Predicting monthly biofuel production using a hybrid ensemble forecasting methodology

Yu, Lean; Liang, Shijie; Chen, Rongda; Lai, Kin Keung

doi:10.1016/j.ijforecast.2019.08.014

Cited by 58 publications

(20 citation statements)

References 25 publications

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“…Fig. 5 shows the MAE and 𝑅 2 𝑎𝑑 𝑗 for the proposed and contrast methods on the Singapore datasets. It can be seen from Fig.…”

Section: A Singapore Data Specific Experimentsmentioning

confidence: 99%

“…In Fig. 7, the first three subplots reflect MAE, RMSE and MAPE, and the smallest area is occupied by the error of the proposed model (red line), while the fourth subplot reflects 𝑅 2 𝑎𝑑 𝑗 , and the largest area is occupied by the proposed model. The experimental results show that the model proposed in this paper has excellent practicality and efficiency.…”

Section: B the Us Data Specific Experimentsmentioning

confidence: 99%

“…Therefore, decomposition algorithms are used by scholars to decompose the load data into multiple subsequences to reduce the difficulty of prediction. A hybrid method based on empirical modal decomposition (EMD) and LSTM with extreme learning machine optimization algorithm was proposed for predicting biofuel in [2]. A feature extraction STLF model based on EMD and improved generalized regression neural network(GRNN) with minimizing redundancy and maximizing correlation(mRMR) was presented in [3].…”

Section: Introductionmentioning

confidence: 99%

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A VMD and LSTM Based Hybrid Model of Load Forecasting for Power Grid Security

Zhang

et al. 2022

IEEE Trans. Ind. Inf.

107

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As the basis for the static security of the power grid, power load forecasting directly affects the safety of grid operation, the rationality of grid planning, and the economy of supply-demand balance. However, various factors lead to drastic changes in short-term power consumption, making the data more complex and thus more difficult to forecast. In response to this problem, a new hybrid model based on Variational mode decomposition (VMD) and Long Short-Term Memory (LSTM) with seasonal factors elimination and error correction is proposed in this paper. Comprehensive case studies on four real-world load datasets from Singapore and the United States are employed to demonstrate the effectiveness and practicality of the proposed hybrid model. The experimental results show that the prediction accuracy of the proposed model is significantly higher than that of the contrast models.

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“…Fig. 5 shows the MAE and 𝑅 2 𝑎𝑑 𝑗 for the proposed and contrast methods on the Singapore datasets. It can be seen from Fig.…”

Section: A Singapore Data Specific Experimentsmentioning

confidence: 99%

Section: B the Us Data Specific Experimentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A VMD and LSTM Based Hybrid Model of Load Forecasting for Power Grid Security

Zhang

et al. 2022

IEEE Trans. Ind. Inf.

107

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“…The aforementioned study indicated that the proposed hybrid ensemble forecasting technique competitively predicted biofuel production. 29 Ribeiro and dos Santos 30 analyzed and studied the role of different types of ensemble learning methods (Bagging, Boosting, and Stacking) on predicting price series, and finally concluded that ensemble technique showed statistically significant gains, reducing prediction errors to a large extent. In addition, many recent studies 20,[31][32][33] have analyzed ensemble learning methods and have made great progress in this regard.…”

Section: Overview Of Studies On Carbon Price Forecasting and Other mentioning

confidence: 99%

“…In this model, the original time series was decomposed and reconstructed into multi‐mode by the empirical mode decomposition method and fine‐to‐coarse approach, after which the modes were separately predicted and the results of the individual predictions were accumulated to generate the final prediction results. The aforementioned study indicated that the proposed hybrid ensemble forecasting technique competitively predicted biofuel production 29 . Ribeiro and dos Santos 30 analyzed and studied the role of different types of ensemble learning methods (Bagging, Boosting, and Stacking) on predicting price series, and finally concluded that ensemble technique showed statistically significant gains, reducing prediction errors to a large extent.…”

Section: Introductionmentioning

confidence: 99%

An ensemble‐driven long short‐term memory model based on mode decomposition for carbon price forecasting of all eight carbon trading pilots in China

Sun

2020

Energy Science & Engineering

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Global climate change, which was triggered by the excessive emissions of greenhouse gases, has become an obstacle in sustainable development. Among all greenhouse gases, carbon dioxide is the most prominent and is artificially controllable. China ranks as the largest emitter of carbon dioxide worldwide and is a pioneer in economic growth. 1 As the international community is advocating in reducing carbon emissions, Chinese carbon emissions and its corresponding

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A bi‐level ensemble learning approach to complex time series forecasting: Taking exchange rates as an example

Hao

Feng

et al. 2023

Journal of Forecasting

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Forecasting complex time series faces a huge challenge due to its high volatility. To improve the accuracy and robustness of prediction, this paper proposes a bi‐level ensemble learning approach by combining decomposition‐ensemble forecasting and resample strategies. The bi‐level ensemble approach consists of four steps: data decomposition via singular spectrum analysis (SSA), resampling by employing a bagging algorithm, individual forecasting utilizing extreme learning machine (ELM), and introducing sorting‐pruning and simple addition ensemble strategies for integrating the inner and outer results, respectively. To verify the effectiveness of the established forecasting approach, three exchange rate time series are selected as samples. The results reveal that the proposed model is significantly better than the other benchmarks at different lead times, which indicates that it can be regarded as an effective and promising tool for complex time series forecasting.

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Predicting monthly biofuel production using a hybrid ensemble forecasting methodology

Cited by 58 publications

References 25 publications

A VMD and LSTM Based Hybrid Model of Load Forecasting for Power Grid Security

A VMD and LSTM Based Hybrid Model of Load Forecasting for Power Grid Security

An ensemble‐driven long short‐term memory model based on mode decomposition for carbon price forecasting of all eight carbon trading pilots in China

A bi‐level ensemble learning approach to complex time series forecasting: Taking exchange rates as an example

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