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Accurate photovoltaic (PV) power forecast is crucial for carbon neutrality. Current researches on PV power forecast mainly focus on using temporal information from single PV station, and the spatial information in multiple PV power stations are often neglected. To address this problem, this paper introduces Moran index to verify the spatial autocorrelation of PV power for the first time, uses Granger causality test and transfer entropy to reveal the spatial information gain in PV power forecast for the first time, and proposes a novel spatial–temporal probabilistic PV forecast method using deep Graph Gated Recurrent Unit (GraphGRU) network-based spatial–temporal multi-task learning and Kernel Density Estimation (KDE). Deep GraphGRU combines the advantages of Graph Convolutional Network (GCN) in spatial feature extraction and the advantages of Gated Recurrent Unit (GRU) network in temporal feature extraction, and thus has strong ability to extract spatial–temporal information in historical data of multiple different PV power stations. Through GraphGRU, temporal dependency information extracted from historical data of multiple PV stations can promote each other to improve the forecast accuracy of each PV stations. KDE is used for estimating the joint probabilistic density function and giving the spatial–temporal probabilistic confidence interval of PV power. Experiments were performed in the five-year actual PV power data from 11 provinces of Belgium and the three-year solar irradiation data from 12 places in China to verify the superiorities of the proposed method. Comparison with conventional spatial–temporal and temporal forecast methods show that the proposed GraphGRU-based spatial–temporal forecast method can extract the spatial–temporal information from multiple PV power stations well and significantly outperform conventional temporal forecast methods. [ABSTRACT FROM AUTHOR]

Due to the imperative nature of addressing climate change, the energy transition is currently underway, prompting the recognition of its urgency. Energy system optimisation models have emerged as crucial tools to assist policymakers in formulating laws and regulations that facilitate the transition towards carbon neutrality. While numerous models have been developed to explore various scenarios and define long-term objectives, only a few models focus on optimising the specific pathway to achieve these objectives. Many existing models lack the necessary time resolution to capture the integration of intermittent renewable energies; or are not open-source, creating a challenge in terms of transparency and reproducibility. This paper introduces EnergyScope Pathway, an open-source and documented model that addresses these limitations. It specifically optimises investment strategies for the whole-energy system over a 30-year period, or more, and optimising its hourly operation. This approach allows for a comprehensive evaluation of the effective integration of intermittent renewable energy sources. The model has a concise and efficient formulation, enabling its execution on personal laptops within approximately 15 min. By applying the model to the case study of Belgium, which presents challenges due to limited potential for renewable energy, we illustrate the importance of four pillars: energy efficiency, renewable energies, sector coupling, electrification and imports. The result pave the way to a new and incremental tool to support decision makers. In comparison to non open-source models, we verified the model's results with similar studies and found consistency in terms of technico-economic estimations. • Linear formulation of an optimisation model to represent a whole energy system transition pathway with an hourly resolution. • Short computational time, 15 min on a personal laptop • Quantify the pillars of the transition: efficiency, renewable energies and sector coupling. • Offers a new and incremental tool to support decision-makers in the energy transition. • Comparison of different approach to represent the transition. [ABSTRACT FROM AUTHOR]

[Display omitted] • Development of two benchmark models for post-world war II residential buildings. • Average energy use intensity per household (archetypes A and B) were 166 and 155 kWh/m2/year. • Models validated with four-year monitoring data on energy consumption. • Households are dominated by senior adults and their occupancy profiles are presented. • Findings on energy needs and use intensity are useful in temperate and continental climates. In the context of the European carbon neutrality targets, building benchmarks are a key issue for the renovation of existing buildings. Although there are various benchmark methods for energy efficiency characterization, their application to the residential sector is still limited. This paper developed two building simulation models for post-world war II houses in Belgium based on data from post-occupancy measurements and field survey campaigns. The study reports the energy characteristics and occupancy profiled of detached single-family houses. An analysis of energy consumption (electricity and natural gas) and a walkthrough survey were conducted between 2016 and 2019. The benchmark model's validity has been further checked against public statistics and verified through model calibration and monthly energy bill comparison. Two reference models representing 633.702 post-WWII single-family houses in Belgium were created and validated. The first archetype has an average energy use intensity of 166 kWh/m2/year and represents detached single-family houses built between 1945 and 1969. The second archetype has an average energy use intensity of 155 kWh/m2/year and represents detached single-family houses built between 1970 and 1990. The paper provides a timely opportunity to evaluate the real performance of post-world war II most common archetypes concerning design assumptions and how building professionals can turn the energy performance gap challenge to their advantage. The findings on energy needs and intensity are useful for creating future renovation scenarios for similar archetypes in Western European countries. [ABSTRACT FROM AUTHOR]