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Exploring the spatial distribution for efficient sewage heat utilization in urban areas using the urban sewage state prediction model

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  • Chen, Wei-An
  • Lim, Jongyeon
  • Miyata, Shohei
  • Akashi, Yasunori

Abstract

The exploration of sewage heat as a clean energy source is an innovative and promising concept in the realm of sustainable energy development. By recovering heat from sewage through regional pipelines, there is enormous potential for maximizing its use. However, this field remains underexplored. This study proposes an evaluation method to enhance the sewage heat utilization strategies using Urban Sewage State Prediction Model (USSPM). With a specific focus on building heat demand and drainage features, the relationship among spatial distribution, building types, and sewage heat utilization potential was investigated through multiple case studies involving both real-world buildings and hypothetical scenarios. This study first demonstrates a decision-making process for identifying priority buildings for sewage heat system installation through a scenario in an actual area based on a comparison of their energy consumption. Moreover, the results of hypothetical scenarios provide recommendations for building locations and the characteristics of different types in sewage heat utilization. For instance, we suggest that residential buildings with high drainage temperatures, stable flow rates, and relatively low heat demand can predominate as ideal contributors to the overall heat supply and should be placed upstream. Hospitals and hotels, with larger heat demand, are advisable to position downstream. Through the proposed method and its application, this study provides recommendations for sewage heat utilization. It is expected to better leverage sewage heat in the future and achieve the goal of energy conservation through the utilization of clean energy.

Suggested Citation

  • Chen, Wei-An & Lim, Jongyeon & Miyata, Shohei & Akashi, Yasunori, 2024. "Exploring the spatial distribution for efficient sewage heat utilization in urban areas using the urban sewage state prediction model," Applied Energy, Elsevier, vol. 360(C).
    • Handle: RePEc:eee:appene:v:360:y:2024:i:c:s0306261924001594
    DOI: 10.1016/j.apenergy.2024.122776
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    References listed on IDEAS

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    1. Gao, Yuan & Hu, Zehuan & Shi, Shanrui & Chen, Wei-An & Liu, Mingzhe, 2024. "Adversarial discriminative domain adaptation for solar radiation prediction: A cross-regional study for zero-label transfer learning in Japan," Applied Energy, Elsevier, vol. 359(C).
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    Cited by:

    1. Gao, Yuan & Hu, Zehuan & Chen, Wei-An & Liu, Mingzhe, 2024. "Solutions to the insufficiency of label data in renewable energy forecasting: A comparative and integrative analysis of domain adaptation and fine-tuning," Energy, Elsevier, vol. 302(C).

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