Study on the Principle Model and Simplification of Subway Station Itemized Energy Consumption

Owing to the constant modernization and urbanization of China,most cities have subways,and the total power consumption of subway stations has been rising,which contradicts with China’s green goals.In this regard,energy consumption indicators for major energy use projects in subway stations should be established,comprehensive evaluation and detailed research on the current situation of subway operation should be conducted,and specific energy-saving measures should be taken from a diversified perspective.It is crucial to systematically sort out the principle of sub-item energy consumption in subway stations,and practice and explore the specific simplification measures of the principle model of sub-item energy consumption,so as to lay a solid foundation for achieving the goal of reducing energy consumption of subway stations.

Multi-Time Scale Optimal Scheduling of a Photovoltaic Energy Storage Building System Based on Model Predictive Control

Building emission reduction is an important way to achieve China’s carbon peaking and carbon neutrality goals.Aiming at the problem of low carbon economic operation of a photovoltaic energy storage building system,a multi-time scale optimal scheduling strategy based on model predictive control(MPC)is proposed under the consideration of load optimization.First,load optimization is achieved by controlling the charging time of electric vehicles as well as adjusting the air conditioning operation temperature,and the photovoltaic energy storage building system model is constructed to propose a day-ahead scheduling strategy with the lowest daily operation cost.Second,considering inter-day to intra-day source-load prediction error,an intraday rolling optimal scheduling strategy based on MPC is proposed that dynamically corrects the day-ahead dispatch results to stabilize system power fluctuations and promote photovoltaic consumption.Finally,taking an office building on a summer work day as an example,the effectiveness of the proposed scheduling strategy is verified.The results of the example show that the strategy reduces the total operating cost of the photovoltaic energy storage building system by 17.11%,improves the carbon emission reduction by 7.99%,and the photovoltaic consumption rate reaches 98.57%,improving the system’s low-carbon and economic performance.

Deep Learning Based Energy Consumption Prediction on Internet of Things Environment

The creation of national energy strategy cannot proceed without accurate projections of future electricity consumption;this is because EC is intimately tied to other forms of energy,such as oil and natural gas.For the purpose of determining and bettering overall energy consumption,there is an urgent requirement for accurate monitoring and calculation of EC at the building level using cutting-edge technology such as data analytics and the internet of things(IoT).Soft computing is a subset of AI that tries to design procedures that are more accurate and reliable,and it has proven to be an effective tool for solving a number of issues that are associated with the use of energy.The use of soft computing for energy prediction is an essential part of the solution to these kinds of challenges.This study presents an improved version of the Harris Hawks Optimization model by combining it with the IHHODL-ECP algorithm for use in Internet of Things settings.The IHHODL-ECP model that has been supplied acts as a useful instrument for the prediction of integrated energy consumption.In order for the raw electrical data to be compatible with the subsequent processing in the IHHODL-ECP model,it is necessary to perform a preprocessing step.The technique of prediction uses a combination of three different kinds of deep learning models,namely DNN,GRU,and DBN.In addition to this,the IHHO algorithm is used as a technique for making adjustments to the hyperparameters.The experimental result analysis of the IHHODL-ECP model is carried out under a variety of different aspects,and the comparison inquiry highlighted the advantages of the IHHODL-ECP model over other present approaches.According to the findings of the experiments conducted with an hourly time resolution,the IHHODL-ECP model obtained a MAPE value of 33.85,which was lower than those produced by the LR,LSTM,and CNN-LSTM models,which had MAPE values of 83.22,44.57,and 34.62 respectively.These findings provided evidence of the IHHODL-ECP model’s improved ability to provide accurate forecasts.

