Short-Term Load Forecasting Based on the CEEMDAN-Sample Entropy-BPNN-Transformer

Huang, Shucai; Zhang, Jing; He, Yu; Fu, Xiaofan; Fan, Luqin; Yao, Gang; Wen, Yan

doi:10.3390/en15103659

Cited by 29 publications

(7 citation statements)

References 18 publications

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“…but EMD will cause modal aliasing [11]. Although the improved EEMD method effectively reduces modal aliasing, there are too many low-frequency components in the subsequence [12]. Shen et al used the CEEMDAN decomposition to overcome the above problems and improve the efficiency and accuracy of decomposition [13].…”

Section: Introductionmentioning

confidence: 99%

A short-term prediction model for photovoltaic power forecasting based on CEEMDAN-CS-LSTM

mao,

2024

Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023)

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Accurate prediction of PV power is essential to maximize solar energy utilization, enhance the performance of PV power generation systems, and ensure the dependability of power infrastructures. In this paper, an approach based on CS-LSTM neural network is proposed for short term power forecasting. The proposed approach involves processing the data using CEEMDAN, followed by the application of the CS algorithm to determine the optimal LSTM hyperparameters. To evaluate the accuracy and dependability of the model, CEEMDAN-CS-LSTM is compared to other models. Experiments show that CEEMDAN-CS-LSTM outperforms the other models in terms of prediction accuracy and stability.

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Section: Introductionmentioning

confidence: 99%

A short-term prediction model for photovoltaic power forecasting based on CEEMDAN-CS-LSTM

mao,

2024

Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023)

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“…Bommidi et al [35] developed a composite approach that harnesses the predictive strength of the transformer model alongside the analytical prowess of ICEEMDAN to improve wind-speed prediction accuracy. Huang et al [36] present a new hybrid forecasting model for short-term power load that effectively decomposes power load data into subsequences of varying complexities; employs BPNN for less complex subsequences and transformers for more intricate ones; and amalgamates the forecasts to form a unified prediction. Wang et al [37] utilized the transformer as a core component to devise an innovative convolutional transformer-based truncated Gaussian density framework, offering both precise wind-speed predictions and reliable probabilistic forecasts.…”

Section: Introductionmentioning

confidence: 99%

Wind-Speed Multi-Step Forecasting Based on Variational Mode Decomposition, Temporal Convolutional Network, and Transformer Model

Zhang,

Zhu,

Guo

2024

Energies

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Reliable and accurate wind-speed forecasts significantly impact the efficiency of wind power utilization and the safety of power systems. In addressing the performance enhancement of transformer models in short-term wind-speed forecasting, a multi-step prediction model based on variational mode decomposition (VMD), temporal convolutional network (TCN), and a transformer is proposed. Initially, the Dung Beetle Optimizer (DBO) is utilized to optimize VMD for decomposing non-stationary wind-speed series data. Subsequently, the TCN is used to extract features from the input sequences. Finally, the processed data are fed into the transformer model for prediction. The effectiveness of this model is validated by comparison with six other prediction models across three datasets, demonstrating its superior accuracy in short-term wind-speed forecasting. Experimental findings from three distinct datasets reveal that the developed model achieves an average improvement of 52.1% for R2. To the best of our knowledge, this places our model at the leading edge of wind-speed prediction for 8 h and 12 h forecasts, demonstrating MSEs of 1.003 and 0.895, MAEs of 0.754 and 0.665, and RMSEs of 1.001 and 0.946, respectively. Therefore, this research offers significant contributions through a new framework and demonstrates the utility of the transformer in effectively predicting short-term wind speed.

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“…After extracting chaotic features by phase space reconstruction of load data, the prediction results are obtained by applying VMD to decompose each dimensional data and re-constructing it into two sequences of high frequency and low frequency into the model. A load decomposition method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and sample entropy (SE) was proposed in the literature [26], and better prediction results were obtained by a back propagation (BP) neural network with the Transformer model for prediction.…”

Section: Introductionmentioning

confidence: 99%

Short-Term Power Load Forecasting Based on PSO-Optimized VMD-TCN-Attention Mechanism

Geng

Zhang

et al. 2023

Energies

Self Cite

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A new prediction framework is proposed to improve short-term power load forecasting accuracy. The framework is based on particle swarm optimization (PSO)-variational mode decomposition (VMD) combined with a time convolution network (TCN) embedded attention mechanism (Attention). The framework follows a two-step process. In the first step, PSO is applied to optimize the VMD decomposition method. The original electricity load sequence is decomposed, and the fitness function uses sample entropy to describe the complexity of the time series. The decomposed sub-sequences are combined with relevant features, such as meteorological data, to form the input sequence of the prediction model. In the second step, TCN is selected as the prediction model, and it is embedded with an attention mechanism to improve prediction accuracy. The above input sequence is fed to the model to obtain the PSO-VMD-TCN-Attention prediction framework. Load datasets and various prediction models validate the PSO-optimized VMD decomposition method and the TCN-Attention prediction model. Simulation results demonstrate that the PSO-optimized VMD decomposition method enhances the model’s prediction accuracy, and the TCN-Attention prediction model outperforms other prediction models in terms of prediction accuracy and ability.

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Short-Term Load Forecasting Based on the CEEMDAN-Sample Entropy-BPNN-Transformer

Cited by 29 publications

References 18 publications

A short-term prediction model for photovoltaic power forecasting based on CEEMDAN-CS-LSTM

A short-term prediction model for photovoltaic power forecasting based on CEEMDAN-CS-LSTM

Wind-Speed Multi-Step Forecasting Based on Variational Mode Decomposition, Temporal Convolutional Network, and Transformer Model

Short-Term Power Load Forecasting Based on PSO-Optimized VMD-TCN-Attention Mechanism

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