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Heat pumps (HP) are essential for electrifying building heating systems and transitioning from fossil fuel-based systems to sustainable energy solutions. Therefore, accurate calculation of HP energy demand is important for urban-scale applications. However, HP demand calculations in the literature rely on hourly heating energy demands, which makes urban-scale analyses impractical due to limitations in data availability and computational resources. This study introduces a novel methodology to quickly and precisely estimate annual HP energy demand using data with low temporal resolution (annual ideal heating demand) and a TMY weather file. The methodology is based on partitioning the hours of the year into heating intensity periods (HIP) using heating degree hours (HDH). For each HIP, a coefficient of performance (COP) is calculated by using its average outdoor air temperature and an existing black-box model. Finally, the HP energy demand for each HIP is calculated based on its COP, and the total annual HP demand is determined by aggregating all Hips. The proposed methodology is tested across three cities in Turkiye with different climatic conditions, achieving validation with Mean Absolute Percentage Error (MAPE) values of 2.10%, 2.20%, and 2.83%, respectively. However, the precision is very sensitive to the base temperature of HDH in each city. Future research should focus on developing approaches to generalize base temperature values specific to a city’s climate and its building characteristics.

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Urban-Scale Prediction of Heat Pump Energy Demand with Low Temporal Resolution Data

  • 会议论文

  • 发表日期：

    2026年

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  • Fatma Ece Gursoy,
  • Ilkim Canli,
  • Yasin Ataberk Demir,
  • Sinan Kalkan,
  • Onur Taylan,
  • Ipek Gursel Dino

摘要

Heat pumps (HP) are essential for electrifying building heating systems and transitioning from fossil fuel-based systems to sustainable energy solutions. Therefore, accurate calculation of HP energy demand is important for urban-scale applications. However, HP demand calculations in the literature rely on hourly heating energy demands, which makes urban-scale analyses impractical due to limitations in data availability and computational resources. This study introduces a novel methodology to quickly and precisely estimate annual HP energy demand using data with low temporal resolution (annual ideal heating demand) and a TMY weather file. The methodology is based on partitioning the hours of the year into heating intensity periods (HIP) using heating degree hours (HDH). For each HIP, a coefficient of performance (COP) is calculated by using its average outdoor air temperature and an existing black-box model. Finally, the HP energy demand for each HIP is calculated based on its COP, and the total annual HP demand is determined by aggregating all Hips. The proposed methodology is tested across three cities in Turkiye with different climatic conditions, achieving validation with Mean Absolute Percentage Error (MAPE) values of 2.10%, 2.20%, and 2.83%, respectively. However, the precision is very sensitive to the base temperature of HDH in each city. Future research should focus on developing approaches to generalize base temperature values specific to a city’s climate and its building characteristics.