Municipal heat planning within The World Avatar

IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Energy and AI Pub Date : 2025-02-14 DOI:10.1016/j.egyai.2025.100479
Yi-Kai Tsai , Markus Hofmeister , Srishti Ganguly , Kushagar Rustagi , Yong Ren Tan , Sebastian Mosbach , Jethro Akroyd , Markus Kraft
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Abstract

This paper presents a novel integration of building energy simulation with The World Avatar (TWA), a dynamic knowledge graph and agent-based framework designed for comprehensive and interoperable digital representation of the world. The study addresses the imperative for accurate and granular building energy data in energy planning scenarios. By leveraging knowledge graph, agents within TWA replace default assumptions in simulation tools with real-time and location-specific input data, such as building geometry, usage, weather, and terrain elevation. This integrated approach automates the simulation process, enabling agents to retrieve input data, execute simulations, and update the knowledge graph with results in a consistent format. To demonstrate this approach, we developed a simulation agent using the City Energy Analyst. Validation against external datasets from Germany and Singapore shows that the agent significantly improves simulation accuracy. The study also highlights the challenges in data acquisition and processing for municipal heat planning, aligning with the requirements of the German Heat Planning Act. Using Pirmasens, a mid-sized city in Germany, as an example, we demonstrate the practical applicability of the agent in municipal heat planning by providing highly granular data on the heating demands and the solar potentials for heat generation. An accompanying economic analysis further evaluates the cost implications and energy storage requirements associated with the installation of solar collectors, and identifies zones in the city with high solar suitability. These insights enable data-driven decision-making, showcasing the potential of this integrated approach to support municipal heat planning.

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世界神通的市政供热规划
本文提出了一种将建筑能源模拟与世界化身(TWA)相结合的新方法,TWA是一种动态知识图谱和基于智能体的框架,旨在全面和可互操作的数字世界表示。该研究解决了在能源规划场景中精确和精细的建筑能源数据的必要性。通过利用知识图,TWA中的代理用实时和特定位置的输入数据(如建筑几何形状、使用情况、天气和地形高度)替换模拟工具中的默认假设。这种集成的方法使模拟过程自动化,使代理能够检索输入数据,执行模拟,并使用一致格式的结果更新知识图谱。为了演示这种方法,我们使用City Energy Analyst开发了一个模拟代理。对来自德国和新加坡的外部数据集的验证表明,该代理显着提高了仿真精度。该研究还强调了城市供热规划数据采集和处理方面的挑战,与《德国供热规划法》的要求保持一致。以德国中型城市皮尔马森斯为例,我们通过提供有关供热需求和太阳能发电潜力的高粒度数据,展示了该剂在城市供热规划中的实际适用性。附带的经济分析进一步评估了与安装太阳能集热器相关的成本影响和能源存储需求,并确定了城市中太阳能高度适宜的区域。这些见解使数据驱动的决策成为可能,展示了这种综合方法支持城市供热规划的潜力。
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来源期刊
Energy and AI
Energy and AI Engineering-Engineering (miscellaneous)
CiteScore
16.50
自引率
0.00%
发文量
64
审稿时长
56 days
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