Group Method Of Data Handling Algorithm Research Articles

Regression modeling and multi-objective optimization of rheological behavior of non-Newtonian hybrid antifreeze: Using different neural networks and evolutionary algorithms

The research used an artificial neural network (ANN) model to examine the rheological properties of hybrid non-Newtonian ferrofluids (HNFFs) composed of Fe-CuO, water, and ethylene glycol. The performance of neural network was optimized using seven regression methods (RMs), namely Group Method of Data Handling (GMDH), Decision Tree (D-Tree), Multi-Layer Perceptron (MLP), Support Vector Machine (SVM), Extreme Learning Machine (ELM), Radial Basis Function (RBF), and Multiple Linear Regression (MLR). The findings highlighted GMDH method's superior performance when compared to neural networks. R and RMSE values attained by GMDH for the objective function (OF) μnf were 0.99436 and 2.0135, respectively. For the torque function OF, the values were 0.97652 and 4.8952. Margin of difference (MOD) calculations across various algorithms, such as MLP, SVM, RBF, D-Tree, ELM, MLR, and GMDH-Algos revealed significant disparities, indicating GMDH's efficacy. Comparison of R, RMSD, and standard deviation values between GMDH and MLR algorithms further underscored performance discrepancies. Specific parameters for which NSGA II Algo was rated highest among evaluation indices were as follows: a crossover rate of 0.7, a mutation rate of 0.02, a population size of 50, and 500 generations. Post-optimization, optimal values for μnf and torque (To) were determined as 6.595 and 3.543, respectively, with corresponding values for φ, T, and γ obtained as 0.185, 49.372, and 3.163, respectively. This comprehensive analysis sheds light on the effectiveness of various regression methods in modeling the rheological behavior of hybrid non-Newtonian ferrofluids, contributing to advancements in fluid dynamics research.

On the Evaluation of Coal Strength Alteration Induced by CO2 Injection Using Advanced Black-Box and White-Box Machine Learning Algorithms

Summary The injection of carbon dioxide (CO2) into coal seams is a prominent technique that can provide carbon sequestration in addition to enhancing coalbed methane extraction. However, CO2 injection into the coal seams can alter the coal strength properties and their long-term integrity. In this work, the strength alteration of coals induced by CO2 exposure was modeled using 147 laboratory-measured unconfined compressive strength (UCS) data points and considering CO2 saturation pressure, CO2 interaction temperature, CO2 interaction time, and coal rank as input variables. Advanced white-box and black-box machine learning algorithms including Gaussian process regression (GPR) with rational quadratic kernel, extreme gradient boosting (XGBoost), categorical boosting (CatBoost), adaptive boosting decision tree (AdaBoost-DT), multivariate adaptive regression splines (MARS), K-nearest neighbor (KNN), gene expression programming (GEP), and group method of data handling (GMDH) were used in the modeling process. The results demonstrated that GPR-Rational Quadratic provided the most accurate estimates of UCS of coals having 3.53%, 3.62%, and 3.55% for the average absolute percent relative error (AAPRE) values of the train, test, and total data sets, respectively. Also, the overall determination coefficient (R2) value of 0.9979 was additional proof of the excellent accuracy of this model compared with other models. Moreover, the first mathematical correlations to estimate the change in coal strength induced by CO2 exposure were established in this work by the GMDH and GEP algorithms with acceptable accuracy. Sensitivity analysis revealed that the Spearman correlation coefficient shows the relative importance of the input parameters on the coal strength better than the Pearson correlation coefficient. Among the inputs, coal rank had the greatest influence on the coal strength (strong nonlinear relationship) based on the Spearman correlation coefficient. After that, CO2 interaction time and CO2 saturation pressure have shown relatively strong nonlinear relationships with model output, respectively. The CO2 interaction temperature had the smallest impact on coal strength alteration induced by CO2 exposure based on both Pearson and Spearman correlation coefficients. Finally, the leverage technique revealed that the laboratory database used for modeling CO2-induced strength alteration of coals was highly reliable, and the suggested GPR-Rational Quadratic model and GMDH correlation could be applied for predicting the UCS of coals exposed to CO2 with high statistical accuracy and reliability.

