Ensemble Neural Network Model for Predicting the Energy Consumption of a Milling Machine

2015 ◽  
Author(s):  
Ronay Ak ◽  
Moneer M. Helu ◽  
Sudarsan Rachuri
Keyword(s):  
Energy Consumption ◽  
Prediction Model ◽  
Prediction Models ◽  
Production Systems ◽  
Face Milling ◽  
Model Parameters ◽  
Efficient Production ◽  
Milling Machine ◽  
Energy Prediction

Accurate prediction of the energy consumption is critical for energy-efficient production systems. However, the majority of existing prediction models aim at providing only point predictions and can be affected by uncertainties in the model parameters and input data. In this paper, a prediction model that generates prediction intervals (PIs) for estimating energy consumption of a milling machine is proposed. PIs are used to provide information on the confidence in the prediction by accounting for the uncertainty in both the model parameters and the noise in the input variables. An ensemble model of neural networks (NNs) is used to estimate PIs. A k-nearest-neighbors (k-nn) approach is applied to identify similar patterns between training and testing sets to increase the accuracy of the results by using local information from the closest patterns of the training sets. Finally, a case study that uses a dataset obtained by machining 18 parts through face-milling, contouring, slotting and pocketing, spiraling, and drilling operations is presented. Of these six operations, the case study focuses on face milling to demonstrate the effectiveness of the proposed energy prediction model.

A Generalized Data-Driven Energy Prediction Model With Uncertainty for a Milling Machine Tool Using Gaussian Process

2015 ◽  
Author(s):  
Jinkyoo Park ◽  
Kincho H. Law ◽  
Raunak Bhinge ◽  
Nishant Biswas ◽  
Amrita Srinivasan ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Prediction Model ◽  
Machine Tool ◽  
Gaussian Process ◽  
Prediction Models ◽  
Numerical Control ◽  
Data Driven ◽  
Machining Parameters ◽  
Milling Machine ◽  
Energy Prediction

Using a machine learning approach, this study investigates the effects of machining parameters on the energy consumption of a milling machine tool, which would allow selection of optimal operational strategies to machine a part with minimum energy. Data-driven prediction models, built upon a nonlinear regression approach, can be used to gain an understanding of the effects of machining parameters on energy consumption. In this study, we use the Gaussian Process to construct the energy prediction model for a computer numerical control (CNC) milling machine tool. Energy prediction models for different machining operations are constructed based on collected data. With the collected data sets, optimum input features for model selection are identified. We demonstrate how the energy prediction models can be used to compare the energy consumption for the different operations and to estimate the total energy usage for machining a generic part. We also present an uncertainty analysis to develop confidence bounds for the prediction model and to provide insight into the vast parameter space and training required to improve the accuracy of the model. Generic parts are machined to test and validate the prediction model constructed using the Gaussian Process and we consistently achieve an accuracy of over 95 % on the total predicted energy.

scholarly journals Toward a Generalized Energy Prediction Model for Machine Tools

2016 ◽  
Vol 139 (4) ◽  
Author(s):  
Raunak Bhinge ◽  
Jinkyoo Park ◽  
Kincho H. Law ◽  
David A. Dornfeld ◽  
Moneer Helu ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Prediction Model ◽  
Machine Tool ◽  
Process Planning ◽  
Machine Tools ◽  
Prediction Models ◽  
Machining Process ◽  
Data Driven ◽  
Process Data ◽  
Energy Prediction

Energy prediction of machine tools can deliver many advantages to a manufacturing enterprise, ranging from energy-efficient process planning to machine tool monitoring. Physics-based energy prediction models have been proposed in the past to understand the energy usage pattern of a machine tool. However, uncertainties in both the machine and the operating environment make it difficult to predict the energy consumption of the target machine reliably. Taking advantage of the opportunity to collect extensive, contextual, energy-consumption data, we discuss a data-driven approach to develop an energy prediction model of a machine tool in this paper. First, we present a methodology that can efficiently and effectively collect and process data extracted from a machine tool and its sensors. We then present a data-driven model that can be used to predict the energy consumption of the machine tool for machining a generic part. Specifically, we use Gaussian process (GP) regression, a nonparametric machine-learning technique, to develop the prediction model. The energy prediction model is then generalized over multiple process parameters and operations. Finally, we apply this generalized model with a method to assess uncertainty intervals to predict the energy consumed by any part of the machine using a Mori Seiki NVD1500 machine tool. Furthermore, the same model can be used during process planning to optimize the energy-efficiency of a machining process.

