Accuracy of automated measurement and verification (M&V) techniques for energy savings in commercial buildings
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DOI: 10.1016/j.apenergy.2016.04.049
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References listed on IDEAS
- Zhao, Hai-xiang & Magoulès, Frédéric, 2012. "A review on the prediction of building energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(6), pages 3586-3592.
- Granderson, Jessica & Price, Phillip N. & Jump, David & Addy, Nathan & Sohn, Michael D., 2015. "Automated measurement and verification: Performance of public domain whole-building electric baseline models," Applied Energy, Elsevier, vol. 144(C), pages 106-113.
- Walter, Travis & Price, Phillip N. & Sohn, Michael D., 2014. "Uncertainty estimation improves energy measurement and verification procedures," Applied Energy, Elsevier, vol. 130(C), pages 230-236.
- Granderson, Jessica & Price, Phillip N., 2014. "Development and application of a statistical methodology to evaluate the predictive accuracy of building energy baseline models," Energy, Elsevier, vol. 66(C), pages 981-990.
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- Jeong, Cheoljoon & Byon, Eunshin, 2024. "Calibration of building energy computer models via bias-corrected iteratively reweighted least squares method," Applied Energy, Elsevier, vol. 360(C).
- Jeong Soo Shin & Jong Woo Park & Sean Hay Kim, 2020. "Measurement and Verification of Integrated Ground Source Heat Pumps on a Shared Ground Loop," Energies, MDPI, vol. 13(7), pages 1-24, April.
- Töppel, Jannick & Tränkler, Timm, 2019. "Modeling energy efficiency insurances and energy performance contracts for a quantitative comparison of risk mitigation potential," Energy Economics, Elsevier, vol. 80(C), pages 842-859.
- Díaz, Julián Arco & Ramos, José Sánchez & Delgado, M. Carmen Guerrero & García, David Hidalgo & Montoya, Francisco Gil & Domínguez, Servando Álvarez, 2018. "A daily baseline model based on transfer functions for the verification of energy saving. A case study of the administration room at the Palacio de la Madraza, Granada," Applied Energy, Elsevier, vol. 224(C), pages 538-549.
- Abdurahman Alrobaie & Moncef Krarti, 2022. "A Review of Data-Driven Approaches for Measurement and Verification Analysis of Building Energy Retrofits," Energies, MDPI, vol. 15(21), pages 1-30, October.
- Carstens, Herman & Xia, Xiaohua & Yadavalli, Sarma, 2018. "Measurement uncertainty in energy monitoring: Present state of the art," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2791-2805.
- Ke, Ming-Tsun & Yeh, Chia-Hung & Su, Cheng-Jie, 2017. "Cloud computing platform for real-time measurement and verification of energy performance," Applied Energy, Elsevier, vol. 188(C), pages 497-507.
- Baltuttis, Dennik & Töppel, Jannick & Tränkler, Timm & Wiethe, Christian, 2020. "Managing the risks of energy efficiency insurances in a portfolio context: An actuarial diversification approach," International Review of Financial Analysis, Elsevier, vol. 68(C).
- Simon Rouchier, 2022. "Bayesian Workflow and Hidden Markov Energy-Signature Model for Measurement and Verification," Energies, MDPI, vol. 15(10), pages 1-19, May.
- Fu, Hongxiang & Baltazar, Juan-Carlos & Claridge, David E., 2021. "Review of developments in whole-building statistical energy consumption models for commercial buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 147(C).
- Cory Mosiman & Gregor Henze & Herbert Els, 2021. "Development and Application of Schema Based Occupant-Centric Building Performance Metrics," Energies, MDPI, vol. 14(12), pages 1-16, June.
- Grillone, Benedetto & Danov, Stoyan & Sumper, Andreas & Cipriano, Jordi & Mor, Gerard, 2020. "A review of deterministic and data-driven methods to quantify energy efficiency savings and to predict retrofitting scenarios in buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
- Mirfin, Anthony & Xiao, Xun & Jack, Michael W., 2024. "TOWST: A physics-informed statistical model for building energy consumption with solar gain," Applied Energy, Elsevier, vol. 369(C).
- Fu, Chun & Miller, Clayton, 2022. "Using Google Trends as a proxy for occupant behavior to predict building energy consumption," Applied Energy, Elsevier, vol. 310(C).
- Granderson, Jessica & Fernandes, Samuel & Touzani, Samir & Lee, Chih-Cheng & Crowe, Eliot & Sheridan, Margaret, 2020. "Spatio-temporal impacts of a utility’s efficiency portfolio on the distribution grid," Energy, Elsevier, vol. 212(C).
- Carstens, Herman & Xia, Xiaohua & Yadavalli, Sarma, 2017. "Low-cost energy meter calibration method for measurement and verification," Applied Energy, Elsevier, vol. 188(C), pages 563-575.
- Herman Carstens & Xiaohua Xia & Sarma Yadavalli, 2018. "Bayesian Energy Measurement and Verification Analysis," Energies, MDPI, vol. 11(2), pages 1-20, February.
- Grillone, Benedetto & Mor, Gerard & Danov, Stoyan & Cipriano, Jordi & Sumper, Andreas, 2021. "A data-driven methodology for enhanced measurement and verification of energy efficiency savings in commercial buildings," Applied Energy, Elsevier, vol. 301(C).
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Keywords
Baseline model; Measurement and verification; Whole-building energy; Predictive performance accuracy; Building energy analysis; M&V 2.0;All these keywords.
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