Applying multivariate linear regression and multi-layer perceptron artificial neural network to design an energy consumption baseline in a low density polyethylene plant

2019 ◽  
Vol 13 (4) ◽  
pp. 1133-1148
Author(s):  
Behnam Hamedi ◽  
Alireza Mokhtar
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Energy Consumption ◽  
Linear Regression ◽  
Energy Management ◽  
Energy Performance ◽  
Multivariate Linear Regression ◽  
Multi Layer Perceptron ◽  
Content Type ◽  
Artificial Neural

Purpose The purpose of this study is to investigate and analysis of energy consumption for this industry. The core part of any energy management system (EnMS) in industry is to perfectly monitor the energy consumption of significant users and to continuously improve the energy performance. In petrochemical plants, production deals with energy-intensive processes, and measuring energy performance for recognition and assessment of potentials for saving is critical. Design/methodology/approach The required data are exploited for the period of March 2011-August 2016 (data set: 2,012 days). Multivariate linear regression (MLR) and multi-layer perceptron artificial neural network (ANN) methods are separately used to anticipate the energy consumption. The baseline will be assumed as a reference to be compared with the actual data to estimate the real saving values. Finally, cumulative summations (CUSUM) are proposed and applied as an effective indicator for measurement of energy performance in an LDPE. Findings In this study, two statistical methods of MLR and ANN were used to design and develop a comprehensive energy baseline representing the predicted amounts of energy consumption based on the recognized drivers. Although both models imply robust outcomes, when the relative errors are taken into account, performance of ANN models appears fairly superior compared to the MLR model. Originality/value It is highly suggested to the ISO technical committee dealing with energy management standards, to consider the proposed model for baseline development in the future version of the standard ISO 50006 as the supplementary extension for the ISO 50001 for measuring energy performance using EnB and EnPI. As for future studies, the research can be extended to investigate the uncertainty and the model could also become completed applying more advanced ANNs such as recurrent neural networks.

Integrating artificial neural network and imperialist competitive algorithm (ICA), to predict the energy consumption for land leveling

2017 ◽  
Vol 11 (4) ◽  
pp. 522-540 ◽  
Author(s):  
Isham Alzoubi ◽  
Mahmoud Delavar ◽  
Farhad Mirzaei ◽  
Babak Nadjar Arrabi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Energy Consumption ◽  
Compressibility Factor ◽  
Imperialist Competitive Algorithm ◽  
Content Type ◽  
Competitive Algorithm ◽  
Land Leveling ◽  
Soil Compressibility ◽  
Artificial Neural

Purpose This work aims to determine the best linear model using an artificial neural network (ANN) with the imperialist competitive algorithm (ICA-ANN) and ANN to predict the energy consumption for land leveling. Design/methodology/approach Using ANN, integrating artificial neural network and imperialist competitive algorithm (ICA-ANN) and sensitivity analysis (SA) can lead to a noticeable improvement in the environment. In this research, effects of various soil properties such as embankment volume, soil compressibility factor, specific gravity, moisture content, slope, sand per cent and soil swelling index on energy consumption were investigated. Findings According to the results, 10-8-3-1, 10-8-2-5-1, 10-5-8-10-1 and 10-6-4-1 multilayer perceptron network structures were chosen as the best arrangements and were trained using the Levenberg–Marquardt method as the network training function. Sensitivity analysis revealed that only three variables, namely, density, soil compressibility factor and cut-fill volume (V), had the highest sensitivity on the output parameters, including labor energy, fuel energy, total machinery cost and total machinery energy. Based on the results, ICA-ANN had a better performance in the prediction of output parameters in comparison with conventional methods such as ANN or particle swarm optimization (PSO)-ANN. Statistical factors of root mean square error (RMSE) and correlation coefficient (R2) illustrate the superiority of ICA-ANN over other methods by values of about 0.02 and 0.99, respectively. Originality/value A limited number of research studies related to energy consumption in land leveling have been done on energy as a function of volume of excavation and embankment. However, in this research, energy and cost of land leveling are shown to be functions of all the properties of the land, including the slope, coefficient of swelling, density of the soil, soil moisture and special weight dirt. Therefore, the authors believe that this paper contains new and significant information adequate for justifying publication in an international journal.

Dimensional Prediction for FDM Machines Using Artificial Neural Network and Support Vector Regression

2019 ◽  
Author(s):  
Jiaqi Lyu ◽  
Souran Manoochehri
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Linear Regression ◽  
Support Vector Regression ◽  
Process Parameters ◽  
Multivariate Linear Regression ◽  
Support Vector ◽  
Ann Model ◽  
Fused Deposition ◽  
Artificial Neural

Abstract With the development of Fused Deposition Modeling (FDM) technology, the quality of fabricated parts is getting more attention. The present study highlights the predictive model for dimensional accuracy in the FDM process. Three process parameters, namely extruder temperature, layer thickness, and infill density, are considered in the model. To achieve better prediction accuracy, three models are studied, namely multivariate linear regression, Artificial Neural Network (ANN), and Support Vector Regression (SVR). The models are used to characterize the complex relationship between the input variables and dimensions of fabricated parts. Based on the experimental data set, it is found that the ANN model performs better than the multivariate linear regression and SVR models. The ANN model is able to study more quality characteristics of fabricated parts with more process parameters of FDM.

