Short-term photovoltaic power point-interval forecasting based on double-layer decomposition and WOA-BiLSTM-Attention and considering weather classification

Yu, Min; Niu, Dongxiao; Wang, Keke; Du, Ruoyun; Yu, Xiaoyu; Sun, Lijie; Wang, Feiran

doi:10.1016/j.energy.2023.127348

Cited by 34 publications

(5 citation statements)

References 35 publications

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“…To further quantify the uncertainty in the ρ d max prediction process, a Prediction Interval (PI) based on interval prediction theory is proposed [ 57 ]. As shown in Figure 8 , a PI includes a prediction upper limit and lower limit, representing the estimated range of the predicted ρ d max under a certain confidence level ( CI ) [ 58 ].…”

Section: Methodsmentioning

confidence: 99%

A Novel Method for Full-Section Assessment of High-Speed Railway Subgrade Compaction Quality Based on ML-Interval Prediction Theory

Deng,

Wang,

et al. 2024

Sensors

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The high-speed railway subgrade compaction quality is controlled by the compaction degree (K), with the maximum dry density (ρdmax) serving as a crucial indicator for its calculation. The current mechanisms and methods for determining the ρdmax still suffer from uncertainties, inefficiencies, and lack of intelligence. These deficiencies can lead to insufficient assessments for the high-speed railway subgrade compaction quality, further impacting the operational safety of high-speed railways. In this paper, a novel method for full-section assessment of high-speed railway subgrade compaction quality based on ML-interval prediction theory is proposed. Firstly, based on indoor vibration compaction tests, a method for determining the ρdmax based on the dynamic stiffness Krb turning point is proposed. Secondly, the Pso-OptimalML-Adaboost (POA) model for predicting ρdmax is determined based on three typical machine learning (ML) algorithms, which are back propagation neural network (BPNN), support vector regression (SVR), and random forest (RF). Thirdly, the interval prediction theory is introduced to quantify the uncertainty in ρdmax prediction. Finally, based on the Bootstrap-POA-ANN interval prediction model and spatial interpolation algorithms, the interval distribution of ρdmax across the full-section can be determined, and a model for full-section assessment of compaction quality is developed based on the compaction standard (95%). Moreover, the proposed method is applied to determine the optimal compaction thicknesses (H0), within the station subgrade test section in the southwest region. The results indicate that: (1) The PSO-BPNN-AdaBoost model performs better in the accuracy and error metrics, which is selected as the POA model for predicting ρdmax. (2) The Bootstrap-POA-ANN interval prediction model for ρdmax can construct clear and reliable prediction intervals. (3) The model for full-section assessment of compaction quality can provide the full-section distribution interval for K. Comparing the H0 of 50~60 cm and 60~70 cm, the compaction quality is better with the H0 of 40~50 cm. The research findings can provide effective techniques for assessing the compaction quality of high-speed railway subgrades.

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Section: Methodsmentioning

confidence: 99%

A Novel Method for Full-Section Assessment of High-Speed Railway Subgrade Compaction Quality Based on ML-Interval Prediction Theory

Deng,

Wang,

et al. 2024

Sensors

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“…Compared with IVMD-SSA-Elman, RMSE and MAE declined by 55.1% and 54.5%, respectively; due to issues when dealing with time series problems, the TCN-GRU network has better performance. Table 4 presents the performance metrics of the novel method proposed in this study alongside the approaches WOA-BiLSTM-Attention [50], LSTM-TCN [51], and CNN-GRU [52] in scenarios characterized by rainy conditions and substantial fluctuations in photovoltaic power generation. Our findings reveal that the proposed method outperforms the existing techniques in terms of predictive accuracy and dependability, as evidenced by the lower MAE and RMSE values obtained by our model.…”

Section: Simulation Analysismentioning

confidence: 99%

A Novel Improved Variational Mode Decomposition-Temporal Convolutional Network-Gated Recurrent Unit with Multi-Head Attention Mechanism for Enhanced Photovoltaic Power Forecasting

