Energy Consumption Prediction of a CNC Machining Process With Incomplete Data

Energy consumption prediction of a CNC machining process is important for energy efficiency optimization strategies.To improve the generalization abilities,more and more parameters are acquired for energy prediction modeling.While the data collected from workshops may be incomplete because of misoperation,unstable network connections,and frequent transfers,etc.This work proposes a framework for energy modeling based on incomplete data to address this issue.First,some necessary preliminary operations are used for incomplete data sets.Then,missing values are estimated to generate a new complete data set based on generative adversarial imputation nets(GAIN).Next,the gene expression programming(GEP)algorithm is utilized to train the energy model based on the generated data sets.Finally,we test the predictive accuracy of the obtained model.Computational experiments are designed to investigate the performance of the proposed framework with different rates of missing data.Experimental results demonstrate that even when the missing data rate increases to 30%,the proposed framework can still make efficient predictions,with the corresponding RMSE and MAE 0.903 k J and 0.739 k J,respectively.

Energy consumption prediction model of typical buildings in hot summer and cold winter zone of China

To overcome the shortcomings of the energyconsumption prediction models in the application during thedesign stage, a quick prediction model for energy consumptionis proposed based on the decoupling method. Taking typicalresidential and office buildings in hot summer and cold winterzones as research objects, the influence factors on buildingenergy consumption are classified into intrinsic factors andoperational factors on the basis of the heat transfer principle.Then, using the intrinsic factors as the fundamental variablesand operational factors as the modified variables, the quickprediction model for the buildings in typical cold and hot zonesis proposed based on the decoupling method and the accuracyof the proposed model is verified. The results show thatcompared to the simulation results of EnergyPlus, the relativeerror of the prediction model is less than 1.5% ; comparedwith the real operating data of the building, the relative erroris 13.14% in 2011 and 8.56% in 2012 due to the fact that thecoincidence factor becomes larger than the design value about16% in 2011 and 13% in 2012. The finding reveals that theproposed model has the advantages of rapid calculationcompared with EnergyPlus and Design Builder when predictingbuilding energy consumption in building designs. The energyconsumption prediction model is of great practical value inoptimal operation and building designs.

Research on Prediction Methods of Energy Consumption Data

This paper analyzes the energy consumption situation in Beijing,based on the comparison of common energy consumption prediction methods.Here we use multiple linear regression analysis,grey prediction,BP neural net-work prediction,grey BP neural network prediction combined method,LSTM long-term and short-term memory network model prediction method.Firstly,before constructing the model,the whole model is explained theoretically.The advantages and disadvantages of each model are analyzed before the modeling,and the corresponding advantages and disadvantages of these models are pointed out.Finally,these models are used to construct the Beijing energy forecasting model,and some years are selected as test samples to test the prediction accuracy.Finally,all models were used to predict the development trend of Beijing's total energy consumption from 2018 to 2019,and the relevant energy-saving opinions were given.

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.

Generating Synthetic Data to Reduce Prediction Error of Energy Consumption

Renewable and nonrenewable energy sources are widely incorporated for solar and wind energy that produces electricity without increasing carbon dioxide emissions.Energy industries worldwide are trying hard to predict future energy consumption that could eliminate over or under contracting energy resources and unnecessary financing.Machine learning techniques for predicting energy are the trending solution to overcome the challenges faced by energy companies.The basic need for machine learning algorithms to be trained for accurate prediction requires a considerable amount of data.Another critical factor is balancing the data for enhanced prediction.Data Augmentation is a technique used for increasing the data available for training.Synthetic data are the generation of new data which can be trained to improve the accuracy of prediction models.In this paper,we propose a model that takes time series energy consumption data as input,pre-processes the data,and then uses multiple augmentation techniques and generative adversarial networks to generate synthetic data which when combined with the original data,reduces energy consumption prediction error.We propose TGAN-skip-Improved-WGAN-GP to generate synthetic energy consumption time series tabular data.We modify TGANwith skip connections,then improveWGANGPby defining a consistency term,and finally use the architecture of improved WGAN-GP for training TGAN-skip.We used various evaluation metrics and visual representation to compare the performance of our proposed model.We also measured prediction accuracy along with mean and maximum error generated while predicting with different variations of augmented and synthetic data with original data.The mode collapse problemcould be handled by TGAN-skip-Improved-WGAN-GP model and it also converged faster than existing GAN models for synthetic data generation.The experiment result shows that our proposed technique of combining synthetic data with original data could significantly reduce the prediction error rate and increase the prediction accuracy of energy consumption.