Prediction of the thermal behavior of multi-walled carbon nanotubes-CuO-CeO2 (20-40-40)/water hybrid nanofluid using different types of regressors and evolutionary algorithms for designing the best artificial neural network modeling

For conducting an analysis of the experimental data, it is imperative to establish a mathematical correlation between the input and output variables. This entails executing a curve fitting or regression procedure on the data, for which numerous methodologies exist. Within the scope of present investigation, the design variables encompass the solid volume fraction (φ) and temperature. Thermal conductivity (TC) of MWCNT-CuO-CeO2 (20-40-40)/water hybrid nanofluid (HNF) is also the objective function. Ten different types of regressors are utilized for regression operations which are Multiple Linear Regression (MLR), Decision Tree (D-Tree), Multi-Layer Perceptron (MLP), Support Vector Machine (SVM), Extreme Learning Machine (ELM), Radial Basis Function (RBF), Adaptive Neuro-Fuzzy Inference System (ANFIS), Gaussian Process Regression (GPR), Multivariate Polynomial Regression (MPR) and Group Method of Data Handling (GMDH). Once the governing equations linking the design variables and the objective functions have been established, these equations can be employed to forecast the simulation data. By substituting the above input values into the equations, we can calculate the corresponding output values for the TC of the HNF. The results obtained from the MPR algorithm are compared to the experimental data. For the GPR, MLR, D-Tree, ELM, MPR, MLP, RBF, SVM, ANFIS, and GMDH algorithms, the maximum margin of error is found to be 0.031, 0.02579, 0.028946, 0.033889, 0.01568, 0.02515, 0.03485, 0.03, 0.0385, and 0.0178, respectively. Moreover, the kernel density estimation diagram indicates the gap between experimental data and data predicted by regression algorithms. Finally, it is evident that the MPR algorithm demonstrates to have a reduced residual dispersion, with the residuals approaching zero.

Using machine learning to predict processes and morphometric features of watershed

The research aims to classify alluvial fans’ morphometric properties using the SOM algorithm. It also determines the relationship between morphometric characteristics and erosion rate and lithology using the GMDH algorithm. For this purpose, alluvial fans of 4 watersheds in Iran are extracted semi-automatically using GIS and digital elevation model (DEM) analysis. The relationships between 25 morphometric features of these watersheds, the amount of erosion, and formation material are investigated using the self-organizing map (SOM) method. Principal component analysis (PCA), Greedy, Best first, Genetic search, Random search as feature selection algorithms are used to select the most important parameters affecting erosion and formation material. The group method of data handling (GMDH) algorithm is employed to predict erosion and formation material based on morphometries. The results indicated that the semi-automatic method in GIS could detect alluvial fans. The SOM algorithm determined that the morphometric factors affecting the formation material were fan length, minimum height of fan, and minimum fan slope. The main factors affecting erosion were fan area (Af) and minimum fan height (Hmin-f). The feature selection algorithm identified (Hmin-f), maximum fan height (Hmax-f), minimum fan slope, and fan length (Lf) to be the morphometries most important for determining formation material, and basin area, fan area, (Hmax-f) and compactness coefficient (Cirb) were the most important characteristics for determining erosion rates. The GMDH algorithm predicted the fan formation materials and rates of erosion with high accuracy (R2 = 0.94, R2 = 0.87).