Forecasting Energy Consumption Using a Novel Hybrid Dipper Throated Optimization and Stochastic Fractal Search Algorithm

The accurate prediction of energy consumption has effective role in decision making and risk management for individuals and governments.Meanwhile,the accurate prediction can be realized using the recent advances in machine learning and predictive models.This research proposes a novel approach for energy consumption forecasting based on a new optimization algorithm and a new forecasting model consisting of a set of long short-term memory(LSTM)units.The proposed optimization algorithm is used to optimize the parameters of the LSTM-based model to boost its forecasting accuracy.This optimization algorithm is based on the recently emerged dipper-throated optimization(DTO)and stochastic fractal search(SFS)algo-rithm and is referred to as dynamic DTOSFS.To prove the effectiveness and superiority of the proposed approach,five standard benchmark algorithms,namely,stochastic fractal search(SFS),dipper throated optimization(DTO),whale optimization algorithm(WOA),particle swarm optimization(PSO),and grey wolf optimization(GWO),are used to optimize the parameters of the LSTM-based model,and the results are compared with that of the proposed approach.Experimental results show that the proposed DDTOSFS+LSTM can accurately forecast the energy consumption with root mean square error RMSE of 0.00013,which is the best among the recorded results of the other methods.In addition,statistical experiments are conducted to prove the statistical difference of the proposed model.The results of these tests confirmed the expected outcomes.

A hybrid agent⁃based machine learning method for human⁃centred energy consumption prediction

Occupant behaviour has significant impacts on the performance of machine learning algorithms when predicting building energy consumption.Due to a variety of reasons(e.g.,underperforming building energy management systems or restrictions due to privacy policies),the availability of occupational data has long been an obstacle that hinders the performance of machine learning algorithms in predicting building energy consumption.Therefore,this study proposed an agent⁃based machine learning model whereby agent⁃based modelling was employed to generate simulated occupational data as input features for machine learning algorithms for building energy consumption prediction.Boruta feature selection was also introduced in this study to select all relevant features.The results indicated that the performances of machine learning algorithms in predicting building energy consumption were significantly improved when using simulated occupational data,with even greater improvements after conducting Boruta feature selection.

Analysis and calculation model of energy consumption and product yields of delayed coking units

Delayed coking is an important process consumption and light oil yield are important factors used to convert heavy oils to light products. Energy for evaluating the delayed coking process. This paper analyzes the energy consumption and product yields of delayed coking units in China. The average energy consumption shows a decreasing trend in recent years. The energy consumption of different refineries varies greatly, with the average value of the highest energy consumption approximately twice that of the lowest energy consumption. The factors affecting both energy consumption and product yields were analyzed, and correlation models of energy consumption and product yields were established using a quadratic polynomial. The model coefficients were calculated through least square regression of collected industrial data of delayed coking units. Both models showed good calculation accuracy. The average absolute error of the energy consumption model was approximately 85 MJ/t, and that of the product yield model ranged from 1 wt% to 2.3 wt%. The model prediction showed that a large annual processing capacity and high load rate will result in a reduction in energy consumption.

Modeling and Analysis on Production Yield and Energy Consumption of Pelleting Process

The existing studies on the pelleting process were reviewed, and then the forming process of pelleting was introduced. Furthermore, the models describing the production yield and energy consumption of pelleting were presented. Based on the models, the influence of the pelleting structure parameters, die speed on the production yield and energy consumption were discussed. The results showed that larger pellet mill was preferred and the proper speed of the die should be selected to increase the production yield and reduce the energy consumption.

Adaptive Error Curve Learning Ensemble Model for Improving Energy Consumption Forecasting

Despite the advancement within the last decades in the field of smart grids,energy consumption forecasting utilizing the metrological features is still challenging.This paper proposes a genetic algorithm-based adaptive error curve learning ensemble(GA-ECLE)model.The proposed technique copes with the stochastic variations of improving energy consumption forecasting using a machine learning-based ensembled approach.A modified ensemble model based on a utilizing error of model as a feature is used to improve the forecast accuracy.This approach combines three models,namely CatBoost(CB),Gradient Boost(GB),and Multilayer Perceptron(MLP).The ensembled CB-GB-MLP model’s inner mechanism consists of generating a meta-data from Gradient Boosting and CatBoost models to compute the final predictions using the Multilayer Perceptron network.A genetic algorithm is used to obtain the optimal features to be used for the model.To prove the proposed model’s effectiveness,we have used a four-phase technique using Jeju island’s real energy consumption data.In the first phase,we have obtained the results by applying the CB-GB-MLP model.In the second phase,we have utilized a GA-ensembled model with optimal features.The third phase is for the comparison of the energy forecasting result with the proposed ECL-based model.The fourth stage is the final stage,where we have applied the GA-ECLE model.We obtained a mean absolute error of 3.05,and a root mean square error of 5.05.Extensive experimental results are provided,demonstrating the superiority of the proposed GA-ECLE model over traditional ensemble models.

