standardizing the accuracy measure and reference forecasting method, the RMSE skill score allows-with appropriate caveats-comparison of forecasts made using different models, across different locations and time periods.

The local generation of renewable electricity through roof-mounted photovoltaic (PV) systems on buildings in urban areas provides huge potentials for the mitigation of greenhouse gas emissions. This contribution presents a new method to provide local decision makers with tools to assess the remaining PV potential within their respective communities. It allows highly detailed analyses without having to rely on 3D city models, which are often not available. This is achieved by a combination of publicly available geographical building data and aerial images that are analyzed using image recognition and machine learning approaches. The method also employs sophisticated algorithms for irradiance simulation and power generation that exhibit a higher accuracy than most existing PV potential studies. The method is demonstrated with an application to the city of Freiburg, for which a technical PV electricity generation potential of about 524 GWh/a is identified. A validation with a 3D city model shows that the correct roof azimuth can be determined with an accuracy of about 70% and existing solar installations can be detected with an accuracy of about 90%. This demonstrates that the method can be employed for spatially and temporally detailed PV potential assessments in arbitrary urban areas when only public geographical building data is available instead of exact 3D city model data. Future work will focus on methodological improvements as well as on the integration of the method within an urban energy system modeling framework

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Knowledge of PV system characteristics is needed in different regional PV modelling approaches. It is the aim of this paper to provide that knowledge by a twofold method that focuses on (1) metadata (tilt and azimuth of modules, installed capacity and specific annual yield) as well as (2) the impact of shading. Metadata from 2,802,797 PV systems located in Europe, USA, Japan and Australia, representing a total capacity of 59 GWp (14.8% of installed capacity worldwide), is analysed. Visually striking interdependencies of the installed capacity and the geographic location to the other parameters tilt, azimuth and specific annual yield motivated a clustering on a country level and between systems sizes. For an eased future utilisation of the analysed metadata, each parameter in a cluster was approximated by a distribution function. Results show strong characteristics unique to each cluster, however, there are some commonalities across all clusters. Mean tilt values were reported in a range between 16.1 • (Australia) and 35.6 • (Belgium), average specific annual yield values occur between 786 kWh/kWp (Denmark) and 1,426 kWh/kWp (USA South). The region with smallest median capacity was the UK (2.94 kWp) and the largest was Germany (8.96 kWp). Almost all countries had a mean azimuth angle facing the equator. PV system shading was considered by deriving viewsheds for ≈ 48,000 buildings in Uppsala, Sweden (all ranges of solar angles were explored). From these viewsheds, two empirical equations were derived related to irradiance losses on roofs due to shading. The first expresses the loss of beam irradiance as a function of the solar elevation angle. The second determines the view factor as a function of the roof tilt including the impact from shading and can be used to estimate the losses of diffuse and reflected irradiance.

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