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This study presents a Geospatial Artificial Intelligence (GeoAI) framework to enhance solar radiation predictions under climate change. Using Bayesian Regularised Artificial Neural Networks (BRANNs), the framework downscales General Circulation Model (GCM) outputs to high-resolution data over the island of Ireland, addressing GCM limitations in capturing localised solar patterns. Key atmospheric and surface variables, including cloud cover, relative humidity, pressure gradients, and topography, are incorporated to improve projection accuracy. The analysis employs two climate change scenarios, SSP2–4.5 and SSP5–8.5 (Shared Socioeconomic Pathway), for the period 2000–2050. A dedicated post-processing toolbox is developed and applied to the case study, demonstrating its capability to refine GCM simulations for both current and future conditions. The BRANN models achieved high accuracy during the baseline period (2000–2014). Projections indicate a decrease in monthly mean solar radiation of 2.01 % under SSP2–4.5 and 1.61 % under SSP5–8.5 by 2050, corresponding to mean monthly values of 9.87 × 10⁶ J.m⁻² and 9.91 × 10⁶ J.m⁻², respectively, with higher values in southeastern Ireland. The framework supports optimal solar panel site selection and provides long-term performance projections, enabling climate scientists and policymakers to make informed decisions for sustainable energy planning. © © 2026. Published by Elsevier Ltd.

The emergence of Geographic Information Systems (GIS) web platforms provides unique opportunities for human societies. GIS web platform technology has a two-way function, utilizing data obtained from physical and virtual environments to create harmony between the two. This review and case study paper examines the recent development and implementation of GIS web technology, focusing on urban areas and city scales. Firstly, this article reviews technology trends in online GIS web platform tools by identifying key features and applications, including their role in decision-making support. Secondly, it describes the GIS-Web platform, data sharing framework, the end-user services integrated, case study and project overview, platform digitalization as next generation. Thirdly, a new energy data portal called “NRGYHUB” is introduced for municipal urban areas in Västerås City, Sweden. This GIS portal platform provides hourly data from thousands of energy meters, collected from electrical and heating energy networks to develop, maintain, and showcase a collection of city-wide GIS tools that assist in creating, implementing, and managing innovative services for urban planning in Västerås City. Additionally, this paper presents a Geospatial Artificial Intelligence (GeoAI) approach for generating wind power projection maps using Machine Learning (ML) models which collectively aim to provide insightful wind power forecasts under the effects of climate change focusing on Västerås. Time series data for each grid cell served as inputs for the Radial Basis Functions (RBF) models, incorporating wind speed projections from the Coupled Model Intercomparison Project Phase 6 (CMIP6) along with other influential variables, such as pressure gradient, temperature gradient, humidity, and Digital Elevation Model (DEM) data. The performance of the ML models was rigorously evaluated using multiple statistical metrics, including bias, Mean Absolute Error (MAE), Correlation Coefficient (Corr), Mean Error (ME), and Root Mean Square Error (RMSE). These metrics enabled a thorough assessment of the model's accuracy and bias-correction capabilities, ultimately improving the reliability of wind speed projections for the study area.

To ensure energy efficiency and resilience, building prosumers must maintain energy supply during disruptions while minimizing environmental impact. This study examines the resilience of three solar-applied building prosumers: a single-family home, a multi-family building, and a commercial property in Västerås, Sweden. It evaluates (1) the impact of PV and battery integration and load prioritization on outage resilience, (2) seasonal variations in resilience for individual buildings in high-latitude region, and (3) district-level performance to assess aggregation effects on system resilience. One-day power outages are simulated for each season, with loads classified as critical or non-critical. System performance is measured using load coverage rate, which quantifies the robustness of building systems in withstanding disruptions. The results show that load prioritization significantly enhances resilience, particularly for multi-family buildings during heating season, ensuring occupant habitability and survivability. The role of PV and battery sizing in improving resilience varies by season, with battery capacity playing a key role during winter outages. The benefits of energy sharing at the district level are limited, but can be advantageous for multi-family buildings with a lower PV-to-load ratio. This study provides insights into the role of PV and storage systems as resilience strategies for buildings and districts in cold-climate regions.

Electric trucks (ETs) offer a potential route towards decarbonizing the transportation sector and reducing transportation-related greenhouse gas emissions. Nonetheless, deploying many ETs brings about operational difficulties regarding optimizing charging strategies which mitigate the cost while guaranteeing stability on grid level. In this paper, we propose a new application of the Lévy Arithmetic Optimization (LAO) algorithm to address the challenges of ET charging schedule optimization. Incorporating high-resolution, real-time load data from Sweden’s electricity grid, the proposed framework dynamically adjusts ET charging power based on grid capacity and electricity prices. The results show that for a fleet of 100 ETs the optimization can give 15.53% savings in total charging costs compared with a baseline scenario. In addition, the method improves state-of-charge (SOC) management and lightens load on the grid, giving scalable, cost-effective and grid-friendly electrification of heavy-duty transportation.

Renewable Energy Communities (RECs) are evolving as key enablers of climate action and renewable energy integration. However, they face technical and economic challenges, particularly in energy allocation, pricing structures and stakeholder expertise. Existing studies primarily focus on multi-owner RECs, overlooking single-owner models - increasingly relevant in urban settings. This study analyzes a real-world single-owner REC, comparing hourly- and monthly-based pricing models and four energy allocation strategies using techno-economic metrics. Results show that hourly-based pricing reduces costs but lowers revenues. Furthermore, allocation strategies minimally impact technical performance in such REC setting. While the differences between the strategies themselves are negligible from the landlord’s perspective, the impacts on tenants in terms of cost distribution and fairness could vary, requiring further investigation.

