Joint optimization of building-envelope and heating-system retrofits at territory scale to enhance decision-aiding
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DOI: 10.1016/j.apenergy.2020.114639
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Cited by:
- Thrampoulidis, Emmanouil & Mavromatidis, Georgios & Lucchi, Aurelien & Orehounig, Kristina, 2021. "A machine learning-based surrogate model to approximate optimal building retrofit solutions," Applied Energy, Elsevier, vol. 281(C).
- Martin, Rit & Arthur, Thomas & Jonathan, Villot & Mathieu, Thorel & Enora, Garreau & Robin, Girard, 2024. "SHAPE: A temporal optimization model for residential buildings retrofit to discuss policy objectives," Applied Energy, Elsevier, vol. 361(C).
- Heleno, Miguel & Sigrin, Benjamin & Popovich, Natalie & Heeter, Jenny & Jain Figueroa, Anjuli & Reiner, Michael & Reames, Tony, 2022. "Optimizing equity in energy policy interventions: A quantitative decision-support framework for energy justice," Applied Energy, Elsevier, vol. 325(C).
- Lerbinger, Alicia & Petkov, Ivalin & Mavromatidis, Georgios & Knoeri, Christof, 2023. "Optimal decarbonization strategies for existing districts considering energy systems and retrofits," Applied Energy, Elsevier, vol. 352(C).
- Yang, Xining & Hu, Mingming & Tukker, Arnold & Zhang, Chunbo & Huo, Tengfei & Steubing, Bernhard, 2022. "A bottom-up dynamic building stock model for residential energy transition: A case study for the Netherlands," Applied Energy, Elsevier, vol. 306(PA).
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Keywords
Energy savings; Retrofit; Linear integer programming; Cost optimization; Knapsack problem; Territory scale;All these keywords.
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