As an industry that consumes a quarter of social energy and emits a third of greenhouse gases, the construction industry has an important responsibility to achieve carbon peaking and carbon neutrality. Based on Web of Science, Science-Direct, and CNKI, the accounting and prediction models of carbon emissions from buildings are reviewed. The carbon emission factor method, mass balance method, and actual measurement method are analyzed. The top-down and bottom-up carbon emission accounting models and their subdivision models are introduced and analyzed. Individual building carbon emission assessments generally adopt a bottom-up physical model, while urban carbon emission assessments generally adopt a top-down economic input-output model. Most of the current studies on building carbon emission prediction models follow the path of “exploring influencing factors then putting forward prediction models based on influencing factors”. The studies on driving factors of carbon emission mainly use the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model, the Logarithmic Mean Divisia Index (LMDI) model, the grey correlation degree model, and other models. The prediction model is realized by the regression model, the system dynamics model, and other mathematical models, as well as the Artificial Neural Network (ANN) model, the Support Vector Machine (SVM) model, and other machine learning models. At present, the research on carbon emission models of individual buildings mainly focuses on the prediction of operational energy consumption, and the research models for the other stages should become a focus in future research.

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A Review of Building Carbon Emission Accounting and Prediction Models

A novel integrated optimization model for carbon emission prediction: A case study on the group of 20.

Human-building interaction for indoor environmental control: Evolution of technology and future prospects

Efficient prediction method of triple failure pressure for corroded pipelines under complex loads based on a backpropagation neural network

Air conditioning system in large public buildings have great potential for energy saving. In order to improve the operational efficiency of the air conditioning system, it is crucial to establish an accurate and effective cooling load forecasting model. This paper proposes a hybrid model based on random forest-improved sparrow search algorithm-long short term memory (RF-ISSA-LSTM) for forecasting the cooling load of large public buildings. Firstly, random forest (RF) is used to select high-impact parameters for building cooling load and reduce the dimensionality of the original input data. Secondly, three strategies are introduced to improve the location update in different stages of the sparrow search algorithm (ISSA), and then an ISSA-optimized LSTM forecasting model is established. Finally, two case studies are selected to evaluate the predictive performance of the model in detail. The experimental results show that the mean average percentage error (MAPE) and root mean square error (RMSE) of the RF-ISSA-LSTM model predicting the cooling load for these two buildings are 0.6072%, 3.3552 and 0.7939%, 4.4227, respectively. Compared with other neural network models, the RF-ISSA-LSTM model has higher prediction accuracy and shorter running time. Meanwhile, with the reduced training sample size, the RF-ISSA-LSTM model can still effectively predict the cooling load, which indicates its strong generalization ability. Therefore, this work is enlightening and the proposed hybrid prediction model can serve as a technical tool to support the energy efficiency management of large public buildings. 

A hybrid forecasting method for cooling load in large public buildings based on improved long short term memory

Prediction and Scenario Simulation of the Carbon Emissions of Public Buildings in the Operation Stage Based on an Energy Audit in Xi'An, China

An integrated modeling method of uncertainties: application-orientated fuzzy random spatiotemporal analysis of pipeline structures

Framework on low-carbon retrofit of rural residential buildings in arid areas of northwest China: A case study of Turpan residential buildings

Study on optimization method of summer nature ventilation for residential buildings in typical thermal zone of Xinjiang, China

Enhancing the performance of Earth-Air Heat Exchanger (EAHE) is highly beneficial for the achievement of sustainable development goals. However, improving EAHE's performance faces challenges due to conflicting demands of heat exchange effectiveness, energy conservation, and economic efficiency. Multi-objective optimization is an effective tool to address these challenges, yet research in this area is sparse. This study introduces a flexible multi-objective optimization framework for EAHE performance. Initially, an EAHE model was established, considering four objective functions: heat exchange efficiency, average thermal capacity, coefficient of performance, and payback time. Moreover, correlation and sensitivity analyses were employed to explore the relationship between EAHE design parameters and these objective functions. Subsequently, NSGA-II and TOPSIS were integrated for multi-objective optimization and decision-making. The results demonstrate that the established EAHE model is relatively accurate. Complex correlations and apparent mutual exclusions among the objective functions underscore the necessity of multi-objective optimization. There are significant differences in parameter sensitivity across different objective functions, and key parameters were selected for the implementation of multi-objective optimization. Five optimal solutions under various design preferences were obtained. Among these, the solution based on the entropy weight method achieved a 75.4 % heat exchange efficiency, 2727.4 W average thermal capacity, a coefficient of performance of 20.4, and a 5.28-year payback time. The research can guide the design optimization of EAHE, promoting its global dissemination and adoption. 

Enhancing the performance of earth-air heat exchanger: A flexible multi-objective optimization framework

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