Carbon Price Prediction Based on Ensemble Empirical Mode Decomposition and Extreme Learning Machine Optimized by Improved Bat Algorithm Considering Energy Price Factors

Sun, Wei; Zhang, Junjian

doi:10.3390/en13133471

Cited by 21 publications

(10 citation statements)

References 33 publications

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“…This architecture is also known as vanilla LSTM and has been applied in similar contexts [35,52]. In attempt to compare performances, we evaluate other 3 models: Random Forest (RF), Support Vector Machine (SVM) with two kernels: Linear (SVML) and Radial Basis Function (SVMR), which are considered as suggested techniques for this kind of problem [14,15]. Both are considered machine learning techniques [14,53].…”

Section: Proposed Model and Benchmarksmentioning

confidence: 99%

“…On the one hand, Statistical modelling needs to simplify the phenomena, when attempting to see linear structures. On the other hand, newer techniques arise from artificial intelligence development and demonstrated interesting outcomes [11,[13][14][15] when outperformed statistical ones in many cases, notably for complex backgrounds.…”

Section: Introductionmentioning

confidence: 99%

“…The results obtained in this paper demonstrate that it is possible to forecast ethanol prices in Brazilian sight with a degree of correctness of direction between 68 and 80% for the periods of 63, 125 and 252 working days (which is equivalent to 3, 6 and 12 months). Also, for validation of the algorithm, 3 models are compared to LSTM: Random Forest (RF), and Support Vector Machine (SVM)-with Linear and RBF kernels, since these techniques have shown satisfactory performance in the financial market contexts [13][14][15]. The model revealed interesting prediction power of the Brazilian biofuel price with a small error in periods of low volatility but poor performance occurred during the pandemic caused by COVID-19 due to the sharp drop of the commodity prices in this period.…”

Section: Introductionmentioning

confidence: 99%

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Forecasting Brazilian Ethanol Spot Prices Using LSTM

et al. 2021

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Ethanol is one of the most used fuels in Brazil, which is the second-largest producer of this biofuel in the world. The uncertainty of price direction in the future increases the risk for agents operating in this market and can affect a dependent price chain, such as food and gasoline. This paper uses the architecture of recurrent neural networks—Long short-term memory (LSTM)—to predict Brazilian ethanol spot prices for three horizon-times (12, 6 and 3 months ahead). The proposed model is compared to three benchmark algorithms: Random Forest, SVM Linear and RBF. We evaluate statistical measures such as MSE (Mean Squared Error), MAPE (Mean Absolute Percentage Error), and accuracy to assess the algorithm robustness. Our findings suggest LSTM outperforms the other techniques in regression, considering both MSE and MAPE but SVM Linear is better to identify price trends. Concerning predictions per se, all errors increase during the pandemic period, reinforcing the challenge to identify patterns in crisis scenarios.

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Section: Proposed Model and Benchmarksmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Forecasting Brazilian Ethanol Spot Prices Using LSTM

et al. 2021

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“…Dutta used the Crude Oil Volatility Index (OVX) to study the impact of oil market uncertainty on emission price fluctuations [27]. Sun et al used a one-time decomposition algorithm combined with influencing factor models to predict carbon prices [28,29].…”

Section: Introductionmentioning

confidence: 99%

A Carbon Price Prediction Model Based on the Secondary Decomposition Algorithm and Influencing Factors

Zhou

Wang

2021

Energies

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Carbon emission reduction is now a global issue, and the prediction of carbon trading market prices is an important means of reducing emissions. This paper innovatively proposes a second decomposition carbon price prediction model based on the nuclear extreme learning machine optimized by the Sparrow search algorithm and considers the structural and nonstructural influencing factors in the model. Firstly, empirical mode decomposition (EMD) is used to decompose the carbon price data and variational mode decomposition (VMD) is used to decompose Intrinsic Mode Function 1 (IMF1), and the decomposition of carbon prices is used as part of the input of the prediction model. Then, a maximum correlation minimum redundancy algorithm (mRMR) is used to preprocess the structural and nonstructural factors as another part of the input of the prediction model. After the Sparrow search algorithm (SSA) optimizes the relevant parameters of Extreme Learning Machine with Kernel (KELM), the model is used for prediction. Finally, in the empirical study, this paper selects two typical carbon trading markets in China for analysis. In the Guangdong and Hubei markets, the EMD-VMD-SSA-KELM model is superior to other models. It shows that this model has good robustness and validity.

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“…In [36], the ELM is optimized by bat algorithm (BA-ELM) for mobile heterogeneous wireless networks. In [37], the ELM optimized by improved bat algorithm is used for carbon price prediction. In [38], the BA-ELM is introduced for fault detection of fuel system.…”

Section: Introductionmentioning

confidence: 99%

The Improved ELM Algorithms Optimized by Bionic WOA for EEG Classification of Brain Computer Interface

et al. 2021

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The breakthrough of electroencephalogram (EEG) signal classification of brain computer interface (BCI) will set off another technological revolution of human computer interaction technology. Because the collected EEG is a type of nonstationary signal with strong randomness, effective feature extraction and data mining techniques are urgently required for EEG classification of BCI. In this paper, the new bionic whale optimization algorithms (WOA) are proposed to promote the improved extreme learning machine (ELM) algorithms for EEG classification of BCI. Two improved WOA-ELM algorithms are designed to compensate for the deficiency of random weight initialization for basic ELM. Firstly, the top several best individuals are selected and voted to make decisions to avoid misjudgment on the best individual. Secondly, the initial connection weights and bias between the input layer nodes and hidden layer nodes are optimized by WOA through bubble-net attacking strategy (BNAS) and shrinking encircling mechanism (SEM), and different regularization mechanisms are introduced in different layers to generate appropriate sparse weight matrix to promote the generalization performance of the algorithm.As shown in the contrast results, the average accuracy of the proposed method can reach 93.67%, which is better than other methods on BCI dataset.

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Carbon Price Prediction Based on Ensemble Empirical Mode Decomposition and Extreme Learning Machine Optimized by Improved Bat Algorithm Considering Energy Price Factors

Cited by 21 publications

References 33 publications

Forecasting Brazilian Ethanol Spot Prices Using LSTM

Forecasting Brazilian Ethanol Spot Prices Using LSTM

A Carbon Price Prediction Model Based on the Secondary Decomposition Algorithm and Influencing Factors

The Improved ELM Algorithms Optimized by Bionic WOA for EEG Classification of Brain Computer Interface

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