Research on Calculation Models of Coal Comminution Energy Consumption

在总结粉碎功耗规律的研究进展的基础上，分析了单一粒度颗粒的粉碎功耗预测理论，介绍了粉碎功耗理论的3种著名假说体积假说、面积假说、裂缝假说及其修正计算方法，并进行了比较，描述了粉碎功耗计算的通用关系式和近年来提出的新机制，包括原生裂纹假说、突变理论及分形理论等；进而针对具有复杂粒度分布物料的粉碎功耗，将给料与产品粒度分布作为参数引入能耗计算中，获得了当粒度分布分别满足G．S分布、R—R分布、分形分布时的各种粉碎功耗理论预测结果。此外，还列出了部分常用的粉碎功耗经验关联式并进行了比较。文中提出在研究煤的粉碎功耗规律时，采用Walker计算式结合分形粒度分布或R-R分布的能耗模型及Morrell经验式是可行的。

Simplified prediction model for lighting energy consumption in office building scheme design

At the scheme design stage,the potential of daylighting is significant due to the saving for electric lighting use. There are few simple tools for architects to optimize the daylighting design. Therefore,it is useful to develop a design guideline related to the evaluation of lighting energy saving potential and sunlight design strategies. This paper analyzes the impacts of different artificial lighting control methods and design parameters on daylighting. A direct correlation between lighting energy consumption and parameters such as orientations,window to wall ratio (WWR) and perimeter depth is established. A simplified prediction model is proposed to estimate lighting energy consumption with the given perimeter depth,WWR,and window transparency. Validation of the model is carried out compared with detailed lighting simulation software for an office building. After the variation analysis for these parameters,design advises for the daylighting design at scheme design phase are summarized.

A novel car-following model by sharing cooperative information transmission delayed effect under V2X environment and its additional energy consumption

A novel car-following model is offered based on the cooperative information transmission delayed effect involving headway and velocity under V2 X environment.The stability conditions and m Kd V equation of the new model are obtained via the linear and nonlinear analysis.Through numerical simulation,the variation trend of headway and hysteresis phenomenon are studied.In addition,we investigate the additional energy consumption of the vehicle during acceleration.In brief,theoretical analysis and simulation results confirm that the new car-following model based on the cooperative information transmission delayed effect can improve traffic stability and reduce additional energy consumption.

Decoupling economic growth from CO_2 emissions: A decomposition analysis of China's household energy consumption

This paper analyzes Chinese household CO_2 emissions in 1994-2012 based on the Logarithmic Mean Divisia Index(LMDI) structure decomposition model, and discusses the relationship between household CO_2 emissions and economic growth based on a decoupling indicator.The results show that in 1994-2012, household CO_2 emissions grew in general and displayed an accelerated growth trend during the early 21 st century. Economic growth leading to an increase in energy consumption is the main driving factor of CO_2 emission growth(an increase of 1.078 Gt CO_2) with cumulative contribution rate of 55.92%, while the decline in energy intensity is the main cause of CO_2 emission growth inhibition(0.723 Gt CO_2 emission reduction) with cumulative contribution rate of 38.27%. Meanwhile, household CO_2 emissions are in a weak state of decoupling in general. The change in CO_2 emissions caused by population and economic growth shows a weak decoupling and expansive decoupling state, respectively. The CO_2 emission change caused by energy intensity is in a state of strong decoupling, and the change caused by energy consumption structure ?uctuates between a weak and a strong decoupling state.

