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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引用次数: 0

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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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
期刊最新文献
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