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The goal of reaching the peak of carbon in the construction industry is urgent. However, the research on the feasibility of realizing this goal and the implementation of relevant policies in China is relatively superficial. In view of the historical data of energy consumption and building CO2 emission from 1995 to 2019, this paper establishes a BP neural network model for predicting building CO2 emissions. Moreover, the influencing factors, such as population, GDP, and total construction output, are introduced as the parameters in the model. Through the scenario analysis method explores the practical path to accomplish the peak of building CO2 emissions. When using traditional prediction methods to predict building carbon emissions, the long prediction cycle will increase the possibility of significant errors. Therefore, this paper constructs the calculation model of building carbon emission and forecasts the future carbon emission value through the BP neural network to avoid the error caused by the nonlinear relationship between influencing factors and predicted value. It will effectively predict the feasibility of the carbon peak and the carbon-neutral target set by government, and provide a useful predictive tool for adjusting the new energy structure and formulating related emission reduction policies. [ABSTRACT FROM AUTHOR]

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The problem of global warming is getting more serious, and vehicle emission is the main cause. In recent years, the number of locomotives in China has been increasing at a rate of more than 20% per year, and the problem of automobile pollution is becoming more serious. The transportation industry is the main source of fossil fuel combustion and environmental pollution. Therefore, in this paper, we propose a multi-view deep neural network (MVDNN) to analyze the key factors affecting the fuel consumption of automobiles. The experiments show that the introduction of human input improves the prediction accuracy and the root mean square error (RMSE) achieves 0.993. In addition, this paper also finds that for drivers, driving habits, driving frequency, and safety awareness are the most important factors affecting the fuel consumption of vehicles by combining Lasso regression with MVDNN. Finally, by comparing the prediction accuracy of different experiments, relevant policy suggestions are made. [ABSTRACT FROM AUTHOR]

Northern China is vigorously promoting cogeneration and clean heating technologies. The accurate prediction of building energy consumption is the basis for heating regulation. In this paper, the daily, weekly, and annual periods of building energy consumption are determined by Fourier transformation. Accordingly, a period-based neural network (PBNN) is proposed to predict building energy consumption. The main innovation of PBNN is the introduction of a new data structure, which is a time-discontinuous sliding window. The sliding window consists of the past 24 h, 24 h for the same period last week, and 24 h for the same period the previous year. When predicting the building energy consumption for the next 1 h, 12 h, and 24 h, the prediction errors of the PBNN are 2.30%, 3.47%, and 3.66% lower than those of the traditional sliding window PBNN (TSW-PBNN), respectively. The training time of PBNN is approximately half that of TSW-PBNN. The time-discontinuous sliding window reduces the energy consumption prediction error and neural network model training time. [ABSTRACT FROM AUTHOR]

The energy performance of buildings especially public buildings needs to be optimized together with environmental, social and cost performance, which can be achieved by the multiobjective optimization method. The traditional building performance simulation (BPS) based multiobjective optimization is time-consuming and inefficient. Practical projects of complex public building design usually involve many-objective optimization problems in which more than three objectives are considered. Using BPS based multiobjective optimization is not sufficient to solve this kind of design problem. This paper aims to propose an artificial neural network (ANN) based many-objective optimization design method, an architect-friendly integrated workflow has been implemented. The proposed method has been applied on a public library building in Changchun city of China to optimize its Energy Use Intensity (EUI), Spatial Daylight Autonomy (sDA), Useful Daylight Illuminance (UDI) and Building Envelope Cost (BEC). The optimization process has obtained 176 non-dominated solutions. By adopting the selected relative optimal solutions, 1.6×105–2.1×105 kWh energy can be saved per year; sDA value and UDI value can be increased by 8.1%–11.0% and 4.3%–4.7% respectively; BEC can be reduced by ¥1.2×105–2.1×105 ($1.7×104–3.0×104). The optimization time has been greatly shortened in this method and the whole process is highly efficient without manual data conversion between different platforms. [ABSTRACT FROM AUTHOR]

Energy piles have attracted much attention in recent years because they provide a profitable solution for efficient shallow geothermal energy utilization in different climatic regions. However, the long-term performance design of energy piles under different thermal scenarios currently relies on time-consuming and computationally expensive methods such as numerical simulations, which seriously hinders the further engineering application of energy piles. To this end, this paper provides a high-precision and computationally efficient model for predicting the long-term performance of energy pile design through an artificial neural network machine learning process. First, experimentally validated numerical models are developed. Then, they are utilized to generate the training and evaluate datasets for the proposed model by inputting sixty typical thermal load distributions in different regions of China. Finally, the performance of the proposed model is evaluated by comparing its calculations with those obtained using numerical models. The results show that the proposed model can predict the long-term performance of energy piles and enrich the current methods for the long-term design of energy piles. Furthermore, its features that tremendously reduce the computational time and minimum required resources make it an excellent supplement compared with numerical simulations. [ABSTRACT FROM AUTHOR]

