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A Comparative Modeling Framework for Forecasting Distributed Energy Resource Adoption Under Trend-Based and Goal-Oriented Scenarios

Author

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  • Zheng Grace Ma

    (SDU Center for Energy Informatics, Maersk Mc-Kinney Moller Institute, The Faculty of Engineering, University of Southern Denmark, 5230 Odense, Denmark)

  • Magnus Værbak

    (SDU Center for Energy Informatics, Maersk Mc-Kinney Moller Institute, The Faculty of Engineering, University of Southern Denmark, 5230 Odense, Denmark)

  • Bo Nørregaard Jørgensen

    (SDU Center for Energy Informatics, Maersk Mc-Kinney Moller Institute, The Faculty of Engineering, University of Southern Denmark, 5230 Odense, Denmark)

Abstract

Accurate forecasting of Distributed Energy Resource (DER) adoption is essential for decarbonization, effective policy, and infrastructure planning. This paper develops a comparative framework integrating trend-based and goal-oriented approaches using the logistic growth and Bass diffusion models. Using Danish household data for electric vehicles (EVs), heat pumps (HPs), and rooftop photovoltaics (PVs), we evaluate four logistic-growth-based and two Bass-diffusion-based methods. Each method supports standard curve-fitting (trend-based) or incorporates explicit policy goals (goal-based), such as reaching a specified adoption threshold by a target year. An integrated flow diagram visually summarizes the decision process for method selection based on data availability, market maturity, and policy targets. Results show that Bass diffusion excels in early-stage or policy-driven markets like EVs, while logistic approaches perform better for PVs after subsidies are removed, with HP adoption falling in between. A key innovation is integrating future adoption targets into parameter estimation, enabling stakeholders to assess the required acceleration in adoption rates. The findings highlight the need to align model choice with data, market conditions, and policy objectives, offering practical guidance to accelerate DER deployment.

Suggested Citation

  • Zheng Grace Ma & Magnus Værbak & Bo Nørregaard Jørgensen, 2025. "A Comparative Modeling Framework for Forecasting Distributed Energy Resource Adoption Under Trend-Based and Goal-Oriented Scenarios," Sustainability, MDPI, vol. 17(12), pages 1-23, June.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:12:p:5283-:d:1674099
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    References listed on IDEAS

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    1. Antonopoulos, Chrissi A. & Fuentes, Tracy L. & McCord, Kieren H. & Rackley, Adrienne L.S. & Biswas, Saurabh, 2024. "Regional assessment of household energy decision-making and technology adoption in the United States," Energy Policy, Elsevier, vol. 185(C).
    2. Kamali Saraji, Mahyar & Aliasgari, Elahe & Streimikiene, Dalia, 2023. "Assessment of the challenges to renewable energy technologies adoption in rural areas: A Fermatean CRITIC-VIKOR approach," Technological Forecasting and Social Change, Elsevier, vol. 189(C).
    3. Dhirasasna, NiNa & Sahin, Oz, 2021. "A system dynamics model for renewable energy technology adoption of the hotel sector," Renewable Energy, Elsevier, vol. 163(C), pages 1994-2007.
    4. Feng, Xiao & Li, Yuyu & Huang, Bo, 2023. "Research on manufacturer's investment strategy and green credit policy for new energy vehicles based on consumers' preferences and technology adoption," Technological Forecasting and Social Change, Elsevier, vol. 191(C).
    5. Liu, Wei & Guo, Chuanmin & Gui, Xuechen & Li, Dong & Chen, Xinyu, 2025. "Exploring preferences for household energy technology adoption from a spatiotemporal perspective: Evidence from demographics, the economy, and the environment," Energy, Elsevier, vol. 319(C).
    6. Junya Wang & Yi-Jiao Zhang & Cong Xu & Jiaze Li & Jiachen Sun & Jiarong Xie & Ling Feng & Tianshou Zhou & Yanqing Hu, 2024. "Reconstructing the evolution history of networked complex systems," Nature Communications, Nature, vol. 15(1), pages 1-11, December.
    7. Galster, H.S. & Van der Wal, A.J. & Batenburg, A.E. & Koning, V. & Faaij, A.P.C., 2025. "A comprehensive review of integrating behavioral drivers of technology adoption and energy service use in energy system models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 214(C).
    8. He, Pan & Lovo, Stefania & Veronesi, Marcella, 2022. "Social networks and renewable energy technology adoption: Empirical evidence from biogas adoption in China," Energy Economics, Elsevier, vol. 106(C).
    9. Ke, Jing & Khanna, Nina & Zhou, Nan, 2022. "Indirect estimation of willingness to pay for energy technology adoption," Applied Energy, Elsevier, vol. 312(C).
    10. Nazir Ullah & Waleed S. Alnumay & Waleed Mugahed Al-Rahmi & Ahmed Ibrahim Alzahrani & Hosam Al-Samarraie, 2020. "Modeling Cost Saving and Innovativeness for Blockchain Technology Adoption by Energy Management," Energies, MDPI, vol. 13(18), pages 1-22, September.
    11. Wang, Liyang & Morabito, Molly & Payne, Christopher T. & Robinson, Gerald, 2020. "Identifying institutional barriers and policy implications for sustainable energy technology adoption among large organizations in California," Energy Policy, Elsevier, vol. 146(C).
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