The global transition to sustainable energy has renewed interest in hydrogen as a clean and versatile energy carrier.Its versatility lies in its ability to serve multiple roles, from electricity generation and industrial heating to long-duration energy storage and mobility applications.This paper evaluates the techno-economic feasibility of a hydrogen-based energy system designed to provide backup power for a newly developing industrial area. Two system configurations, i.e., compressed and liquid hydrogen, are assessed in terms of their performance and cost-effectiveness. The model captures the dynamic operation of key components, including electrolyzers, storage systems, compressors, liquefiers, gasifiers, fuel cells, batteries, and heat exchangers, using high-resolution, yearlong data on solar irradiance, electricity prices, and industrial load patterns in Eskilstuna, Sweden. A structured sensitivity analysis examines how design decisions affect economic outcomes such as net profit and the Levelized Cost of Hydrogen (LCOH).Results show that compressed hydrogen achieves an LCOH of 81 SEK/kg and a net loss of –1.985MSEK, compared to liquid hydrogen with 90S EK/kg and –2.817 MSEK. However, under current market conditions and cost structures, neither system achieves profitability.The findings provide actionable guidance for industrial stakeholders and suggest avenues for future cost reduction, value stacking, and policy support.

The global transition to sustainable energy has renewed interest in hydrogen as a clean and versatile energy carrier. Its versatility lies in its ability to serve multiple roles, from electricity generation and industrial heating to long-duration energy storage and mobility applications. This paper evaluates the techno-economic feasibility of a hydrogen-based energy system designed to provide backup power for a newly developing industrial area. Two system configurations, i.e., compressed and liquid hydrogen, are assessed in terms of their performance and cost-effectiveness. The model captures the dynamic operation of key components, including electrolyzers, storage systems, compressors, liquefiers, gasifiers, fuel cells, batteries, and heat exchangers, using high-resolution, yearlong data on solar irradiance, electricity prices, and industrial load patterns in Eskilstuna, Sweden. A structured sensitivity analysis examines how design decisions affect economic outcomes such as net profit and the Levelized Cost of Hydrogen (LCOH). Results show that compressed hydrogen achieves an LCOH of 81 SEK/kg and a net loss of –1.985 MSEK, compared to liquid hydrogen with 90 SEK/kg and –2.817 MSEK. However, under current market conditions and cost structures, neither system achieves profitability. The findings provide actionable guidance for industrial stakeholders and suggest avenues for future cost reduction, value stacking, and policy support.

This study conducts a techno-environmental assessment of a fully electrified rural single-family house in Västerås, Sweden, functioning as a resilient, sustainable prosumer. It evaluates: (i) grid independence under normal conditions; (ii) thermal resilience during power outages with various enhancement strategies; and (iii) operational COâ‚‚ emission reductions versus combustion-based heating. Using IDA ICE simulations, the building’s energy performance is analyzed with integrated renewable energy (RE) systems: PV, solar thermal, batteries, thermal storage, electric heater, and heat pump. Key performance indicators include self-sufficiency rate (SSR), operational COâ‚‚ emissions equivalent, and thermal resilience metrics. Results show that combining RE technologies significantly improves year-round reliability and emissions reduction. PV-only systems perform well during long outages, while battery-only setups suffice for short ones. Even basic electric heaters maintain habitability during 3-day outages. The findings offer practical insights into enhancing resilience and sustainability in rural prosumer buildings through distributed RE systems.

Proper resilience metrics (RMs) for energy systems are necessary to be identified, evaluated, and implemented to improve energy system resilience. The proposed framework aims to be implemented as a tool, in the form of an energy resilience matrix, for energy system evaluation. The application of the engineering and infrastructure part of the framework was demonstrated in evaluation of a sport activity area located in Sweden. The results show potential to improve the resilience by installing solar cells and increase battery storage capacity in the area, connect with neighbouring area and utilize vehicle to grid.

The electrification of vehicles has become a critical means to achieve climate-neutral transportation. As more electric vehicles (EV) are adopted, an increasing number of lithiumion batteries will be utilized, inevitably experiencing capacity degradation over time. Retaining the value of these retired batteries through remanufacturing, reusing, and repurposing to create a second life holds significant environmental and economic benefits. However, many companies within the battery ecosystem struggle to capitalize on this opportunity due to a lack of business insight and suitable business models tailored to their operational contexts.

The ReCreate (Second Life Management of Electric Vehicle Batteries) research project was initiated to address these industrial needs through close collaboration with selected companies in the battery ecosystem. The project aims to define appropriate circular business models, methods, and processes to guide battery ecosystem actors in developing and implementing electric vehicle battery second life solutions, thereby advancing circularity around batteries and climate-neutral objectives. 

This handbook represents the culmination of three years of research within the ReCreate project. Its purpose is to present a simplified and practical overview of project outcomes across a series of key chapters. Comprising six chapters, the handbook will begin by discussing barriers and enablers, followed by circular business models and battery ecosystem management. It will then delve into design principles and performance monitoring guidelines, concluding with an integrated framework for second life and circular solutions for EV batteries. 

Each chapter briefly presents the main findings of the theme and concludes with discussion questions. The discussion questions include suggestions for relevant templates for workshops, and all templates are conveniently provided in the appendix for practical application. These templates serve as boundary objects, offering a starting point for internal and external cross-functional and cross-organizational dialogues within the electric vehicle battery ecosystem. They facilitate discussions and collaborations among various stakeholders, fostering alignment and synergy in developing circular business models for the second life of EV batteries.  

By facilitating reflection on current business strategies, needs, and pain points, the handbook aims to aid in the definition of future second life business strategies. We anticipate that this handbook will serve as a valuable resource for actors within the EV battery ecosystem, supporting their journey towards climate-neutral transportation.