Analysis and forecast of residential building energy consumption in Chongqing on carbon emissions

Carbon emissions mainly result from energy consumption. Carbon emissions inevitably will increase to some extent with economic expansion and rising energy consumption. We introduce a gray theory of quantitative analysis of the energy consumption of residential buildings in Chongqing,China,on the impact of carbon emission factors. Three impacts are analyzed,namely per capita residential housing area,domestic water consumption and the rate of air conditioner ownership per 100 urban households. The gray prediction model established using the Chongqing carbon emission-residential building energy consumption forecast model is sufficiently accurate to achieve a measure of feasibility and applicability.

Digital finance and renewable energy consumption:evidence from China

While digital finance and renewable energy consumption(REC)are two timely issues,it remains unclear whether the former affects the latter,especially in developing economies.This paper examines the impact of digital finance on China’s REC between 2011 and 2018 and explores the underlying mechanisms.Results show that digital finance,along with its coverage breadth and usage depth,significantly improved REC in China and that digital finance in the area of credit has had the most significant impact.Additionally,the results show that loan scale and income level are the main mediation variables,through which digital finance affects REC.The findings also suggest that economic growth and technological progress have increased REC in China,while carbon dioxide emissions have had no meaningful effect on this consumption.The results further indicate that policymakers must pay close attention to the role of digital finance when formulating policies on REC.To promote REC and environmental sustainability,developing economies like China should strengthen the breadth and depth of digital finance development,focus on the influence channels of digital finance,and promote economic growth and technological progress.

Nonlinear density wave and energy consumption investigation of traffic flow on a curved road

A new car-following model is proposed based on the full velocity difference model（FVDM） taking the influence of the friction coefficient and the road curvature into account. Through the control theory, the stability conditions are obtained,and by using nonlinear analysis, the time-dependent Ginzburg-Landau（TDGL） equation and the modified Korteweg-de Vries（mKdV） equation are derived. Furthermore, the connection between TDGL and mKdV equations is also given. The numerical simulation is consistent with the theoretical analysis. The evolution of a traffic jam and the corresponding energy consumption are explored. The numerical results show that the control scheme is effective not only to suppress the traffic jam but also to reduce the energy consumption.

The relationship between energy consumption and train delay in railway traffic

Based on deterministic NaSch model, we propose a new cellular automation model for simulating train movement. In the proposed model, the reaction time of driver/train equipment is considered. Our study is focused on the additional energy consumption arising by train delay around a traffic bottle （station）. The simulation results demonstrate that the proposed model is suitable for simulating the train movement under high speed condition. Further, we discuss the relationship between the additional energy consumption and some factors which affect the formation of train delay, such as the maximum speed of trains and the station dwell time etc.

Prediction of Low-Energy Building Energy Consumption Based on Genetic BP Algorithm

Combined with the energy consumption data of individual buildings in the logistics group of Yangtze University,the analysis model scheme of energy consumption of individual buildings in the university is studied by using Back Propagation(BP)neural network to solve nonlinear problems and have the ability of global approximation and generalization.By analyzing the influence of different uses,different building surfaces and different energysaving schemes on the change of building energy consumption,the grey correlation method is used to determine the main influencing factors affecting each building energy consumption,including uses,building surfaces and energy-saving schemes,which are used as the input of the model and the building energy consumption as the output of the model,so as to establish the building energy consumption analysis model based on BP neural network.However,in practical application,BP neural network has the defects of slow convergence and easy to fall into local minima.In view of this,this paper uses genetic algorithm to optimize the weight and threshold of BP neural network,completes the improvement of various building energy consumption analysis models,and realizes the qualitative analysis of building energy consumption.The model verification results show that the viscosity of the building energy consumption analysis model based on genetic algorithm improved BP neural network algorithm(GABP)in this paper is relatively high,which is more accurate than the results of the traditional BP neural network model,and the relative error of the analysis model is reduced from 11.56%to 8.13%,which proves that the GABP can be better suitable for the study of school building energy consumption analysis model,It is applied to the prediction of building energy consumption,which lays a foundation for the realization of carbon neutralization in the South expansion plan of Yangtze University.

