Energy-saving potential prediction models for large-scale building: A state-of-the-art review

Yang, Xiu'e; Liu, Shuli; Zou, Yuliang; Ji, Wenjie; Zhang, Qunli; Ahmed, Abdullahi; Han, Xiangxin; Shen, Yongliang; Zhang, Shaoliang

doi:10.1016/j.rser.2021.111992

Cited by 32 publications

(11 citation statements)

References 79 publications

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“…In addition, the EE retrofit of floors was not considered either in the analysis. The EE retrofit option of floors on the ground was excluded mainly due to the long payback period and small energy savings compared to other measures [46,47]. Both floor and orientation factors should be included in future studies.…”

Section: Discussionmentioning

confidence: 99%

Applying the response surface methodology to predict the energy retrofit performance of the TABULA residential building stock

Kadric

Aganović

Kadrić

et al. 2022

Journal of Building Engineering

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Section: Discussionmentioning

confidence: 99%

Applying the response surface methodology to predict the energy retrofit performance of the TABULA residential building stock

Kadric

Aganović

Kadrić

et al. 2022

Journal of Building Engineering

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“…Therefore, the global impact needs to be considered during the decision making for enacting policies and standards. This research direction was also highlighted in the recent review [22], where the authors concluded that climate change, along with occupants' behaviors, needs to be introduced as uncertainties.…”

Section: Introductionmentioning

confidence: 94%

“…Therefore, a simplified but robust and straightforward measure is needed for scientific decision making regarding the national building energy standards and policies. This research direction was suggested in a recent and extensive review [22]; in the study, the pros and cons of physics-based, data-driven, and hybrid models integrating the Sustainability 2022, 14, 6000 3 of 13 two were analyzed. The overall recommendation was to develop fast and accurate models to support decision making for building energy policies.…”

Section: Introductionmentioning

confidence: 99%

Development of Simplified Building Energy Prediction Model to Support Policymaking in South Korea—Case Study for Office Buildings

Joe

Min

et al. 2022

Sustainability

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This study aims to support building energy policymaking for office buildings in South Korea through regression models by considering the global temperature rise. The key variables representing building energy standards and codes are selected, and their impact on the annual energy consumption is simulated using EnergyPlus reference models. Then, simplified regression models are built on the basis of the annual energy consumption using the selected variables. The prediction performance of the developed model for forecasting the annual energy consumption of each reference building is good, and the prediction error is negligible. An additional global coefficient is estimated to address the impact of increased outdoor air temperature in the future. The final model shows fair prediction performance with global coefficients of 1.27 and 0.9 for cooling and heating, respectively. It is expected that the proposed simplified model can be leveraged by non-expert policymakers to predict building energy consumption and corresponding greenhouse gas emissions for the target year.

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“…In 1886, Galton proposed MLR to describe the linear connection between several independent parameters and target variables, as shown in Equation ( 1) [73]…”

Section: Multiple Linear Regression (Mlr)mentioning

confidence: 99%

Building Energy Prediction Models and Related Uncertainties: A Review

Chang

Dong

2022

Buildings

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Building energy usage has been an important issue in recent decades, and energy prediction models are important tools for analysing this problem. This study provides a comprehensive review of building energy prediction models and uncertainties in the models. First, this paper introduces three types of prediction methods: white-box models, black-box models, and grey-box models. The principles, strengths, shortcomings, and applications of every model are discussed systematically. Second, this paper analyses prediction model uncertainties in terms of human, building, and weather factors. Finally, the research gaps in predicting building energy consumption are summarised in order to guide the optimisation of building energy prediction methods.

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Energy-saving potential prediction models for large-scale building: A state-of-the-art review

Cited by 32 publications

References 79 publications

Applying the response surface methodology to predict the energy retrofit performance of the TABULA residential building stock

Applying the response surface methodology to predict the energy retrofit performance of the TABULA residential building stock

Development of Simplified Building Energy Prediction Model to Support Policymaking in South Korea—Case Study for Office Buildings

Building Energy Prediction Models and Related Uncertainties: A Review

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