This paper explores the design approach of multi-atrium configurations to improve the energy efficiency of shopping malls in cold climates. A typical building prototype in northern China was considered, and an artificial neural network (ANN)-based metamodel was developed with four design variables: the atrium area ratio (r), atrium number (n), distance between adjacent atriums (d0), and distance between atrium and façade wall (d1). The proposed metamodel was used for sensitivity analysis (SA) and building design optimization (BDO) to understand the performance of multi-atrium configuration. The SA indicates that the contribution of the design variables to the building energy efficiency follows the order r > d0 > n > d1, and there are interactions between variables r and n. The BDO demonstrates that it is beneficial for energy efficiency to incorporate more atrium areas, appropriate atrium sizes, larger distances between adjacent atriums, and more west-facing atriums. [ABSTRACT FROM AUTHOR]

The energy consumption pattern dominated by traditional fossil energy has led to global energy resource constraints and the deterioration of the ecological environment. These challenges have become a major issue all over the world. At present, the Chinese government aims to significantly reduce the fossil energy consumption contribution in the terminal energy consumption. The development of renewable energy in the terminal energy and energy conversion links has significantly increased the proportion of clean low-carbon energy. In order to accurately get the proportion of renewable energy terminal power consumption, firstly, this paper selects a primary influencing-factors set including the gross GDP, fixed investment in renewable energy industry, total length of cross-provincial and cross-regional high-voltage transmission lines, etc. as influencing factors of China's electricity consumption fraction produced by renewable energy based on a multitude of papers. Secondly, from the perspective of signal decomposition, the data inevitably has a lot of interference and noise. This paper uses the empirical mode decomposition (EMD) algorithm to reduce the degree of signal distortion and decomposes the signal into natural modes including several intrinsic mode functions (IMFs) and a residual term (Res); afterwards, a new extreme learning machine (ELM) forecasting model optimized by an Inverse Square Root Linear Units (ISRLU) activation function is proposed, and the ISRLU function is used to replace the implicit layer activation function in the original ELM algorithm. Then, a new bacterial foraging algorithm (BFOA) is applied to optimize the parameters of the optimized ELM forecasting model. After multiple learning and training operations, the optimal parameters are obtained. Finally, we superimpose the output of each IMF and Res training task to get the amount of China's power consumption produced by renewable energy. Some statistical indicators including root mean squard error (RMSE) are applied to compare the accuracy of several intelligent machine forecasting algorithms. We prove that the proposed forecasting model has higher prediction accuracy and achieves faster training speed by an empirical analysis. Finally, the proposed combined forecasting algorithm is applied to predict China's renewable energy terminal power consumption from 2018 to 2030. According to the forecasting results, it is found that China's renewable energy terminal power consumption shows a gradual growth trend, and will exceeded 3300 billion kWh in 2030, which will represent a renewable energy terminal power ratio of about 38% in 2030. [ABSTRACT FROM AUTHOR]

• A novel vector field-based SVR for building energy consumption prediction (ECP) is proposed. • Nonlinearity of the ECP models is transformed into linearity via vector field-based SVR. • Two high precision, generalization capability, and robustness energy consumption prediction models are established. • A real case study demonstrates the superiority of this study compared with previous studies. Building energy consumption prediction plays an irreplaceable role in energy planning, management, and conservation. Data-driven approaches, such as artificial neural networks, support vector regression, gradient boosting regression and extreme learning machine are the most advanced methods for building energy prediction. However, owing to the high nonlinearity between inputs and outputs of building energy consumption prediction models, the aforementioned approaches require improvement with regard to the prediction accuracy, robustness, and generalization ability. To counter these shortcomings, a novel vector field-based support vector regression method is proposed in this paper. Through multi-distortions in the sample data space or high-dimensional feature space mapped by a vector field, the optimal feature space is found, in which the high nonlinearity between inputs and outputs is approximated by linearity. Hence, the proposed method ensures a high accuracy, a generalization ability, and robustness for building energy consumption prediction. A large office building in a coastal town of China is used for a case study, and its summer hourly cooling load data are used as energy consumption data. The results indicate that the proposed method achieves better performance than commonly used methods with regard to the accuracy, robustness, and generalization ability. [ABSTRACT FROM AUTHOR]

Buildings account for a majority of the primary energy consumption of the human society, therefore, analyses of building energy consumption monitoring data are of significance to the discovery of anomalous energy usage patterns, saving of building utility expenditures, and contribution to the greater environmental protection effort. This paper presents a unified framework for the automatic extraction and integration of building energy consumption data from heterogeneous building management systems, along with building static data from building information models to serve analysis applications. This paper also proposes a diagnosis framework based on density-based clustering and artificial neural network regression using the integrated data to identify anomalous energy usages. The framework and the methods have been implemented and validated from data collected from a multitude of large-scale public buildings across China. [ABSTRACT FROM AUTHOR]