Permeability prediction of heterogeneous carbonate gas condensate reservoirs applying group method of data handling

Carbonate petroleum reservoirs typically have lower permeabilities and recovery factors than sandstone reservoirs, so the natural fractures they often incorporate have positive impacts on resource recovery and fluid production rates. Quantifying effective permeability, incorporating contributions from pores and fractures, is therefore essential in the reservoir characterization and flow-regime modelling of carbonate reservoirs. This research applies a robust machine-learning forecasting model to predict permeability (K) for heterogeneous carbonate gas condensate reservoirs. A 212-point dataset from six gas-condensate carbonate reservoirs (Russia and Iran) is compiled. The input variables considered are porosity (Φ, %), specific surface area (Sp, 1/cm) and irreducible water saturation (Swir, %). These variables are assessed using four machine learning models: group method of data handling (GMDH), polynomial regression (PR), support vector machine (SVR), and decision tree (DT) to predict permeability. The GMDH algorithm, a polynomial neural network with a customized architecture is developed, such that it displays increased prediction accuracy and improved learning capabilities. All four models developed in this study substantially improve upon K predictions derived from established empirical correlations. The GMDH model also outperforms the other models in respect of K prediction accuracy using Φ, Swir, and Sp as input variables. It achieves permeability prediction accuracy for the multi-field dataset evaluated with a root mean squared error (RMSE) and coefficient of determination (R2) for the training and testing of the best model (GMDH) of RMSE = 9.2 mD and R2 = 0.9988; RMSE = 0.4 mD and R2 = 0.9972, respectively. The model can be readily adapted for application to other field datasets to estimate K from limited well-log and/or core data.

Sensitivity Analysis for Performance Evaluation of a Real Water Distribution System by a Pressure Driven Analysis Approach and Artificial Intelligence Method

Proper performance of water distribution networks (WDNs) plays a vital role in customer satisfaction. The aim of this study is to conduct a sensitivity analysis to evaluate the behavior of WDNs analyzed by a pressure-driven analysis (PDA) approach and the classification technique by using an appropriate artificial neural network, namely the Group Method of Data Handling (GMDH). For this purpose, this study is divided into four distinct steps. In the first and second steps, a real network has been analyzed by using a Pressure-Driven Analysis approach (PDA) to obtain the pressure, and α coefficient, the percentage of supplied flow. The analysis has been performed by using three different values of the design peak coefficient k*. In the third step, the Group Method of Data Handling (GMDH) has been applied and several binary models have been constructed. The analysis has been carried out by using input data, including the real topology of the network and the base demand necessary to satisfy requests of users in average conditions and by assuming that the demand in each single one-hour time step depends on a peak coefficient. Finally, the results obtained from the PDA hydraulic analysis and those obtained by using them in the GMDH algorithm have been compared and sensitivity analysis has been carried out. The innovation of the study is to demonstrate that the input parameters adopted in the design are correct. The analysis confirms that the GMDH algorithm gives proper results for this case study and the results are stable also when the value of each k*, characteristic of a different time hour step, varies in an admissible technical range. It was confirmed that the results obtained by using the PDA approach, analyzed by using a GMDH-type neural network, can provide higher performance sufficiency in the evaluation of WDNs.

Morphometry of AFs in upstream and downstream of floods in Gribayegan, Iran

This study aims to determine the effect of a flood-spreading system on the morphometric characteristics of alluvial fans (AFs) in Gribayegan Fasa, Iran, and its relationship with erosion, age, texture, and type of formations. After determining the AFs using the semiautomatic method and determining their recharged watersheds, 25 morphometric characteristics were investigated. The most important morphometric characteristics were identified using principal component analysis (PCA). The group method of data handling (GMDH) neural network is used to predict erosion, soil texture, age, and formation material based on the morphometric characteristics of the fan. The results demonstrate that the semiautomatic method can effectively extract AF from the landscape. The results of AF morphometry before and after flood spreading show that the fan area, drainage basin circularity (Cirb), fan perimeter, relief ratio of the fan, fan length, minimum fan height, and maximum fan height were higher before flood spreading, and basin shape, St (soil texture), upper fan slope, fan volume, and sweep angle had higher values after the flood. In addition, the results of PCA show that fan area, fan perimeter, fan length, fan radius, fan volume, basin area, basin perimeter, main channel length, basin length, and drainage density are important factors. The results of the GMDH algorithm reveal that this method can accurately predict the formation, age, soil texture, and erosion rate using morphometric characteristics. Therefore, the R2 is 0.92 for the formation age and R2 is 0.86 for the erosion rates, formation types, and soil texture, respectively. Therefore, the most important morphometric parameters can be determined using PCA, and the conditions and processes in the basin, such as formation material, age, soil texture, and erosion rate, can be predicted with high accuracy using the GMDH algorithm.