scholarly journals Modeling and Optimization for Piercing Efficiency and Energy Consumption Based on Mean Value Substaged KELM-PLS and GA Method

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Dong Xiao ◽  
Jichun Wang
Keyword(s):  
Energy Consumption ◽  
Prediction Model ◽  
Prediction Models ◽  
Optimization Method ◽  
Mean Value ◽  
Genetic Optimization ◽  
Simulation And Experiment ◽  
Coupling Characteristics ◽  
Energy Consumption Prediction ◽  
Consumption Prediction

Piercing manufacture of seamless tubes is the process that pierces solid blank into tube hollow. Piercing efficiency and energy consumption are the important indexes in the production of seamless tubes. Piercing process has the multivariate, nonlinear, cross-coupling characteristics. The complex factors that affect efficiency and consumption make it difficult to establish the mechanism models for optimization. Based on the production process, this paper divides the piercing process into three parts and proposes the piercing efficiency and energy consumption prediction models based on mean value staged KELM-PLS method. On the basis of mean value staged KELM-PLS prediction model, the minimum piercing energy consumption and maximum piercing efficiency are calculated by genetic optimization algorithm. Simulation and experiment prove that the optimization method based on the piercing efficiency and energy consumption prediction model can obtain the optimal process parameters effectively and also provide reliable evidences for practical production.

Building Energy Prediction Using Artificial Neural Networks (LSTM)

2020 ◽  
Author(s):  
Sankhanil Goswami
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Energy Consumption ◽  
Energy Use ◽  
Prediction Models ◽  
Short Term Memory ◽  
Significant Proportion ◽  
Cooling Load ◽  
Energy Prediction ◽  
Artificial Neural

Abstract Modern buildings account for a significant proportion of global energy consumption worldwide. Therefore, accurate energy use forecast is necessary for energy management and conservation. With the advent of smart sensors, a large amount of accurate energy data is available. Also, with the advancements in data analytics and machine learning, there have been numerous studies on developing data-driven prediction models based on Artificial Neural Networks (ANNs). In this work a type of ANN called Large Short-Term Memory (LSTM) is used to predict the energy use and cooling load of an existing building. A university administrative building was chosen for its typical commercial environment. The network was trained with one year of data and was used to predict the energy consumption and cooling load of the following year. The mean absolute testing error for the energy consumption and the cooling load were 0.105 and 0.05. The percentage mean accuracy was found to be 92.8% and 96.1%. The process was applied to several other buildings in the university and similar results were obtained. This indicates the model can successfully predict the energy consumption and cooling load for the buildings studied. The further improvement and application of this technique for optimizing building performance are also explored.

Prediction of Field Aging Gradient in Asphalt Pavements

2015 ◽  
Vol 2507 (1) ◽  
pp. 19-28 ◽  
Author(s):  
Xue Luo ◽  
Fan Gu ◽  
Robert L. Lytton
Keyword(s):  
Activation Energy ◽  
Prediction Model ◽  
Prediction Models ◽  
Fast Rate ◽  
Chemical Properties ◽  
Asphalt Pavements ◽  
Model Parameters ◽  
Void Content ◽  
Constant Rate ◽  
Rate Period

