Exploring cost-effective strategies for emission reduction of public buildings in a life-cycle

Hu, Yu-Jie; Huang, He; Wang, Honglei; Li, Chengjiang; Deng, Yiqirui

doi:10.1016/j.enbuild.2023.112927

Cited by 13 publications

(2 citation statements)

References 95 publications

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“…The results revealed the placement of insulation in different layers of the walls has a most effective impact on energy saving of the buildings and the design of the insulations (Chersoni et al, 2022;Ozel, 2011). Furthermore, The results of the surveys highlighted that the optimal thickness of insulation placed in the outer layer of the wall in the different seasons made a tangible reduction in CO 2 production (Kharrufa and Noori, 2022;Yüksel et al, 2021;Hu et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

Thermal conductivity improvement in a green building with Nano insulations using machine learning methods

et al. 2023

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Section: Introductionmentioning

confidence: 99%

Thermal conductivity improvement in a green building with Nano insulations using machine learning methods

et al. 2023

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“…CO 2 , a greenhouse gas, is believed to be the main cause of global warming. To cope with climate change, many countries are implementing CO 2 emission reductions [1]. China is the second largest economy in the world and is also a major country in energy consumption and CO 2 emissions.…”

Section: Introductionmentioning

confidence: 99%

Joint Forecasting Model for the Hourly Cooling Load and Fluctuation Range of a Large Public Building Based on GA-SVM and IG-SVM

Wang,

Yu,

Zhou

et al. 2023

Sustainability

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Building load prediction is one of the important means of saving energy and reducing emissions, and accurate cold load prediction is conducive to the realization of online monitoring and the optimal control of building air conditioning systems. Therefore, a joint prediction model was proposed in this paper. Firstly, by combining the Pearson correlation coefficient (PCC) method with sensitivity analysis, the optimal combination of parameters that influence building cooling load (BCL) were obtained. Secondly, the parameters of the support vector machine (SVM) model were improved by using the genetic algorithm (GA), and a GA-SVM prediction model was proposed to perform building hourly cold load prediction. Then, when there is a demand for the fluctuation prediction of BCL or extreme weather conditions are encountered, the information granulation (IG) method is used to fuzzy granulate the data. At the same time, the fluctuation range of the BCL was obtained by combining the prediction of the established GA-SVM model. Finally, the model was validated with the actual operational data of a large public building in Xi’an. The results show that the CV-RMSE and MAPE of the GA-SVM model are reduced by 58.85% and 68.04%, respectively, compared with the SVM for the time-by-time BCL prediction, indicating that the optimization of the SVM by using the GA can effectively reduce the error of the prediction model. Compared with the other three widely used prediction models, the R2 of the GA-SVM model is improved by 4.75~6.35%, the MAPE is reduced by 68.00~72.76%, and the CV-RMSE is reduced by 59.69~64.97%. This proved that the GA-SVM has higher prediction accuracy. In addition, the joint model was used for BCL fluctuation range prediction, and the R2 of the prediction model was 97.27~99.68%, the MAPE was 2.59~2.84%, and the CV-RMSE was only 0.0249~0.0319, which demonstrated the effectiveness of the joint prediction model. The results of the study have important guiding significance for building load interval prediction, daily energy management and energy scheduling.

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Calculation of Carbon Emissions During Construction Using the Carbon Emission Factor Method

Yi,

Fu,

Liu

2024

Lecture Notes in Operations Research

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Exploring cost-effective strategies for emission reduction of public buildings in a life-cycle

Cited by 13 publications

References 95 publications

Thermal conductivity improvement in a green building with Nano insulations using machine learning methods

Thermal conductivity improvement in a green building with Nano insulations using machine learning methods

Joint Forecasting Model for the Hourly Cooling Load and Fluctuation Range of a Large Public Building Based on GA-SVM and IG-SVM

Calculation of Carbon Emissions During Construction Using the Carbon Emission Factor Method

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