Development of Radio Propagation Path Loss Model for Kaduna Town, Nigeria Using GMDH Algorithm

Radio propagation measurement were acquired at the 900 MHz and 1800 MHz frequency bands from six (6) live base stations (BS1 to BS6) in Kaduna town, Nigeria using an Asus Zenfone enhanced with a network monitoring software (Network Cell Info Lite). The receive signal strength (RSS) measurements were taken from the BSs at a distances of 200 m apart (in dB) until the signal faded out and the measurements were taken for twelve (12) calendar months which covered all seasons of the year, the corresponding path loss were calculated which were subsequently used to develop a propagation path loss prediction model with the Group Method of Data Handling (GMDH) algorithm. However, the results obtained shows very small variations between the model fit (which was the best fit curve from the measured data) and the predictions (which is the forecast). Hence, since the variations between the model fit and the predictions are not wide, with sometime the values of prediction being better than that model fit, the GMDH model is showing good prediction for Kaduna metropolis.

Development of Radio Propagation Path Loss Model for Kaduna Town, Nigeria Using GMDH Algorithm

Radio propagation measurement were acquired at the 900 MHz and 1800 MHz frequency bands from six (6) live base stations (BS1 to BS6) in Kaduna town, Nigeria using an Asus Zenfone enhanced with a network monitoring software (Network Cell Info Lite). The receive signal strength (RSS) measurements were taken from the BSs at a distances of 200 m apart (in dB) until the signal faded out and the measurements were taken for twelve (12) calendar months which covered all seasons of the year, the corresponding path loss were calculated which were subsequently used to develop a propagation path loss prediction model with the Group Method of Data Handling (GMDH) algorithm. However, the results obtained shows very small variations between the model fit (which was the best fit curve from the measured data) and the predictions (which is the forecast). Hence, since the variations between the model fit and the predictions are not wide, with sometime the values of prediction being better than that model fit, the GMDH model is showing good prediction for Kaduna metropolis.

Design of a virtual test cell based on GMDH-type neural network for a heavy-duty diesel engine

The engine development process faces big challenges from new strict emission regulations in addition to the need for fuel efficiency improvements. The Software-in-the-Loop (SiL) and Hardware-in-the-Loop (HiL) environments decreases the required time during engine development, calibration, verification, and validation of the product. An accurate and easy to build dyno-engine model with real-time operational ability is required for this purpose. Artificial Neural Networks (ANN) have shown ability to model dynamic and complex systems like internal combustion engines. In this paper, the Group Method of Data Handling (GMDH) algorithm was utilized to build an ANN model of a heavy-duty diesel engine. One objective is to reduce the amount of manual labor on the results during the ANN model development process. The GMDH algorithm is a self-organizing process that will find the system laws and optimize the model structure automatically in one iteration. The GMDH model results were compared with a model developed by Levenberg-Marquardt Backpropagation (LM-BP) algorithm. The ANN models used actuator signals from an Engine Management System (EMS) to simulate the engine operation parameters. As revealed by the simulation results, the ANN models successfully predicted engine torque, fuel flow, and NOx concentration. The GMDH model as a self-organized model reduced lead time, had slightly higher transient cycle accuracy, had fewer inconsistent predictions, and demonstrated better extrapolation capability. The prediction accuracy for transient operation was improved by shifting the predicted value by calculating time delay and a decrease of 76.66% for fuel flow and 66.51% for NOX concentration in model accuracy were achieved. The GMDH dyno-engine model can be effectively applied as a virtual test cell instrument for testing, calibration, and optimization purposes.

