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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]

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]

• 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]

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]

Abstract: As a regular data-driven method, Artificial Neural Networks (ANNs) are popular in building energy prediction. In this paper, an alternative approach, namely, hybrid genetic algorithm-adaptive network-based fuzzy inference system (GA-ANFIS) is presented. In this model, GA optimizes the subtractive clustering’s radiuses which help form the rule base, and ANFIS adjusts the premise and consequent parameters to optimize the forecasting performance. a hierarchical structure of ANFIS is also suggested to solve the probably curse-of-dimensionality problem. The performance of the proposed model is compared with ANN using two different data sets, which are collected from the Energy Prediction Shootout I contest and a library building located in Zhejiang University, China. Results show that the hybrid GA-ANFIS model has better performance than ANN in term of prediction accuracy. The proposed model also has the same scale of modeling time as ANN if parameters in GA procedure are carefully selected. It can be regarded as an alternative method in building energy prediction. [Copyright &y& Elsevier]

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]