The aging of asphalt pavements is a key factor that influences pavement performance. Aging can be characterized by laboratory tests and prediction models. Common aging prediction models use the change of physical or chemical properties of asphalt binders based on regression techniques or aging reaction kinetics. The objective of this study was to develop a kinetics-based aging prediction model for the mixture modulus gradient in asphalt pavements to study long-term in-service aging. The proposed model was composed of three submodels for baseline modulus, surface modulus, and aging exponent to define the change of the mixture modulus with pavement depth. The model used kinetic parameters (aging activation energy and preexponential factor) of asphalt mixtures and combined the two reaction rate periods (fast-rate and constant-rate). Laboratory-measured modulus gradients of 29 field cores at different ages were used to determine the model parameters. The laboratory testing condition was converted to the field condition at a given age and corresponding temperature by introducing the rheological activation energy to quantify the temperature dependence of field cores at each age. The end of the fast-rate period or the beginning of the constant-rate period was accurately identified to model these two periods and to determine the associated parameters separately. The results showed that the predictions matched well with the measurements and the calculated model parameters were verified. The proposed aging prediction model took into account the major factors that affect field aging speed of an asphalt pavement, such as the binder type, aggregate type, air void content, pavement depth, aging temperature, and aging time.

Short-Term Wind Power Dynamic Prediction Based on GA-BP Neural Network

2014 ◽  
Vol 986-987 ◽  
pp. 524-528 ◽  
Author(s):  
Ting Jing Ke ◽  
Min You Chen ◽  
Huan Luo
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Wind Power ◽  
Bp Neural Network ◽  
Prediction Models ◽  
Model Parameters ◽  
Short Term ◽  
Dynamic Prediction ◽  
Power Dynamic ◽  
Prediction Approach

This paper proposes a short-term wind power dynamic prediction model based on GA-BP neural network. Different from conventional prediction models, the proposed approach incorporates a prediction error adjusting strategy into neural network based prediction model to realize the function of model parameters self-adjusting, thus increase the prediction accuracy. Genetic algorithm is used to optimize the parameters of BP neural network. The wind power prediction results from different models with and without error adjusting strategy are compared. The comparative results show that the proposed dynamic prediction approach can provide more accurate wind power forecasting.

scholarly journals Useful Energy Prediction Model of a Lithium-ion Cell Operating on Various Duty Cycles

2021 ◽  
Author(s):  
Damian Burzyński
Keyword(s):  
Prediction Model ◽  
Gaussian Process Regression ◽  
Lithium Ion ◽  
Model Parameters ◽  
Battery Management ◽  
Acceptable Error ◽  
Energy Prediction ◽  
Depth Of Discharge ◽  
Duty Cycles ◽  
Lithium Ion Cell

The paper deals with the subject of the prediction of useful energy during the cycling of a lithium-ion cell (LIC), using machine learning-based techniques. It was demonstrated that depending on the combination of cycling parameters, the useful energy (<i>RUE<sub>c</sub></i>) that can be transfered during a full cycle is variable, and also three different types of evolution of changes in <i>RUE<sub>c</sub></i> were identified. The paper presents a new non-parametric <i>RUE<sub>c</sub></i> prediction model based on Gaussian process regression. It was proven that the proposed methodology enables the <i>RUE<sub>c</sub></i> prediction for LICs discharged, above the depth of discharge, at a level of 70% with an acceptable error, which is confirmed for new load profiles. Furthermore, techniques associated with explainable artificial intelligence were applied, for the first time, to determine the significance of model input parameters – the variable importance method – and to determine the quantitative effect of individual model parameters (their reciprocal interaction) on <i>RUE<sub>c</sub></i> – the accumulated local effects model of the first and second order. Not only is the <i>RUE<sub>c</sub></i> prediction methodology presented in the paper characterised by high prediction accuracy when using small learning datasets, but it also shows high application potential in all kinds of battery management systems.

scholarly journals Optimization Model Based on Multivariable Grey Model and Its Application to Energy Consumption in China

2021 ◽  
Author(s):  
Kai Xu ◽  
Xilin Luo ◽  
Xinyu Pang
Keyword(s):  
Energy Consumption ◽  
Natural Gas ◽  
Prediction Model ◽  
Prediction Models ◽  
Clean Energy ◽  
Energy Development ◽  
The Other ◽  
The Novel ◽  
Grey Prediction ◽  
Novel Model

