Load Forecasting for the Laser Metal Processing Industry Using VMD and Hybrid Deep Learning Models

Aksan, Fachrizal; Suresh, Vishnu; Janík, P.; Sikorski, Tomasz

doi:10.3390/en16145381

Cited by 7 publications

(5 citation statements)

References 56 publications

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“…In general, machine learning models outperform univariate models in both the training and testing phases [86]. In this paper, multiple machine learning models were fused and integrated to simulate runoff, and the runoff simulation and reconstruction results corresponding to each method were obtained in only 37 s, which is shorter than the training and prediction time of the LSTM and ANN models (t = 120-200 s) [87].…”

Section: Discussionmentioning

confidence: 99%

Simulation and Reconstruction of Runoff in the High-Cold Mountains Area Based on Multiple Machine Learning Models

Wang,

Sun,

Wang

et al. 2023

Water

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Runoff from the high-cold mountains area (HCMA) is the most important water resource in the arid zone, and its accurate forecasting is key to the scientific management of water resources downstream of the basin. Constrained by the scarcity of meteorological and hydrological stations in the HCMA and the inconsistency of the observed time series, the simulation and reconstruction of mountain runoff have always been a focus of cold region hydrological research. Based on the runoff observations of the Yurungkash and Kalakash Rivers, the upstream tributaries of the Hotan River on the northern slope of the Kunlun Mountains at different time periods, and the meteorological and atmospheric circulation indices, we used feature analysis and machine learning methods to select the input elements, train, simulate, and select the preferences of the machine learning models of the runoffs of the two watersheds, and reconstruct the missing time series runoff of the Kalakash River. The results show the following. (1) Air temperature is the most important driver of runoff variability in mountainous areas upstream of the Hotan River, and had the strongest performance in terms of the Pearson correlation coefficient (ρXY) and random forest feature importance (FI) (ρXY = 0.63, FI = 0.723), followed by soil temperature (ρXY = 0.63, FI = 0.043), precipitation, hours of sunshine, wind speed, relative humidity, and atmospheric circulation were weakly correlated. A total of 12 elements were selected as the machine learning input data. (2) Comparing the results of the Yurungkash River runoff simulated by eight machine learning methods, we found that the gradient boosting and random forest methods performed best, followed by the AdaBoost and Bagging methods, with Nash–Sutcliffe efficiency coefficients (NSE) of 0.84, 0.82, 0.78, and 0.78, while the support vector regression (NSE = 0.68), ridge (NSE = 0.53), K-nearest neighbor (NSE = 0.56), and linear regression (NSE = 0.51) were simulated poorly. (3) The application of four machine learning methods, gradient boosting, random forest, AdaBoost, and bagging, to simulate the runoff of the Kalakash River for 1978–1998 was generally outstanding, with the NSE exceeding 0.75, and the results of reconstructing the runoff data for the missing period (1999–2019) could well reflect the characteristics of the intra-annual and inter-annual changes in runoff.

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Section: Discussionmentioning

confidence: 99%

Simulation and Reconstruction of Runoff in the High-Cold Mountains Area Based on Multiple Machine Learning Models

Wang,

Sun,

Wang

et al. 2023

Water

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“…During the training phase, the hyperparameter configuration in deep-learning models is crucial for adjusting and optimizing the model's behavior and performance. There is no fixed answer for setting hyperparameters in deep-learning models; it involves systematic experimentation, adherence to best practices, and leveraging domain knowledge [24]. In this paper, repeated experiments were conducted based on a large number of previous references to draw conclusions on a reasonable setting of network parameters.…”

Section: Description Of the Experimentsmentioning

confidence: 99%

“…Yildiz et al [23] converts the subsequence obtained with variational mode decomposition (VMD) into spatial input feature maps, and then predicts wind power using a residual-based deep convolutional neural network. Aksan et al [24] combines VMD and multiple deep-learning methods to form hybrid forecasting models, and these models show satisfactory applicability to load forecasting development scenarios under different conditions. However, the VMD decomposition parameters in references [23,24] all use empirical values.…”