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.

Comprehensive optimized GM(1,1) model and application for short term forecasting of Chinese energy consumption and production

In order to improve prediction accuracy of the grey prediction model and forecast China energy consumption and production in a short term, this paper proposes a novel com- prehensively optimized GM（1,1） model, also named COGM（1,1）, based on the grey modeling mechanism. First, the relationship of the background value formula and its whitenization equation is analyzed and a new method optimizing background values is proposed to eliminate systemic errors in the modeling process. Second, the solving process of the new model is derived. For parameter estimation, a set of auxiliary parameters are used to change grey equation＇s form. Then, original parameters are re- stored by an equations system. After solving the whitenization equation, initial value in time response function is established by least errors criteria. Finally, a numerical case and comparison with other grey prediction models are made to testify the new model＇s effectiveness, and the computational results show that the COGM（1,1） model has a better property and achieves higher precision. The new model is used to forecast China energy con- sumption and production, and the ability of energy self-sufficiency is further analyzed. Results indicate that gaps between consump- tion and production in future are predicted to decline.

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.

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.

A Review of Energy-Related Cost Issues and Prediction Models in Cloud Computing Environments

With the expansion of cloud computing,optimizing the energy efficiency and cost of the cloud paradigm is considered significantly important,since it directly affects providers’revenue and customers’payment.Thus,providing prediction information of the cloud services can be very beneficial for the service providers,as they need to carefully predict their business growths and efficiently manage their resources.To optimize the use of cloud services,predictive mechanisms can be applied to improve resource utilization and reduce energy-related costs.However,such mechanisms need to be provided with energy awareness not only at the level of the Physical Machine(PM)but also at the level of the Virtual Machine(VM)in order to make improved cost decisions.Therefore,this paper presents a comprehensive literature review on the subject of energy-related cost issues and prediction models in cloud computing environments,along with an overall discussion of the closely related works.The outcomes of this research can be used and incorporated by predictive resource management techniques to make improved cost decisions assisted with energy awareness and leverage cloud resources efficiently.

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。

Intelligent Energy Consumption For Smart Homes Using Fused Machine-Learning Technique

Energy is essential to practically all exercises and is imperative for the development of personal satisfaction.So,valuable energy has been in great demand for many years,especially for using smart homes and structures,as individuals quickly improve their way of life depending on current innovations.However,there is a shortage of energy,as the energy required is higher than that produced.Many new plans are being designed to meet the consumer’s energy requirements.In many regions,energy utilization in the housing area is 30%–40%.The growth of smart homes has raised the requirement for intelligence in applications such as asset management,energy-efficient automation,security,and healthcare monitoring to learn about residents’actions and forecast their future demands.To overcome the challenges of energy consumption optimization,in this study,we apply an energy management technique.Data fusion has recently attracted much energy efficiency in buildings,where numerous types of information are processed.The proposed research developed a data fusion model to predict energy consumption for accuracy and miss rate.The results of the proposed approach are compared with those of the previously published techniques and found that the prediction accuracy of the proposed method is 92%,which is higher than the previously published approaches.

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.

Energy consumption dynamic prediction for HVAC systems based on feature clustering deconstruction and model training adaptation

The prediction of building energy consumption offers essential technical support for intelligent operation and maintenance of buildings,promoting energy conservation and low-carbon control.This paper focused on the energy consumption of heating,ventilation and air conditioning(HVAC)systems operating under various modes across different seasons.We constructed multi-attribute and high-dimensional clustering vectors that encompass indoor and outdoor environmental parameters,along with historical energy consumption data.To enhance the K-means algorithm,we employed statistical feature extraction and dimensional normalization(SFEDN)to facilitate data clustering and deconstruction.This method,combined with the gated recurrent unit(GRU)prediction model employing adaptive training based on the Particle Swarm Optimization algorithm,was evaluated for robustness and stability through k-fold cross-validation.Within the clustering-based modeling framework,optimal submodels were configured based on the statistical features of historical 24-hour data to achieve dynamic prediction using multiple models.The dynamic prediction models with SFEDN cluster showed a 11.9%reduction in root mean square error(RMSE)compared to static prediction,achieving a coefficient of determination(R2)of 0.890 and a mean absolute percentage error(MAPE)reduction of 19.9%.When compared to dynamic prediction based on single-attribute of HVAC systems energy consumption clustering modeling,RMSE decreased by 12.6%,R2 increased by 4.0%,and MAPE decreased by 26.3%.The dynamic prediction performance demonstrated that the SFEDN clustering method surpasses conventional clustering method,and multi-attribute clustering modeling outperforms single-attribute modeling.

