Load forecasting for regional integrated energy system based on complementary ensemble empirical mode decomposition and multi-model fusion

Shi, Jian; Teh, Jiashen

doi:10.1016/j.apenergy.2023.122146

Cited by 36 publications

(2 citation statements)

References 35 publications

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“…The basic idea of SVM is to construct an optimal hyperplane that maximizes the distance between the hyperplane and the sample sets of different classes in the sample or feature space, aiming to achieve the goal of maximizing generalization ability [19,20]. Unlike traditional artificial neural network methods, SVM adopts a structural risk minimization criterion, minimizing the generalization error bound to achieve maximum generalization ability [22]. SVM has better generalization ability compared to artificial neural network methods, and its solution is the unique global optimum.…”

Section: Quantitative Analysis Methods For Pid Signalmentioning

confidence: 99%

A High-Precision Monitoring Method Based on SVM Regression for Multivariate Quantitative Analysis of PID Response to VOC Signals

Feng,

Liu,

Ren

et al. 2024

Chemosensors

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In the moist environment of soil-water-air, there is a problem of low accuracy in monitoring volatile organic compounds (VOCs) using a photoionization detector (PID). This study is based on the PID water-soil-gas VOC online monitor developed by this group, online monitoring of the concentration of different constituents of VOCs in different production enterprises of the petroleum and chemical industries in Shandong Province, with the concentration of the laboratory test, to build a relevant model. The correlation coefficient about the PID test concentration and the actual concentration correlation coefficient was obtained through the collection of a large number of data trainings. Based on the application of PID in VOC monitoring, the establishment of a PID high-precision calibration model is important for the precise monitoring of VOCs. In this paper, multiple quantitative analyses were conducted, based on SVM regression of PID response to VOC signals, to study the high-precision VOC monitoring method. To select the response signals of PID under different concentrations of environmental VOCs measured by the research group, first, the PID response to VOC signals was modeled using the support vector machine principle to verify the effect of traditional SVM regression. For the problem of raw data redundancy, calculate the time-domain and frequency-domain characteristics of the PID signal, and conduct the principal component analysis of the time-domain of the PID signal. In order to make the SVM regression more generalized and robust, the selection of kernel function parameters and penalty factor of SVM is optimized by genetic algorithm. By comparing the accuracy of PID calibration models such as PID signal feature extraction, SVM regression, and principal component analysis SVM regression, the superiority of photoionization detector using the signal feature extraction PCA-GA-SVM method to monitor VOCs is verified.

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Section: Quantitative Analysis Methods For Pid Signalmentioning

confidence: 99%

A High-Precision Monitoring Method Based on SVM Regression for Multivariate Quantitative Analysis of PID Response to VOC Signals

Feng,

Liu,

Ren

et al. 2024

Chemosensors

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“…The findings validate the effectiveness of the predictive model is effective in handling nonstationary sequences of electricity consumption and demonstrates the ultmost precision in forecasting. In [38], a neural network ensemble framework (eNN) is proposed, employing LSTM, SVM, BPNN, and ELM to forecast wind and solar power generation in China. Three algorithms were employed to improve the accuracy of predictions.…”

Section: Related Study and Contributionsmentioning

confidence: 99%

Comparative Analysis between Intelligent Machine Committees and Hybrid Deep Learning with Genetic Algorithms in Energy Sector Forecasting: A Case Study on Electricity Price and Wind Speed in the Brazilian Market

Conte,

Oliveira

2024

Energies

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Global environmental impacts such as climate change require behavior from society that aims to minimize greenhouse gas emissions. This includes the substitution of fossil fuels with other energy sources. An important aspect of efficient and sustainable management of the electricity supply in Brazil is the prediction of some variables of the national electric system (NES), such as the price of differences settlement (PLD) and wind speed for wind energy. In this context, the present study investigated two distinct forecasting approaches. The first involved the combination of deep artificial neural network techniques, long short-term memory (LSTM), and multilayer perceptron (MLP), optimized through the canonical genetic algorithm (GA). The second approach focused on machine committees including MLP, decision tree, linear regression, and support vector machine (SVM) in one committee, and MLP, LSTM, SVM, and autoregressive integrated moving average (ARIMA) in another. The results indicate that the hybrid AG + LSTM algorithm demonstrated the best performance for PLD, with a mean squared error (MSE) of 4.68. For wind speed, there is a MSE of 1.26. These solutions aim to contribute to the Brazilian electricity market’s decision making.

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Two-stage forecasting of TCN-GRU short-term load considering error compensation and real-time decomposition

Li,

Ye,

et al. 2024

Earth Sci Inform

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Load forecasting for regional integrated energy system based on complementary ensemble empirical mode decomposition and multi-model fusion

Cited by 36 publications

References 35 publications

A High-Precision Monitoring Method Based on SVM Regression for Multivariate Quantitative Analysis of PID Response to VOC Signals

A High-Precision Monitoring Method Based on SVM Regression for Multivariate Quantitative Analysis of PID Response to VOC Signals

Comparative Analysis between Intelligent Machine Committees and Hybrid Deep Learning with Genetic Algorithms in Energy Sector Forecasting: A Case Study on Electricity Price and Wind Speed in the Brazilian Market

Two-stage forecasting of TCN-GRU short-term load considering error compensation and real-time decomposition

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