Overview

This book constitutes the proceedings of the First Nordic Energy Informatics Academy Conference, Nordic EIA 2025, which took place in Stockholm, Sweden, in August 2025. The 43 full papers and 8 short papers accepted were carefully reviewed and selected from 65 submissions. They were organized in topical sections as follows:  Part I: Energy Forecasting and Intelligent Control Systems; District HEating, Thermal Systems, and Retrofit Strategies; Building Simulation, Urban Energy, and Environmental Sensing; Industrial Process Efficiency and Biomass Utlilzation; Energy Informatics for Electric Vehicles and Mobility Systems; Multi-Agent Systems and Local Market Coordination;  Part II:  Policy, Metrics, and Infrastructure Performance; Smart Buliding Systems and Semantic Data Integration; Prosumer Optimization and Energy Storage in Local Energy Communities, Grid-Oriented AI, Simulation, and Resilience; Non-Intrusive Load Monitoring and Data Competitions.  

Full Product Details

Author:   Ivo Martinac ,  Bo Nørregaard Jørgensen ,  Zheng Grace Ma ,  Rúnar Unnþórsson
Publisher:   Springer Nature Switzerland AG
Imprint:   Springer Nature Switzerland AG
ISBN:  

9783032030979

ISBN 10:   3032030978
Pages:   409
Publication Date:   01 November 2025
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Not yet available  
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Table of Contents

.- Policy, Metrics, and Infrastructure Performance. .- Proper definitions of micro grid metrics are needed! - a generalizable framework. .- Solar-Geothermal Power ""HGS-ORC"" System for Energy Co-generation: En ergy, Economic and Environmental analysis: Algerian case. .- Towards the Integration of Data Space Technology in Hydrogen Research Workflows. .- Comparison of Outages Trends and Statistics in Nordic Countries Across Distribution Networks and their Impacts. .- Managing Risk in Distribution Systems with Solar Generation: A Case Study Using the MATPOWER Optimal Scheduling Tool. .- Smart Energy Management System With Individual Load Monitoring. .- Smart Building Systems and Semantic Data Integration. .- Towards a Taxonomy for Application of Machine Learning and Artificial Intelligence in Building and District Energy Management Systems. .- A Dynamic Semantic Data Modeling Approach: Application to Flexible HVAC Zones. .- Leveraging Generative AI and semantic data for improved operation of a real-life building. .- Development of an LSTM-Based Model for High-Resolution Downsampling and Reconstruction of HVAC Chiller Flow Data. .- Data-Driven Optimal Air-Balancing Control for Multizone Ventilation Systems with Design-to-Operation Adaptation. .- Prosumer Optimization and Energy Storage in Local Energy Communities. .- Evaluating the Potential for Developing Local Energy Communities in Sweden: Case Studies at Jättesten and Chalmers Campus. .- Data-driven Correlated Uncertainty Sets for PV Generation and Electricity Demand. .- Scheduling Heat Pumps for Balancing Thermal Storage and Grid Export. .- Battery Energy Storage Integration with BIPV Systems: A Multi-Scenario Economic Analysis and Optimization. .- Grid-Oriented AI, Simulation, and Resilience. .- A data-driven analysis of unscheduled flows in the European power system. .- Green Hydrogen under Uncertainty: Evaluating Power-to-X Strategies Using Agent-Based Simulation and Multi-Criteria Decision Framework. .- Synthesizing Fault Localization Datasets. .- Machine Learning-Based Cyberattack Detection in Power Data. .- Optimization of Second-Life Battery Energy Storage System in Buildings with Photovoltaic Panels: A Norwegian Case Study. .- Non-Intrusive Load Monitoring and Data Competitions. .- ADRENALIN: Energy Data Preparation and Validation for HVAC Load Disaggregation in Commercial Buildings. .-  Advancing Non-Intrusive Load Monitoring: Insights from the Winning Algorithms in the ADRENALIN 2024 Load Disaggregation Competition. .- Comparison of Three Algorithms for Low-Frequency Temperature Dependent Load Disaggregation in Buildings Without Submetering. .- Lessons Learned from the ADRENALIN Load Disaggregation Challenge. .- Business Model Innovation in Data Competitions: Insights from the 2024 ADRENALIN Load Disaggregation Challenge.

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