Assessment of the Potential of High-Performance Buildings to Achieve Zero Energy: A Case Study

Wang, Dan; Pang, Xiufeng; Wang, Wei; Qi, Zewei; Jin, Li; Luo, Duo

doi:10.3390/app9040775

Cited by 7 publications

(6 citation statements)

References 22 publications

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“…A grid search was established to find the optimal values for σ and C with σ lies in the range of [0,1]; C lies in the range of [0. 25,5]. The stepwise for σ and C in the grid search is 0.05 and 0.25, respectively.…”

Section: Parameters Acronym Valuementioning

confidence: 99%

“…Smart cities are the development goals of many countries around the world [1]. Intelligent systems have been widely researched and applied in smart cities to provide a better quality of life, as well as to bring higher economic efficiency [2][3][4][5][6]. One of the critical issues of smart cities is the efficient use of energy by buildings [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

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Estimating the Heating Load of Buildings for Smart City Planning Using a Novel Artificial Intelligence Technique PSO-XGBoost

et al. 2019

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In this study, a novel technique to support smart city planning in estimating and controlling the heating load (HL) of buildings, was proposed, namely PSO-XGBoost. Accordingly, the extreme gradient boosting machine (XGBoost) was developed to estimate HL first; then, the particle swarm optimization (PSO) algorithm was applied to optimize the performance of the XGBoost model. The classical XGBoost model, support vector machine (SVM), random forest (RF), Gaussian process (GP), and classification and regression trees (CART) models were also investigated and developed to predict the HL of building systems, and compared with the proposed PSO-XGBoost model; 837 investigations of buildings were considered and analyzed with many influential factors, such as glazing area distribution (GAD), glazing area (GA), orientation (O), overall height (OH), roof area (RA), wall area (WA), surface area (SA), and relative compactness (RC). Mean absolute percentage error (MAPE), root-mean-squared error (RMSE), variance account for (VAF), mean absolute error (MAE), and determination coefficient (R2), were used as the statistical criteria for evaluating the performance of the above models. The color intensity, as well as the ranking method, were also used to compare and evaluate the models. The results showed that the proposed PSO-XGBoost model was the most robust technique for estimating the HL of building systems. The remaining models (i.e., XGBoost, SVM, RF, GP, and CART) yielded more mediocre performance through RMSE, MAE, R2, VAF, and MAPE metrics. Another finding of this study also indicated that OH, RA, WA, and SA were the most critical parameters for the accuracy of the proposed PSO-XGBoost model. They should be particularly interested in smart city planning as well as the optimization of smart cities.

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Section: Parameters Acronym Valuementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Estimating the Heating Load of Buildings for Smart City Planning Using a Novel Artificial Intelligence Technique PSO-XGBoost

et al. 2019

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“…Net Zero-Energy Building has become a popular catch phrase to describe the synergy between energy-efficient building and renewable energy utilization to achieve a balanced energy budget over an annual cycle. Several experts have proposed different methods for designing zero energy and carbon buildings in several types of climate [16][17][18][19][20][21][22]. But no study has yet been done in the specific case of a country in the Indian Ocean (Madagascar, Mayotte, Reunion, Comoros etc.).…”

Section: Introductionmentioning

confidence: 99%

Net Zero Energy Buildings and Low Carbon Emission, a Case of Study of Madagascar Island

Nematchoua¹,

Reiter²

2020

Zero-Energy Buildings - New Approaches and Technologies

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The buildings respecting the concept "Net Zero energy" are becoming more and more flowering in the world these last years. The main goal of this research is to evaluate the different possibilities of implementation of buildings with Net zero energy and low environmental impacts in Sub-Saharan Africa. The proposed building is 80% made of local materials with low carbon emissions and especially at lower cost. The optimization and modeling of the building is carried out by the Design Builder software, which is a world-renowned software in the field of optimization of comfort, cost, carbon reduction, etc. By fixing the insulation thickness up to 11 cm, cooling and heating energy are found equal to zero during the different operating seasons in this residential building. The results show that the optimal solution to consider a net zero energy building in Antananarivo city requires an additional expense estimated at 40% of the cost of buildings more conventional encountered in the island. This will save $475 each year starting in 2030, with 99% reduction in the CO 2 release. The choice of local materials with low conductivity, low emissions, and low cost, has a significant impact on the implementation of a sustainable building, and more adapted to climate change concept.

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“…So, everything has been said on that topic? Not at all, and up-to-date research on all aspects related to the design of a zero-energy building is still being developed, as the articles reported [1][2][3][4][5][6][7][8][9][10][11] in this special issue testify.…”

mentioning

confidence: 99%

“…In particular, the second one, provides an holistic framework (EDeSSOpt) for the simultaneous optimization of the energy demand (envelope) and supply (systems, energy sources) sides options to reach the ZEB target in case of a single-family house. Wang et al [4] treats through dynamic simulation the capability of a highly-performing building case study to reach the zero-energy target. But ZEB is not only limited to new buildings.…”

mentioning

confidence: 99%

Zero Energy Buildings: A Reached Target or a Starting Point?

Fabrizio

2020

Applied Sciences

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Assessment of the Potential of High-Performance Buildings to Achieve Zero Energy: A Case Study

Cited by 7 publications

References 22 publications

Estimating the Heating Load of Buildings for Smart City Planning Using a Novel Artificial Intelligence Technique PSO-XGBoost

Estimating the Heating Load of Buildings for Smart City Planning Using a Novel Artificial Intelligence Technique PSO-XGBoost

Net Zero Energy Buildings and Low Carbon Emission, a Case of Study of Madagascar Island

Zero Energy Buildings: A Reached Target or a Starting Point?

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