To better forecast solar variability, spatio-temporal methods exploit spatially distributed solar time series, seeking to improve forecasting accuracy by including neighboring solar information. This review work is, to the authors’ understanding, the first to offer a compendium of references published since 2011 on such approaches for global horizontal irradiance and photovoltaic generation. The identified bibliography was categorized according to different parameters (method, data sources, baselines, performance metrics, forecasting horizon), and associated statistics were explored. Lastly, general findings are outlined, and suggestions for future research are provided based on the identification of less explored methods and data sources.

The recently approved regulation on Energy Communities in Europe is paving the way for new collective forms of energy consumption and production, mainly based on photovoltaics. However, energy modeling approaches that can adequately evaluate the impact of these new regulations on energy community configurations are still lacking, particularly with regards to the grid tariffs imposed on collective systems. Thus, the present work models three different energy community configurations sustained on collective photovoltaics self-consumption for a small city in southern Portugal. This energy community, which integrates the city consumers and a local winery, was modeled using the Python-based Calliope framework. Using real electricity demand data from power transformers and an actual winery, the techno-economic feasibility of each configuration was assessed. Results show that all collective arrangements can promote a higher penetration of photovoltaic capacity (up to 23%) and a modest reduction in the overall cost of electricity (up to 8%). However, there are clear trade-offs between the different pathways: more centralized configurations have 53% lower installation costs but are more sensitive to grid use costs (which can represent up to 74% of the total system costs). Moreover, key actor’s individual self-consumption rate may decrease by 10% in order to benefit the energy community as a whole.

The EU Copernicus Climate Change Service (C3S) has produced an operational climate service, called C3S Energy, designed to enable the energy industry and policymakers to assess the impacts of climate variability and climate change on the energy sector in Europe. The C3S Energy service covers different time horizons, for the past 40 years and the future. It provides time series of electricity demand and supply from wind, solar photovoltaic and hydropower, and can be used for recent trends analysis, seasonal outlooks or the assessment of climate change impacts on energy mixes in the long term. This article introduces this service and the resulting dataset, with a focus on the design and validation of the energy conversion models, based on ENTSO‐E energy data and the ERA5 climate reanalysis. Flexibility and coherence across all countries have been preferred upon models' accuracy. However, the comparison with ENTSO‐E data shows that the models provide plausible energy indicators and, in particular, allow comparing climate variability effects on power demand and generation in a harmonized manner all over Europe.

Probabilistic solar forecasting is an issue of growing relevance for the integration of photovoltaic (PV) energy. However, for short-term applications, estimating the forecast uncertainty is challenging and usually delegated to statistical models. To address this limitation, the present work proposes an approach which combines physical and statistical foundations and leverages on satellite-derived clear-sky index ($k_c$) and cloud motion vectors (CMV), both traditionally used for deterministic forecasting. The forecast uncertainty is estimated by using the CMV in a different way than the one generally used by standard CMV-based forecasting approach and by implementing an ensemble approach based on a Gaussian noise-adding step to both the $k_c$ and the CMV estimations. Using 15-min average ground-measured Global Horizontal Irradiance (GHI) data for two locations in France as reference, the proposed model shows to largely surpass the baseline probabilistic forecast Complete History Persistence Ensemble (CH-PeEn), reducing the Continuous Ranked Probability Score (CRPS) between 37% and 62%, depending on the forecast horizon. Results also show that this is mainly driven by improving the model’s sharpness, which was measured using the Prediction Interval Normalized Average Width (PINAW) metric.

This study investigated a method for modelling a landscape of opportunities for penetrative passing completed on the ground by ball carriers in association football. Analysis of video footage of competitive, professional football performance was undertaken, identifying a sample (n = 20) of attacking sub-phases of gameplay which ended in a penetrative pass being made between defenders to a receiver. Players’ relative co-positioning during performance was modelled using bi-dimensional x and y coordinates of each player recorded at 25 fps. Data on player movements during competitive interactions were captured using an automatic video tracking system, recording player co-locations emerging over time, as well as current and estimated running velocities. Results revealed that the half spaces between the midfield and both sidelines were the key locations on field providing most affordances for penetrating passes in the competitive performance sample analysed. Due to the dynamics of players’ co-adaptive performance behaviours, it was expected that opportunities for penetrative passing by ball carriers would not display a homogeneous space-time spread across the entire field. Results agreed with these expectations, showing how a landscape of opportunities for penetrative passing might be specified by information emerging from continuous player interactions in competitive performance.