Forecasting house prices in Iran using GMDH

Purpose An accurate predictive model for forecasting urban housing price in Isfahan can be useful for sellers and owners to take more appropriate actions about housing supplying. Also, it can help urban housing planners and policymakers in managing of the housing market and preventing an urban housing crisis in Isfahan. The purpose of this paper is forecasting housing price in Isfahan city of Iran until 2022 using group method of data handling (GMDH). Design/methodology/approach This paper presents an accurate predictive model by applying the GMDH algorithm by using GMDH-Shell software for forecasting housing price in municipal boroughs of Isfahan city till the second half of 2022 based on creating time series and existing data. Alongside housing price, some other affecting factors have been also considered to control the forecasting process and make it more accurate. Furthermore, this research shows the housing price changes of boroughs on map using ArcMap. Findings Based on forecasting results, the housing price will increase at all boroughs of Isfahan till second half of the year 2022. Amongst them, Borough 15 will have the highest percentage of the price increasing (28.27%) to year 2022 and Borough 6 will have the lowest percentage of the price increasing (8.34%) to the year 2022. About ranking of the boroughs in terms of housing price, Borough number 6 and 3 will keep their current position at the top and Borough number 15 will stay at the bottom. Research limitations/implications In this research, just few factors have been selected alongside housing price to control the forecasting process owing to limitation of reliable data availability about affecting factors. Originality/value The most remarkable point of this paper is reaching to a mathematical formula that can accurately forecast housing price in Isfahan city which has been rarely investigated in former studies, especially in simplified form. The technique used in this paper to forecast housing price in Isfahan city of Iran can be useful for other cities too.

Development of a Binary Model for Evaluating Water Distribution Systems by a Pressure Driven Analysis (PDA) Approach

Investigation of Water Distribution Networks (WDNs) is considered a challenging task due to the unpredicted and uncertain conditions in water engineering. When in a WDN, a pipe failure occurs, and shut-off valves to isolate the broken pipe to allow repairing works are activated. In these new conditions, the hydraulic parameters in the network are modified because the topology of the entire system changes. If the head becomes inadequate, the Pressure Driven Analysis (PDA) is the correct approach to evaluate the performance of water networks. Hence, in the present study, the water distribution system was evaluated in pressure-driven conditions for 100 different scenarios and then using a type of neural network called Group Method of Data Handling (GMDH) as a stochastic technique. For this purpose, several most notable parameters including the base demand, pressure, and alpha (the percentage of effective supplied flow) were calculated using simulations based on a PDA approach and applied to the water distribution network of Praia a Mare in Southern Italy. In the second stage, the output parameters were used in a developed binary classification model. Finally, the obtained results showed that the GMDH algorithm can be applied as a powerful tool for modeling water distribution networks.

Estimation of contact heat transfer between curvilinear contacts using inverse method and group method of data handling (GMDH)-type neural networks

Precise knowledge of thermal contact conductance (TCC) across fixed solid contacts of flat-flat and curvilinear surfaces is becoming a crucial topic in advanced industrial applications such as thermal management of electronic packaging and nuclear energy production. In this study, at first, TCC across fixed solid contacts of flat-flat and cylinder-cylinder contacts has been calculated by inverse solution method. Furthermore, the accuracy of inverse method for estimation of TCC between flat-flat and cylinder-cylinder contacts has been evaluated based on the error analysis. Then, the GMDH (group method of data handling) algorithm has been used for heat transfer function estimation between contact surfaces based on input–output data which has been achieved from the experimental investigation and the inverse method using the Conjugate Gradient Method (CGM) with Adjoint problem. Different models of GMDH algorithm are applied and the optimal model is selected based on the common error criteria of RMSE (root mean square error). According to the results, the inverse method is accurate enough to predict TCC between flat-flat and cylinder-cylinder contacts with the root mean square error of 0.167 and 0.205, respectively. Also, it has shown that among the different models of GMDH algorithm, Genetic algorithm obtained by SVD method, make the best algorithm for TTC identification in fixed flat-flat and cylinder-cylinder contacting surfaces. Meanwhile, the attained Root Mean Square Error (RMSE) of the Genetic algorithm obtained by SVD method compared with the experimental results for flat-flat and cylinder-cylinder are 0.1459 and 0.1758, respectively.

