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Evaluating energy management and economic performance of a retrofitted zero-energy house under local climate-based seasonal patterns

Author

Listed:
  • Li, Zongmao
  • Su, Yuan
  • Lu, Yunqin
  • Wang, Yuhan
  • Li, Haifeng
  • Wang, Chanjuan
  • Gao, Weijun

Abstract

Achieving carbon neutrality requires large-scale retrofitting of existing residential buildings to zero-energy house standards. However, conventional season classifications often fail to capture the real impact of climate variability on energy use patterns, limiting the effectiveness of retrofit strategies. This study introduces a climate-responsive seasonal framework to assess the energy and economic sustainability of a fully electrified, grid-connected detached house in Saga City, Japan. Using meteorological data (2003–2024) and detailed energy use records (2022–2023), Seasonal-Trend Decomposition using Loess and appliance-level load analysis were applied to reveal seasonal load dynamics and photovoltaic performance. Findings show that local climate-based season definitions significantly improved the interpretation of seasonal load patterns, with the within-season variability of air-conditioning demand decreasing by more than 20 % compared with the conventional definition, effectively reducing misclassification during transitional months. A mismatch between photovoltaic generation and household load led to a low self-consumption ratio (12–22 %). Although feed-in revenues were stable, the system investment (4.92 million JPY) could not be recovered within the contractual period. The winter demand–photovoltaic mismatch calls for season-aligned storage planning, not just added capacity. The study highlights the need for season-sensitive analysis and integrated storge planning to guide sustainable residential retrofits.

Suggested Citation

  • Li, Zongmao & Su, Yuan & Lu, Yunqin & Wang, Yuhan & Li, Haifeng & Wang, Chanjuan & Gao, Weijun, 2026. "Evaluating energy management and economic performance of a retrofitted zero-energy house under local climate-based seasonal patterns," Renewable Energy, Elsevier, vol. 257(C).
    • Handle: RePEc:eee:renene:v:257:y:2026:i:c:s0960148125024760
    DOI: 10.1016/j.renene.2025.124812
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    References listed on IDEAS