Fu,

Zhang,

Xie

2024

Electronics

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Photovoltaic (PV) power forecasting plays a crucial role in optimizing renewable energy integration into the grid, necessitating accurate predictions to mitigate the inherent variability of solar energy generation. We propose a novel forecasting model that combines improved variational mode decomposition (IVMD) with the temporal convolutional network-gated recurrent unit (TCN-GRU) architecture, enriched with a multi-head attention mechanism. By focusing on four key environmental factors influencing PV output, the proposed IVMD-TCN-GRU framework targets a significant research gap in renewable energy forecasting methodologies. Initially, leveraging the sparrow search algorithm (SSA), we optimize the parameters of VMD, including the mode component K-value and penalty factor, based on the minimum envelope entropy principle. The optimized VMD then decomposes PV power, while the TCN-GRU model harnesses TCN’s proficiency in learning local temporal features and GRU’s capability in rapidly modeling sequence data, while leveraging multi-head attention to better utilize the global correlation information within sequence data. Through this design, the model adeptly captures the correlations within time series data, demonstrating superior performance in prediction tasks. Subsequently, the SSA is employed to optimize GRU parameters, and the decomposed PV power mode components and environmental feature attributes are inputted into the TCN-GRU neural network. This facilitates dynamic temporal modeling of multivariate feature sequences. Finally, the predicted values of each component are summed to realize PV power forecasting. Validation using real data from a PV station corroborates that the novel model demonstrates a substantial reduction in RMSE and MAE of up to 55.1% and 54.5%, respectively, particularly evident in instances of pronounced photovoltaic power fluctuations during inclement weather conditions. The proposed method exhibits marked improvements in accuracy compared to traditional PV power prediction methods, underscoring its significance in enhancing forecasting precision and ensuring the secure scheduling and stable operation of power systems.

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“…These approaches exhibited adaptability and accuracy in solar energy production predictions. With the continuous development of intelligent optimization algorithms, many scholars are committed to combining algorithms with deep learning networks, striving to find the optimal parameters of the model through optimization algorithms, such as using the PSO [20] to optimize Bi-LSTM, the SSA [21] to optimize LSTM models, and using the WOA [22,23] to optimize BiLSTM, ELM, etc. to reduce the impact of model parameters on prediction results and improve prediction accuracy.…”

Section: Deep Learningmentioning

confidence: 99%

A Photovoltaic Power Prediction Approach Based on Data Decomposition and Stacked Deep Learning Model

et al. 2023

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Correctly anticipating PV electricity production may lessen stochastic fluctuations and incentivize energy consumption. To address the intermittent and unpredictable nature of photovoltaic power generation, this article presents an ensemble learning model (MVMD-CLES) based on the whale optimization algorithm (WOA), variational mode decomposition (VMD), convolutional neural network (CNN), long and short-term memory (LSTM), and extreme learning machine (ELM) stacking. Given the variances in the spatiotemporal distribution of photovoltaic data and meteorological features, a multi-branch character extraction iterative mixture learning model is proposed: we apply the MWOA algorithm to find the optimal decomposition times and VMD penalty factor, and then divide the PV power sequences into sub-modes with different frequencies using a two-layer algorithmic structure to reconstruct the obtained power components. The primary learner is CNN–BiLSTM, which is utilized to understand the temporal and spatial correlation of PV power from information about the weather and the output of photovoltaic cells, and the LSTM learns the periodicity and proximity correlation of the power data and obtains the corresponding component predictions. The second level is the secondary learner—the output of the first layer is learned again using the ELM to attenuate noise and achieve short-term prediction. In different case studies, regardless of weather changes, the proposed method is provided with the best group of consistency and constancy, with an average RMSE improvement of 12.08–39.14% over a single-step forecast compared to other models, the average forecast RMSE increased by 5.71–9.47% for the first two steps.

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Short-term photovoltaic power point-interval forecasting based on double-layer decomposition and WOA-BiLSTM-Attention and considering weather classification

Cited by 34 publications

References 35 publications

A Novel Method for Full-Section Assessment of High-Speed Railway Subgrade Compaction Quality Based on ML-Interval Prediction Theory

A Novel Method for Full-Section Assessment of High-Speed Railway Subgrade Compaction Quality Based on ML-Interval Prediction Theory

A Novel Improved Variational Mode Decomposition-Temporal Convolutional Network-Gated Recurrent Unit with Multi-Head Attention Mechanism for Enhanced Photovoltaic Power Forecasting

A Photovoltaic Power Prediction Approach Based on Data Decomposition and Stacked Deep Learning Model

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