Diverse classifiers ensemble based on GMDH-type neural network algorithm for binary classification

Group Method of Data Handling (GMDH) - type neural network algorithm is the heuristic self-organizing algorithm to model the sophisticated systems. In this study, we propose a new algorithm assembling different classifiers based on GMDH algorithm for binary classification. A Monte Carlo simulation study is conducted to compare diverse classifier ensemble based on GMDH (dce-GMDH) algorithm to the other well-known classifiers and to give recommendations for applied researchers on the selection of appropriate classifier under the different conditions. The simulation study illustrates the proposed approach is more successful than the other classifiers in classification in most scenarios generated under the different conditions. Our proposed method is compared to the other classifiers on Cleveland heart disease data. An implementation of the proposed approach is demonstrated on urine data. Moreover, the proposed algorithm is released under R package GMDH2 under the name of “dceGMDH” for implementation.

Numerical analysis of natural frequency and stress intensity factor in Euler–Bernoulli cracked beam

In this paper, the evaluation procedures of the natural frequency and the stress intensity factor in the opening mode are established for the Euler–Bernoulli cracked beam by using (a) a technique in the framework of the finite element method, (b) a group method of data handling (GMDH), and (c) the software ABAQUS software. In the first one, the stiffness and mass matrices of the beam are enriched according to the depth and location of the crack for the determination of the natural frequency. A discrete spring model is used to simulate the crack in the structure based on the energy release rate. The continuity conditions in a cracked element are applied to connect two sub-elements of both sides of the crack. In the second method, the natural frequency and the stress intensity factor are determined using the GMDH algorithm. Design of experiments technique is employed to create an optimum arrangement for the application in the GMDH neural network. A few case studies are examined to investigate the results of the analysis, in addition, to identify the priority and the comparison of the three methods. The procedure of the analysis explains the advantages and limitations of the finite element-based technique, the GMDH method, and ABAQUS.

A novel energy evaluation approach of machining processes based on data analysis

ABSTRACT Energy shortages, air pollution, and stringent environmental requirements have been gaining increasing concern. Since the scarcity of energy resources, it is imperative to consider the economic and environmental issues of growth in manufacturing. This paper presented an energy evaluation method (considering cutting edges, workpiece hardness, material removal rate, and rotation speed) for machining system based on the group method of data handling (GMDH) algorithm. First, a response surface experimental was carried out with three materials to determine whether the cutting edges, workpiece hardness, spindle speed, and material removal rate (MRR) affect the processing energy. It was concluded that cutting edges number and hardness obviously have a significant influence on energy consumption. Then, further experimentations were designed, and an evaluation model of the electrical energy demand based on GMDH algorithm has been proposed, in which the application in a milling machine tool indicates that the predicted output values were found to correlate closely with the measured values. Finally, the proposed model is applied to a workpiece machining process, and the results show that the approach presented in this paper is effective, and hence, this paper provides an important addition to existing machining processes energy-evaluating methods.

Estimation of Minimum Energy Consumption Index in a Protected Farm Using Bacteria Foraging Optimization Algorithm

This article estimates the minimum energy consumption index in a single story protected farm which would suggest the condition of minimum energy consumption or loss condition. The analytic hierarchy process (AHP), weighted sum method (WSM) and weighted product method (WPM) are used for relative ranking of four energy consumption indicators viz., water pumping, light supplement, CO2 balancing and cooling-heating under three criteria viz., cropping area, daily crop water, and indoor environment. The minimum, maximum and average energy consumption index was predicted by the bacteria foraging optimization (BFO) algorithm and the group method of data handling (GMDH). The minimum energy consumption index (1.4174) predicted by the BFO algorithm gives higher prediction compared energy consumption index (1.114) predicted by the GMDH algorithm.

