PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
September 20, 2025˜The œinternational archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences4 citationsOpen Access

A Data-Driven Urban Digital Twin Approach for Evaluating Positive Energy District Potential Using OGC Standards in Stuttgart

View Full Paper
RPRushikesh PadsalaBFBasak FalayAHAli Hainoun

Key Points

  • The modular workflow integrates building-scale simulations and district assessments for energy planning.
  • It harmonizes multi-scale energy data via a new Energy ADE 2.0 standard for urban environments.
  • The study provides a practical decision-support system for planners regarding net-positive energy solutions.
  • Connecting detailed simulations with standardized models enhances methodology for evaluating urban energy potentials.

Abstract

Abstract. This article introduces an urban digital twin workflow based on OGC standards and newly developed Energy ADE 2.0 that integrates building-scale simulations from SimStadt with district-level assessments using MAPED, connected through interactive web-based visualisation. This approach delivers a modular, open-source pipeline that harmonises multi-scale energy data and enables data-driven scenario analysis and stakeholder engagement in support of net-positive energy planning for urban districts. By connecting detailed simulation tools with standardised, spatially linked data models, the study advances the methodological foundation for assessing Positive Energy Districts (PED) using digital technologies and provides a practical decision-support system for planners and policy-makers involved in sustainable urban transformation.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Padsala et al. (2025) studied this question.

synapsesocial.com/papers/68d469ba31b076d99fa660d0https://doi.org/10.5194/isprs-archives-xlviii-4-w16-2025-67-2025
Ask AI
Helpful
Bookmark
Share
View Full Paper