Federated learning-based short-term building energy consumption prediction method for solving the data silos problem

Transfer learning is an effective method to predict the energy consumption of information-poor buildings by learning transferable knowledge from operational data of information-rich buildings.However,it is not recommended to directly use the operational data without protection due to the risk of leaking occupants’privacy.To address this problem,this study proposes a federated learning-based method to learn transferable knowledge from building operational data without privacy leaking.It trains a transferable federated model based on the operational data from the buildings similar to the target building with limited data.An advanced secure aggregation algorithm is adopted in the training process to ensure that no one can infer private information from the training data.The federated model obtained is evaluated by comparing it with the standalone model without federated learning based on 13 similar office buildings from the Building Data Genome Project.The results show that the federated model outperforms the standalone model concerning the prediction accuracy and training time.On average,the federated model achieves a 25.4%decrease in CV-RMSE when the target building has limited operational data.Even if the target building has no operational data,the federated model still achieves acceptable accuracy(CV-RMSE is 22.2%).Meanwhile,the training time of the federated model is 90%less than that of the standalone model.The research insights can help develop federated learning-based methods for solving the data silos problem in building energy management.The methodology and analysis procedures are reproducible and all codes and data sets are available on Github.

Combined Prediction for Vehicle Speed with Fixed Route

Achieving accurate speed prediction provides the most critical support parameter for high-level energy management of plug-in hybrid electric vehicles.Nowadays,people often drive a vehicle on fixed routes in their daily travels and accurate speed predictions of these routes are possible with random prediction and machine learning,but the prediction accuracy still needs to be improved.The prediction accuracy of traditional prediction algorithms is difficult to further improve after reaching a certain accuracy;problems,such as over fitting,occur in the process of improving prediction accuracy.The combined prediction model proposed in this paper can abandon the transitional dependence on a single prediction.By combining the two prediction algorithms,the fusion of prediction performance is achieved,the limit of the single prediction performance is crossed,and the goal of improving vehicle speed prediction performance is achieved.In this paper,an extraction method suitable for fixed route vehicle speed is designed.The application of Markov and back propagation(BP)neural network in predictions is introduced.Three new combined prediction methods,all named Markov and BP Neural Network(MBNN)combined prediction algorithm,are proposed,which make full use of the advantages of Markov and BP neural network algorithms.Finally,the comparison among the prediction methods has been carried out.The results show that the three MBNN models have improved by about 19%,28%,and 29%compared with the Markov prediction model,which has better performance in the single prediction models.Overall,the MBNN combined prediction models can improve the prediction accuracy by 25.3%on average,which provides important support for the possible optimization of plug-in hybrid electric vehicle energy consumption.

Predicting Energy Consumption Using Stacked LSTM Snapshot Ensemble

The ability to make accurate energy predictions while considering all related energy factors allows production plants,regulatory bodies,and governments to meet energy demand and assess the effects of energy-saving initiatives.When energy consumption falls within normal parameters,it will be possible to use the developed model to predict energy consumption and develop improvements and mitigating measures for energy consumption.The objective of this model is to accurately predict energy consumption without data limitations and provide results that are easily interpretable.The proposed model is an implementation of the stacked Long Short-Term Memory(LSTM)snapshot ensemble combined with the Fast Fourier Transform(FFT)and meta-learner.Hebrail and Berard’s Individual Household Electric-Power Consumption(IHEPC)dataset incorporated with weather data are used to analyse the model’s accuracy with predicting energy consumption.The model is trained,and the results measured using Root Mean Square Error(RMSE),Mean Absolute Error(MAE),Mean Absolute Percentage Error(MAPE),and coefficient of determination(R^(2))metrics are 0.020,0.013,0.017,and 0.999,respectively.The stacked LSTM snapshot ensemble performs better than the compared models based on prediction accuracy and minimized errors.The results of this study show that prediction accuracy is high,and the model’s stability is high as well.The model shows that high levels of accuracy prove accurate predictive ability,and together with high levels of stability,the model has good interpretability,which is not typically accounted for in models.However,this study shows that it can be inferred.