Group Method of Data Handling (GMDH) Lithology Identification Based on Wavelet Analysis and Dimensionality Reduction as Well Log Data Pre-Processing Techniques

Although the group method of data handling (GMDH) is a self-organizing metaheuristic neural network capable of developing a classification function using influential input variables, the results can be improved by using some pre-processing steps. In this paper, we propose a joint principal component analysis (PCA) and GMDH (PCA-GMDH) classifier method. We investigated well log data pre-processing techniques composed of dimensionality reduction (DR) and wavelet analysis (WA), using the southern basin of the South Yellow Sea as a case study, with the aim of improving the lithology classification accuracy of the GMDH. Our results showed that the dimensionality reduction method, which is composed of PCA and linear discriminant analysis (LDA), minimized the complexity of the classifier by reducing the number of well log suites to the relevant components and factors. On the other hand, the WA decomposed the well log signals into time-frequency wavelets for the GMDH algorithm. Of all the pre-processing methods, only the PCA was able to significantly increase the classification accuracy rate of the GMDH. Finally, the proposed joint PCA-GMDH classifier not only increased the accuracy but also was able to distinguish between all the classes of lithofacies present in the southern basin of the South Yellow Sea.

A Qualitative Model for Predicting Energy Consumption of Rapid Prototyping Processes–a Case of Fused Deposition Modeling Processe

Fused deposition modeling (FDM) technology is a method of additive manufacturing that is growing with widely application. Due to the increasing tense of energy situation, it is also timely to consider the economic and environmental issues of growth in rapid prototyping technology. However, the question of how to model the functional relationship between printing parameters and energy consumption has received little attention. Only few researchers deal with the process optimization including the energy aspects. This paper explores how the printing parameters affect the energy demand for FDM process based on Group Method of Data Handling (GMDH) method. Further experimentations were designed, and an evaluation model of the energy demand based on GMDH algorithm has been proposed, in which the ANOVA revealed that the model is very significant. Meanwhile, by the ANOVA method, the printing system’s energy consumption is analyzed to find the critical factors, in order to make best improvement in the system level. It is concluded that worktable temperature and layering thickness obviously have a significant influence on energy demand of the printing process. Finally, using differential evolution (DE) algorithm, optimal process parameters have been found to achieve good energy consumption simultaneously for the response. The results showed that the energy demand of the FDM process is improved by optimizing the printing parameters. Hence, the approach presented in this paper provides an important addition to existing additive manufacturing processes energy evaluating methods.

Short-Term Load Forecasting Based on Elastic Net Improved GMDH and Difference Degree Weighting Optimization

As objects of load prediction are becoming increasingly diversified and complicated, it is extremely important to improve the accuracy of load forecasting under complex systems. When using the group method of data handling (GMDH), it is easy for the load forecasting to suffer from overfitting and be unable to deal with multicollinearity under complex systems. To solve this problem, this paper proposes a GMDH algorithm based on elastic net regression, that is, group method of data handling based on elastic net (EN-GMDH), as a short-term load forecasting model. The algorithm uses an elastic net to compress and punish the coefficients of the Kolmogorov–Gabor (K–G) polynomial and select variables. Meanwhile, based on the difference degree of historical data, this paper carries out variable weight processing on the input data of load forecasting, so as to solve the impact brought by the abrupt change of load law. Ten characteristic variables, including meteorological factors, meteorological accumulation factors, and holiday factors, are taken as input variables. Then, EN-GMDH is used to establish the relationship between the characteristic variables and the load, and a short-term load forecasting model is established. The results demonstrate that, compared with other algorithms, the evaluation index of EN-GMDH is significantly better than that of the rest algorithm models in short-term load forecasting, and the accuracy of prediction is obviously improved.