Abstract Currently, the energy development in China is in a critical period of transformation and reform, facing unprecedented opportunities and challenges. Accurate energy consumption forecast is conducive to promoting the diversification of energy development and utilization, and ensuring the healthy and rapid development of China's economy. Based on the existing multivariable grey prediction model, a nonlinear multivariable grey prediction model with parameter optimization is established in this paper, which used the genetic algorithms to find the optimal parameters, and the modelling steps are obtained. Then, the novel model takes the oil natural gas, coal and clean energy in China as the research objects, and the results are compared with the other four grey prediction models. The novel model has higher simulation and prediction accuracy, which is better than the other four grey prediction models. Finally, the novel model is used to predict those four energy consumption forecasts in China from 2020 to 2024. The results show that various energy consumption will further increase, while the fastest growing is clean energy and natural gas, which provides effective information for the Chinese government to formulate energy economic policies.

scholarly journals Energy Consumption on Dairy Farms: A Review of Monitoring, Prediction Modelling, and Analyses

Energies ◽  
2020 ◽  
Vol 13 (5) ◽  
pp. 1288 ◽  
Author(s):  
Philip Shine ◽  
John Upton ◽  
Paria Sefeedpari ◽  
Michael D. Murphy
Keyword(s):  
Energy Consumption ◽  
Environmental Sustainability ◽  
Prediction Models ◽  
Farming Systems ◽  
Primary Energy ◽  
Dairy Farming ◽  
Machine Learning Algorithms ◽  
Energy Prediction ◽  
Prediction Modelling ◽  
The Impact

The global consumption of dairy produce is forecasted to increase by 19% per person by 2050. However, milk production is an intense energy consuming process. Coupled with concerns related to global greenhouse gas emissions from agriculture, increasing the production of milk must be met with the sustainable use of energy resources, to ensure the future monetary and environmental sustainability of the dairy industry. This body of work focused on summarizing and reviewing dairy energy research from the monitoring, prediction modelling and analyses point of view. Total primary energy consumption values in literature ranged from 2.7 MJ kg−1 Energy Corrected Milk on organic dairy farming systems to 4.2 MJ kg−1 Energy Corrected Milk on conventional dairy farming systems. Variances in total primary energy requirements were further assessed according to whether confinement or pasture-based systems were employed. Overall, a 35% energy reduction was seen across literature due to employing a pasture-based dairy system. Compared to standard regression methods, increased prediction accuracy has been demonstrated in energy literature due to employing various machine-learning algorithms. Dairy energy prediction models have been frequently utilized throughout literature to conduct dairy energy analyses, for estimating the impact of changes to infrastructural equipment and managerial practices.

scholarly journals Energy Flexibility Prediction for Data Center Engagement in Demand Response Programs

2020 ◽  
Vol 12 (4) ◽  
pp. 1417 ◽  
Author(s):  
Andreea Valeria Vesa ◽  
Tudor Cioara ◽  
Ionut Anghel ◽  
Marcel Antal ◽  
Claudia Pop ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Demand Response ◽  
Prediction Error ◽  
Energy Demand ◽  
Business Processes ◽  
Prediction Models ◽  
Percentage Error ◽  
Energy Prediction ◽  
Demand Prediction ◽  
Optimal Ensemble

In this paper, we address the problem of the efficient and sustainable operation of data centers (DCs) from the perspective of their optimal integration with the local energy grid through active participation in demand response (DR) programs. For DCs’ successful participation in such programs and for minimizing the risks for their core business processes, their energy demand and potential flexibility must be accurately forecasted in advance. Therefore, in this paper, we propose an energy prediction model that uses a genetic heuristic to determine the optimal ensemble of a set of neural network prediction models to minimize the prediction error and the uncertainty concerning DR participation. The model considers short term time horizons (i.e., day-ahead and 4-h-ahead refinements) and different aspects such as the energy demand and potential energy flexibility (the latter being defined in relation with the baseline energy consumption). The obtained results, considering the hardware characteristics as well as the historical energy consumption data of a medium scale DC, show that the genetic-based heuristic improves the energy demand prediction accuracy while the intra-day prediction refinements further reduce the day-ahead prediction error. In relation to flexibility, the prediction of both above and below baseline energy flexibility curves provides good results for the mean absolute percentage error (MAPE), which is just above 6%, allowing for safe DC participation in DR programs.

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