Section: Introductionmentioning

confidence: 99%

“…Aksan et al [24] combines VMD and multiple deep-learning methods to form hybrid forecasting models, and these models show satisfactory applicability to load forecasting development scenarios under different conditions. However, the VMD decomposition parameters in references [23,24] all use empirical values. In practice, the VMD decomposition effect largely depends on the setting of the decomposition parameters.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Step Prediction of Wind Power Based on Hybrid Model with Improved Variational Mode Decomposition and Sequence-to-Sequence Network

Bai,

Jin,

et al. 2024

Processes

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Due to the complexity of wind power, traditional prediction models are incapable of fully extracting the hidden features of multidimensional strong fluctuation data, which results in poor multi-step prediction performance. To predict continuous power effectively in the future, an improved wind power multi-step prediction model combining variational mode decomposition (VMD) with sequence-to-sequence (Seq2Seq) is proposed. Firstly, the wind power sequence is smoothed using VMD and the decomposition parameters of VMD are optimized by using the squirrel search algorithm (SSA) to effectively optimize the decomposition effect. Then, the subsequence obtained from decomposition, together with the original wind power data, is reconstructed into multivariate time series features. Finally, a Seq2Seq model is constructed, and convolutional neural networks (CNNs) with bidirectional gate recurrent units (BiGRUs) are used to learn the coupling and timing relationships of the input data and encode them. The gate recurrent unit (GRU) is decoded to achieve continuous power prediction. Based on the actual operating data of a wind farm, a case analysis is conducted. Experimental results show that SSA-VMD can effectively optimize the decomposition effect, and the subsequences obtained with its decomposition are highly accurate when applied to predictions. The Seq2Seq model has better multi-step prediction results than traditional prediction methods, and as the prediction step size increases, the advantages are more obvious.

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“…These collective efforts spanning several years are comprehensively summarized in Table 1. Taking a leaf from hybrid model designs, Aksan et al [26] introduced models that combined variational mode decomposition (VMD) with DL models, such as CNN and RNNs. Their models, VMD-CNN-long short-term memory (LSTM) and VMD-CNN-gated recurrent unit (GRU), showcased versatility, adeptly managing seasonal and daily energy consumption variations.…”

Section: Introductionmentioning

confidence: 99%

BiGTA-Net: A Hybrid Deep Learning-Based Electrical Energy Forecasting Model for Building Energy Management Systems

So,

Oh,

Jeon

et al. 2023

Systems

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The growth of urban areas and the management of energy resources highlight the need for precise short-term load forecasting (STLF) in energy management systems to improve economic gains and reduce peak energy usage. Traditional deep learning models for STLF present challenges in addressing these demands efficiently due to their limitations in modeling complex temporal dependencies and processing large amounts of data. This study presents a groundbreaking hybrid deep learning model, BiGTA-net, which integrates a bi-directional gated recurrent unit (Bi-GRU), a temporal convolutional network (TCN), and an attention mechanism. Designed explicitly for day-ahead 24-point multistep-ahead building electricity consumption forecasting, BiGTA-net undergoes rigorous testing against diverse neural networks and activation functions. Its performance is marked by the lowest mean absolute percentage error (MAPE) of 5.37 and a root mean squared error (RMSE) of 171.3 on an educational building dataset. Furthermore, it exhibits flexibility and competitive accuracy on the Appliances Energy Prediction (AEP) dataset. Compared to traditional deep learning models, BiGTA-net reports a remarkable average improvement of approximately 36.9% in MAPE. This advancement emphasizes the model’s significant contribution to energy management and load forecasting, accentuating the efficacy of the proposed hybrid approach in power system optimizations and smart city energy enhancements.

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Load Forecasting for the Laser Metal Processing Industry Using VMD and Hybrid Deep Learning Models

Cited by 7 publications

References 56 publications

Simulation and Reconstruction of Runoff in the High-Cold Mountains Area Based on Multiple Machine Learning Models

Simulation and Reconstruction of Runoff in the High-Cold Mountains Area Based on Multiple Machine Learning Models

Multi-Step Prediction of Wind Power Based on Hybrid Model with Improved Variational Mode Decomposition and Sequence-to-Sequence Network

BiGTA-Net: A Hybrid Deep Learning-Based Electrical Energy Forecasting Model for Building Energy Management Systems

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