The Non-Linear Effect of China’s Energy Consumption on Eco-Environment Pollution

With the increase of total energy consumption,eco-environmental quality drops sharply,which has attracted concerns from all circles.It has become the top priority of construction of socialist ecological civilization to clarify the influences of energy consumption on the level of eco-environmental pollution.Ecological environmental pollution control cannot be one size fits all.It can avoid resource depletion and environmental deterioration via adjusting measures to local conditions to coordinate ecological environmental pollution and energy consumption problems.In this essay,entropy method is adopted to measure the composite indexes of eco-environmental pollution of 30 provinces and cities in China,based on which kernel density function is used to analyze the dynamic law of eco-environmental pollution.And then,traditional fixed effect model and panel quantile regression model are adopted respectively to analyze the influences of energy consumption on eco-environmental pollution.The research finds that composite index of eco-environmental pollution shows N-shaped curve of“rising-dropping-rising”during the sample period,with the overall difference decreasing gradually and the polarization disappearing gradually;in areas with higher eco-environmental pollution,energy consumption has aggravated ecoenvironmental pollution,while in areas with lower eco-environmental pollution,energy consumption could alleviate eco-environmental pollution to some degree;foreign direct investment could relieve eco-environmental pollution.Therefore,corresponding measures should be taken to improve the quality of eco-environment based on the changes of energy consumption in areas with different levels of eco-environmental pollution.

The Influence of Urbanization on Cooling Energy Consumption in Xi＇an

Based on data of daily air temperature during 1951-2013,long-term variation characteristics of cooling degree days（ CDD） in Xi＇an and Chang＇an in summer were analyzed by using CDD to evaluate cooling energy consumption and 26 ℃ as the basic temperature of CDD. The results indicated that the changing trends of CDD in Xi＇an and Chang＇an were basically identical within a year,and the demand for cooling refrigeration was large mainly from June to August,especially in July. The maximum of urban-rural difference of CDD between Xi＇an and Chang＇an appeared in June.In order to achieve the same temperature,energy needed by the urban area was 5-7 ℃·d more than the suburb from June to August. Temperature and the cooling energy consumption were closely related,and the correlation degree increased with the rise of temperature. The effects of temperature increase of 1 ℃ on cooling energy consumption rate in Xi＇an were more obvious than that in Chang＇an. In both Xi＇an and Chang＇an,the effects of temperature increase of 1 ℃ on cooling energy consumption rate in July and August were greater than that in May,June and September.Evaluation models of cooling energy consumption in summer in Xi＇an and Chang＇an were built using temperature anomaly and CDD variability and can be applied to business systems.

Influencing factors and contribution analysis of CO_(2) emissions originating from final energy consumption in Sichuan Province,China

Within the context of CO_(2)emission peaking and carbon neutrality,the study of CO_(2)emissions at the provincial level is few.Sichuan Province in China has not only superior clean energy resources endowment but also great potential for the reduction of CO_(2)emissions.Therefore,using logarithmic mean Divisia index(LMDI)model to analysis the influence degree of different influencing factors on CO_(2)emissions from final energy consumption in Sichuan Province,so as to formulate corresponding emission reduction countermeasures from different paths according to the influencing factors.Based on the data of final energy consumption in Sichuan Province from 2010 to 2019,we calculated CO_(2)emission by the indirect emission calculation method.The influencing factors of CO_(2)emissions originating from final energy consumption in Sichuan Province were decomposed into population size,economic development,industrial structure,energy consumption intensity,and energy consumption structure by the Kaya-logarithmic mean Divisia index(LMDI)decomposition model.At the same time,grey correlation analysis was used to identify the correlation between CO_(2)emissions originating from final energy consumption and the influencing factors in Sichuan Province.The results showed that population size,economic development and energy consumption structure have positive contributions to CO_(2)emissions from final energy consumption in Sichuan Province,and economic development has a significant contribution to CO_(2)emissions from final energy consumption,with a contribution rate of 519.11%.The industrial structure and energy consumption intensity have negative contributions to CO_(2)emissions in Sichuan Province,and both of them have significant contributions,among which the contribution rate of energy consumption structure was 325.96%.From the perspective of industrial structure,secondary industry makes significant contributions and will maintain a restraining effect;from the perspective of energy consumption structure,industry sector has a significant contribution.The results of this paper are conducive to the implementation of carbon emission reduction policies in Sichuan Province.