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A Fast-Processing Open-Source Building Energy Model for Energy Communities

Research output: Contribution to journalConference articleAcademicpeer-review

Abstract

Renewable and Citizen energy communities offer a collective pathway to decarbonize the building energy sector through shared energy generation, investment, and energy efficiency. Social science studies focus on RECs and CECs are mostly case specific. Current energy system models mostly do not capture individual building heterogeneity. EnerPlanET, an open-source, web browser-based, and highly spatially explicit model, aims at addressing this gap by integrating building energy system with regional renewable and energy infrastructure planning. This paper presents an open-source single building parameterized Python-based model, called Building Energy Model (BuEM). BuEM is intended to become one of EnerPlanET’s modules. It is based on the lumped capacitance 5R1C model that follows the NEN-ISO 52016-1 standard. BuEM calculates thermal energy demands considering detailed 3D building geometry, weather, and a European, commonly used, buildings-related TABULA database. The model can calculate the hourly heating and cooling loads of building for a year in less than 8 seconds using a standard laptop. The model input choices can be made by users, developers, or modelers, where atypical characteristics of every building within a community can be captured, which is highly helpful in regional spatial planning.

Original languageEnglish
Number of pages6
JournalEnergy Proceedings
Volume65
DOIs
Publication statusPublished - 2026
Event17th International Conference on Applied Energy, ICAE 2025 - Bangkok, Thailand
Duration: 8 Dec 202512 Dec 2025

Bibliographical note

Publisher Copyright:
© 2026, Scanditale AB. All rights reserved.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 7 - Affordable and Clean Energy
    SDG 7 Affordable and Clean Energy

Keywords

  • building energy model
  • energy communities
  • open-source
  • parameterization
  • python
  • regional spatial planning

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