    as
    1. Iwafune, Yumiko & Mori, Yuko & Kawai, Toshiaki & Yagita, Yoshie, 2017. "Energy-saving effect of automatic home energy report utilizing home energy management system data in Japan," Energy, Elsevier, vol. 125(C), pages 382-392.
    2. Hirvonen, Janne & Kayo, Genku & Hasan, Ala & Sirén, Kai, 2016. "Zero energy level and economic potential of small-scale building-integrated PV with different heating systems in Nordic conditions," Applied Energy, Elsevier, vol. 167(C), pages 255-269.
    3. She, Xiaohui & Wang, Huiru & Zhang, Tongtong & Li, Yongliang & Zhao, Xuemin & Ding, Yulong & Wang, Chen, 2025. "Liquid air energy storage – A critical review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 208(C).
    4. Dennis L Buchanan & Mark H A Davis, 2025. "Metals and Energy Project Appraisal and Finance," World Scientific Book Chapters, in: METALS AND ENERGY FINANCE Interrelationship between Technical and Financial Risk in Mineral Projects, chapter 8, pages 177-208, World Scientific Publishing Co. Pte. Ltd..
    5. Ding, Lili & Zhao, Haoran & Zhang, Rui, 2024. "Predicting multi-frequency crude oil price dynamics: Based on MIDAS and STL methods," Energy, Elsevier, vol. 313(C).
    6. Higashitani, Takuya & Ikegami, Takashi & Uemichi, Akane & Akisawa, Atsushi, 2021. "Evaluation of residential power supply by photovoltaics and electric vehicles," Renewable Energy, Elsevier, vol. 178(C), pages 745-756.
    7. Deb, Chirag & Zhang, Fan & Yang, Junjing & Lee, Siew Eang & Shah, Kwok Wei, 2017. "A review on time series forecasting techniques for building energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 74(C), pages 902-924.
    8. Theodosiou, Marina, 2011. "Forecasting monthly and quarterly time series using STL decomposition," International Journal of Forecasting, Elsevier, vol. 27(4), pages 1178-1195, October.
    9. Baetens, R. & De Coninck, R. & Van Roy, J. & Verbruggen, B. & Driesen, J. & Helsen, L. & Saelens, D., 2012. "Assessing electrical bottlenecks at feeder level for residential net zero-energy buildings by integrated system simulation," Applied Energy, Elsevier, vol. 96(C), pages 74-83.
    10. Zakeri, Behnam & Cross, Samuel & Dodds, Paul.E. & Gissey, Giorgio Castagneto, 2021. "Policy options for enhancing economic profitability of residential solar photovoltaic with battery energy storage," Applied Energy, Elsevier, vol. 290(C).
    11. De Luca, Giovanna & Ballarini, Ilaria & Lorenzati, Alice & Corrado, Vincenzo, 2020. "Renovation of a social house into a NZEB: Use of renewable energy sources and economic implications," Renewable Energy, Elsevier, vol. 159(C), pages 356-370.
    12. Li, Honglian & Yang, Yi & Lv, Kailin & Liu, Jing & Yang, Liu, 2020. "Compare several methods of select typical meteorological year for building energy simulation in China," Energy, Elsevier, vol. 209(C).
    13. Gong, Jianqiang & Qu, Zhiguo & Zhu, Zhenle & Xu, Hongtao, 2025. "Parallel TimesNet-BiLSTM model for ultra-short-term photovoltaic power forecasting using STL decomposition and auto-tuning," Energy, Elsevier, vol. 320(C).
    14. Fikru, Mahelet G. & Gelles, Gregory & Ichim, Ana-Maria & Kimball, Jonathan W. & Smith, Joseph D. & Zawodniok, Maciej Jan, 2018. "An economic model for residential energy consumption, generation, storage and reliance on cleaner energy," Renewable Energy, Elsevier, vol. 119(C), pages 429-438.
    15. Evan Kutta & Jason A. Hubbart, 2016. "Reconsidering meteorological seasons in a changing climate," Climatic Change, Springer, vol. 137(3), pages 511-524, August.
    16. Eshraghi, Hadi & Rodrigo de Queiroz, Anderson & Sankarasubramanian, A. & DeCarolis, Joseph F., 2021. "Quantification of climate-induced interannual variability in residential U.S. electricity demand," Energy, Elsevier, vol. 236(C).
    17. Pirker, Vivienne & Wöss, David & Grohmann, Simon & Scheiblauer, Sophie & Vitovec, Caroline & Wiesbauer, Jonathan & Pröll, Tobias, 2026. "Attractiveness of small-scale residential photovoltaic systems through investment profitability in five EU markets," Renewable Energy, Elsevier, vol. 256(PI).
    18. Aghamolaei, Reihaneh & Shamsi, Mohammad Haris & O’Donnell, James, 2020. "Feasibility analysis of community-based PV systems for residential districts: A comparison of on-site centralized and distributed PV installations," Renewable Energy, Elsevier, vol. 157(C), pages 793-808.
    19. Cheng, Zhen & Ding, Chante Jian & Zhao, Kunqian, 2025. "Energy use rights trading and carbon emissions," Energy, Elsevier, vol. 315(C).
    20. Mohammadi, Zahra & Ahmadi, Pouria & Ashjaee, Mehdi, 2024. "Comparative transient assessment and optimization of battery and hydrogen energy storage systems for near-zero energy buildings," Renewable Energy, Elsevier, vol. 220(C).
    21. de Oliveira, Erick Meira & Cyrino Oliveira, Fernando Luiz, 2018. "Forecasting mid-long term electric energy consumption through bagging ARIMA and exponential smoothing methods," Energy, Elsevier, vol. 144(C), pages 776-788.
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