scholarly journals A comparative analysis of artificial neural network architectures for building energy consumption forecasting

2019 ◽  
Vol 15 (9) ◽  
pp. 155014771987761 ◽  
Author(s):  
Jihoon Moon ◽  
Sungwoo Park ◽  
Seungmin Rho ◽  
Eenjun Hwang
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Network ◽  
Energy Consumption ◽  
Energy Management ◽  
Smart Grids ◽  
Load Forecasting ◽  
Energy Management Strategy ◽  
Forecasting Models ◽  
Artificial Neural

Smart grids have recently attracted increasing attention because of their reliability, flexibility, sustainability, and efficiency. A typical smart grid consists of diverse components such as smart meters, energy management systems, energy storage systems, and renewable energy resources. In particular, to make an effective energy management strategy for the energy management system, accurate load forecasting is necessary. Recently, artificial neural network–based load forecasting models with good performance have been proposed. For accurate load forecasting, it is critical to determine effective hyperparameters of neural networks, which is a complex and time-consuming task. Among these parameters, the type of activation function and the number of hidden layers are critical in the performance of neural networks. In this study, we construct diverse artificial neural network–based building electric energy consumption forecasting models using different combinations of the two hyperparameters and compare their performance. Experimental results indicate that neural networks with scaled exponential linear units and five hidden layers exhibit better performance, on average than other forecasting models.

scholarly journals Ambient air pollution and cardiovascular disease rate an ANN modeling: Yazd-Central of Iran

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Mahrokh Jalili ◽  
Mohammad Hassan Ehrampoush ◽  
Mehdi Mokhtari ◽  
Ali Asghar Ebrahimi ◽  
Faezeh Mazidi ◽  
...  
Keyword(s):  
Neural Network ◽  
Cardiovascular Disease ◽  
Time Series ◽  
Artificial Neural Network ◽  
Linear Regression ◽  
Air Pollutants ◽  
Meteorological Parameters ◽  
Ambient Air ◽  
Multivariate Linear Regression ◽  
Artificial Neural

AbstractThis study was aimed to investigate the air pollutants impact on heart patient's hospital admission rates in Yazd for the first time. Modeling was done by time series, multivariate linear regression, and artificial neural network (ANN). During 5 years, the mean concentrations of PM10, SO2, O3, NO2, and CO were 98.48 μg m−3, 8.57 ppm, 19.66 ppm, 18.14 ppm, and 4.07 ppm, respectively. The total number of cardiovascular disease (CD) patients was 12,491, of which 57% and 43% were related to men and women, respectively. The maximum correlation of air pollutants was observed between CO and PM10 (R = 0.62). The presence of SO2 and NO2 can be dependent on meteorological parameters (R = 0.48). Despite there was a positive correlation between age and CD (p = 0.001), the highest correlation was detected between SO2 and CD (R = 0.4). The annual variation trend of SO2, NO2, and CO concentrations was more similar to the variations trend in meteorological parameters. Moreover, the temperature had also been an effective factor in the O3 variation rate at lag = 0. On the other hand, SO2 has been the most effective contaminant in CD patient admissions in hospitals (R = 0.45). In the monthly database classification, SO2 and NO2 were the most prominent factors in the CD (R = 0.5). The multivariate linear regression model also showed that CO and SO2 were significant contaminants in the number of hospital admissions (R = 0.46, p = 0.001) that both pollutants were a function of air temperature (p = 0.002). In the ANN nonlinear model, the 14, 12, 10, and 13 neurons in the hidden layer were formed the best structure for PM, NO2, O3, and SO2, respectively. Thus, the Rall rate for these structures was 0.78–0.83. In these structures, according to the autocorrelation of error in lag = 0, the series are stationary, which makes it possible to predict using this model. According to the results, the artificial neural network had a good ability to predict the relationship between the effect of air pollutants on the CD in a 5 years' time series.

scholarly journals Artificial neural network based technique for energy management prediction

2020 ◽  
Vol 17 (1) ◽  
pp. 94
Author(s):  
N. Ab. Wahab ◽  
Z. Mat Yasin ◽  
N. A. Salim ◽  
N. F. A. Aziz
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Energy Consumption ◽  
Power Consumption ◽  
Energy Management ◽  
Electrical Machine ◽  
Electricity Cost ◽  
Artificial Neural ◽  
Low Efficiency ◽  
Artificial Neural Network Ann

<p>The energy management of electrical machine is significant to ensure efficient power consumption. Mismanagement of energy consumption could give impact on low efficiency of energy consumption that leads to power wastage.  This paper presents analysis of power consumption and electricity costing of the electrical machineries and equipment in High Voltage (HV) and Electrical Machine (EM) Laboratories at Faculty of Electrical Engineering (FKE), Universiti Teknologi MARA (UiTM) Shah Alam, Selangor, Malaysia. The electrical data are collected using Fluke Meter 1750. Based on the analysis, it is found that the estimated annually electricity cost for HV Laboratory and EM Laboratory are RM 392.00 and RM 3197.76 respectively. For prediction of energy consumption of the two laboratories, Artificial Neural Network (ANN) algorithm is applied as computational tool using feedforward network type. The results show that the ANN is successfully modelled to predict the energy consumption.</p>

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