Energy production and consumption prediction and their response to environment based on coupling model in China

The paper presents the prediction of total energy production and consumption in all provinces and autonomous regions as well as determination of the variation of gravity center of the energy production, consumption and total discharge of industrial waste water, gas and residue of China via the energy and environmental quality data from 1978 to 2009 in China by use of GM（1,1） model and gravity center model, based on which the paper also analyzes the dynamic variation in regional difference in energy production, consumption and environmental quality and their relationship. The results are shown as follows. 1） The gravity center of energy production is gradually moving southwestward and the entire movement track approxi-mates to linear variation, indicating that the difference of energy production between the east and west, south and north is narrowing to a certain extent, with the difference between the east and the west narrowing faster than that between the south and the north. 2） The gravity center of energy consumption is moving southwestward with perceptible fluctuation, of which the gravity center position from 2000 to 2005 was relatively stable, with slight annual position variation, indicating that the growth rates of all provinces and autonomous regions are basically the same. 3） The gravity center of the total discharge of industrial waste water, gas and residue is characterized by fluctuation in longitude and latitude to a certain degree. But, it shows a southwestward trend on the whole. 4） There are common ground and discrepancy in the variation track of the gravity center of the energy production consumption of China, and the comparative analysis of the gravity center of them and that of total discharge of industrial waste water, gas and residue shows that the environmental quality level is closely associated with the energy production and consumption （especially the energy consumption）, indicating that the environment cost in economy of energy is higher in China.

基于高阶特征挖掘和贝叶斯MCMC方法的建筑能耗预测

针对建筑能耗预测模型需要解决解释变量选取困难、建筑样本数据有限和建模算法难以捕捉复杂的非线性关系等问题,提出了基于高阶特征挖掘和贝叶斯MCMC(马尔可夫链蒙特卡罗)方法的能耗预测模型。本研究利用建筑本体、建筑环境及建筑人员活动等特征指标作为模型输入,以CBECS数据库中205栋办公建筑的供热能耗数据作为训练集样本,选用符号检验与K-fold交叉验证识别高响应交互作用因子组合,通过MCMC方法进行重要性采样,预测了办公建筑的供热能耗强度。研究发现,建筑的层高、面积、层数之间存在高阶交互关系,模型预测精确度指标归一化平均偏差(NMBE)为3.5%,均方根误差(RMSE)为0.014,均方根误差变异系数(CVRMSE)为14.0%,可有效预测办公建筑供热能耗。

An energy consumption prediction approach of die casting machines driven by product parameters

Die casting machines,which are the core equipment of the machinery manufacturing industry,consume great amounts of energy.The energy consumption prediction of die casting machines can support energy consumption quota,process parameter energy-saving optimization,energy-saving design,and energy efficiency evaluation;thus,it is of great significance for Industry 4.0 and green manufacturing.Nevertheless,due to the uncertainty and complexity of the energy consumption in die casting machines,there is still a lack of an approach for energy consumption prediction that can provide support for process parameter optimization and product design taking energy efficiency into consideration.To fill this gap,this paper proposes an energy consumption prediction approach for die casting machines driven by product parameters.Firstly,the system boundary of energy consumption prediction is defined,and subsequently,based on the energy consumption characteristics analysis,a theoretical energy consumption model is established.Consequently,a systematic energy consumption prediction approach for die casting machines,involving product,die,equipment,and process parameters,is proposed.Finally,the feasibility and reliability of the proposed energy consumption prediction approach are verified with the help of three die casting machines and six types of products.The results show that the prediction accuracy of production time and energy consumption reached 91.64%and 85.55%,respectively.Overall,the proposed approach can be used for the energy consumption prediction of different die casting machines with different products.