This paper explores the optimization scheme of carbon trading in China based on a novel energy-saving and emission-reduction (ESER) system with carbon price constraints. With the aid of nonlinear dynamics theory, the dynamics behavior of the novel system is discussed. Genetic algorithm and back propagation neural network is used to identify the quantitative coefficients according to the statistical data of the second period in European Union (EU). Taking the actual situation in EU for instance, the variables which are sensitive to carbon trading are detailedly researched. Enlightened by the EU’s experience, an optimal road of China ’ s carbon trading is put forward. The results show that carbon emissions could be controlled by carbon trading. The investment to carbon trading hampers economic growth in the near future, and ESER technical progress is negatively correlated with carbon trading in the long run. Demand and supply relationship is closely related to carbon price, both are the important issues in carbon trading system. Excessive government control and extortionate carbon price will deliver the opposite effect and even fatal influence on carbon trading system. [ABSTRACT FROM AUTHOR]

Carbon dioxide has a significant impact on global climate change due to its natural greenhouse effect. The objective and credible forecast of carbon emissions is very important for the government to formulate and implement the corresponding emission reduction targets. For controlling the growth of carbon emissions, Chinese government has put forward the low-carbon pilot policy and carbon trading policy. However, the existing grey models cannot measure the impact of policies and their interactions. In order to remedy the defect, a novel grey multivariable model based on dummy variables and their interactions is established. Two kinds of grey multivariable models and back propagation neural network model are chosen as comparison models to highlight that the introduction of dummy variables and their interactions plays an important part in improving the model performance. To verify the effectiveness, these four models are selected to simulate and predict the carbon emissions generated from primary energy consumption in Guangdong Province of China. The empirical results indicate that the mean absolute percentage errors of the novel model are 2.87% and 0.86%, respectively, which is significantly better than these three competing models. Finally, based on the outstanding performance of the novel model, it is chosen to forecast the fluctuating tendency of carbon emissions in the next 5 years. [ABSTRACT FROM AUTHOR]

Energy efficiency of data centers (DCs) is of great concern due to their large amount of energy consumption and the foreseeable growth in the demand of digital services in the future. The past decade witnessed improvements of the energy efficiency of DCs from an extensive margin—a shift from small to large, more efficient DCs. Improvements from the intensive margin, that is, from more efficient operation, would be critical in limiting the energy consumption and environmental impact of DCs in the upcoming period. Machine learning algorithms have advantages in optimizing the operation of DCs to improve energy efficiency as they have shown the potential of discovering control strategies not found by traditional method, and producing working condition‐dependent control strategies. This study proposes ready‐to‐use machine learning methods with practical details to decrease the most commonly used energy efficiency metric—power usage effectiveness in DCs. We achieved an accurate prediction by properly selecting the features used in the proposed prediction models established by neural network, light gradient boosting machine, recurrent neural network, and random forests. The proposed approaches are implemented in one of the largest hyperscale DCs in China—Tencent Tianjin DC, to optimize the set points of controllable variables in the cooling system and to detect and adjust the unreasonable working conditions in the modular data centers. The lower bound of PUE reduction was 0.005 with the proposed approaches, leading to about 1500 MWh (0.24% of the total designed electricity consumption of this DC) of energy saved per year in this hyperscale DC. The proposed methods have the potential to be transferred to DCs of similar scale, and the framework of our work could serve as a guide for machine learning‐based optimization of environmental indicators in other complex product/service systems. [ABSTRACT FROM AUTHOR]

This study is meant to investigate the main driving factors of energy consumption structure (ECS) in China and construct a hybrid prediction model with higher accuracy. In this paper, Logarithmic Mean Divisia Index (LMDI) is applied to study the factors of energy consumption structure of China covering 1980 to 2016 and calculate the contribution of each factor. The factors that have large contribution rate are selected as the main factors of energy consumption structure to simplify prediction model. And then, the original data series of energy consumption structure and main factors are decomposed into different components by empirical mode decomposition (EMD) model to deal with the intrinsic complexity and irregularity of sequences. Finally, the components are used as input data to construct the prediction model, least square support vector machine (LSSVM), the coefficients of which are optimized by the particle swarm optimization (PSO) algorithm. The results show that prediction error of EMD-PSOLSSVM model is significantly lower than benchmark models of grey model neural network and PSOLSSVM model. The constructed model fully exploits the advantages of each algorithm of the hybrid model and improves the prediction accuracy of energy consumption structure. Image 1 • The energy consumption structure in China is decomposed for 1980–2016. • The empirical mode decomposition is applied to decompose factors. • A hybrid prediction model is constructed to forecast energy consumption structure. • Income is dominant force of energy consumption structure. • The accuracy of hybrid model is far higher than benchmark models. [ABSTRACT FROM AUTHOR]

The forecasting of energy consumption in China is a key requirement for achieving national energy security and energy planning. In this study, multi-variable linear regression (MLR) and support vector regression (SVR) were utilized with a gated recurrent unit (GRU) artificial neural network of Chinese energy to establish a forecasting model. The derived model was validated through four economic variables; the gross domestic product (GDP), population, imports, and exports. The performance of various forecasting models was assessed via MAPE and RMSE, and three scenarios were configured based on different sources of variable data. In predicting Chinese energy consumption from 2015 to 2021, results from the established GRU model of the highest predictive accuracy showed that Chinese energy consumption would be likely to fluctuate from 2954.04 Mtoe to 5618.67 Mtoe in 2021. [ABSTRACT FROM AUTHOR]