Hourly stepwise forecasting for solar irradiance using integrated hybrid models CNN-LSTM-MLP combined with error correction and VMD

Li, Jun; Huang, Xiaoqiao; Li, Qiong; Chen, Zaiqing; Liu, Gang; Tai, Yonghang

doi:10.1016/j.enconman.2023.116804

Cited by 41 publications

(12 citation statements)

References 53 publications

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“…These results demonstrate the effectiveness of the proposed model in accurately predicting the wind power in the short term. Reference [55] introduced an integrated hybrid model called CNN-LSTM-MLP, which incorporates error correction and the variational mode decomposition (VMD). This study claims that the proposed model surpasses numerous conventional alternative approaches in terms of both accuracy and robustness.…”

Section: Resultsmentioning

confidence: 99%

Load Forecasting for the Laser Metal Processing Industry Using VMD and Hybrid Deep Learning Models

et al. 2023

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Electric load forecasting is crucial for the metallurgy industry because it enables effective resource allocation, production scheduling, and optimized energy management. To achieve an accurate load forecasting, it is essential to develop an efficient approach. In this study, we considered the time factor of univariate time-series data to implement various deep learning models for predicting the load one hour ahead under different conditions (seasonal and daily variations). The goal was to identify the most suitable model for each specific condition. In this study, two hybrid deep learning models were proposed. The first model combines variational mode decomposition (VMD) with a convolutional neural network (CNN) and gated recurrent unit (GRU). The second model incorporates VMD with a CNN and long short-term memory (LSTM). The proposed models outperformed the baseline models. The VMD–CNN–LSTM performed well for seasonal conditions, with an average RMSE of 12.215 kW, MAE of 9.543 kW, and MAPE of 0.095%. Meanwhile, the VMD–CNN–GRU performed well for daily variations, with an average RMSE value of 11.595 kW, MAE of 9.092 kW, and MAPE of 0.079%. The findings support the practical application of the proposed models for electrical load forecasting in diverse scenarios, especially concerning seasonal and daily variations.

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Section: Resultsmentioning

confidence: 99%

Load Forecasting for the Laser Metal Processing Industry Using VMD and Hybrid Deep Learning Models

et al. 2023

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“…In the VMD algorithm, the parameters that significantly influence the decomposition of the original data are the number of decomposition levels and the penalty factor(L. . For more details, see references (Liu et al, 2023;. In the experimental process, manually setting the algorithm parameters often fails to yield the optimal settings.…”

Section: Gwo-improved Vmdmentioning

confidence: 99%

Ensemble Framework for Multistep Carbon Emission Prediction: An Improved Bi-LSTM Model Based on the Bayesian Optimization Algorithm and Two-Stage Decomposition

Song,

Bai,

Wang

et al. 2024

Preprint

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Effective prediction of carbon dioxide emissions is crucial for the real-time monitoring of carbon emission dynamics and the formulation of emission reduction policies. For the accurate and stable prediction of carbon emission data, multiple challenges must be addressed, mainly including nonlinearity, nonstationarity, and dynamic uncertainty. To further improve the accuracy of carbon emission prediction, an ensemble framework for daily CO₂ emission forecasting based on data decomposition-reconstruction is proposed. First, the original data are decomposed into trend, seasonal, and residual components using seasonal trend decomposition via the Loess method. For the trend and seasonal components, the Bayesian optimization algorithm (BOA) is applied to optimize a bidirectional long short-term memory (Bi-LSTM) neural network for further prediction. The Bi-LSTM adopts a novel LeakyReLU activation function to effectively extract data features, thereby enhancing the model's expressive power. Meanwhile, the Nadam optimizer is employed, which combines the adaptive learning rate mechanism of the Adam algorithm with the Nesterov momentum to update weights and biases, enabling efficient model training, accelerating convergence, and improving model performance. Then, the residual component is decomposed again using gray wolf optimizer (GWO)-optimized variational mode decomposition (VMD), VMD incorporates a quadratic penalty function and Lagrange multipliers to derive the input series. and the decomposed subsequences are predicted using gated recurrent unit (GRU) networks. Finally, the predictions of each subsequence are stacked and reconstructed to obtain the final forecast value, creating an ensemble framework referred to as STL-BOA-Bi-LSTM-GWO-VMD-GRU. The proposed ensemble framework is tested on real data from seven different regions. In both single-step and multistep prediction processes, the proposed ensemble framework has higher prediction accuracy and stability than other models, demonstrating significant advantages.

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“…Integrated models combine multiple base learners in serial/parallel manners, which helps to achieve more robust and generalized performance than any individual base learner [21][22][23]. In the field of PV prediction, Chaouachi et al [21] proposed an integrated model containing four neural networks for short-term solar power forecasting.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Experimental results proved that the proposed model provided more accurate forecasts than some conventional networks. Liu et al [22] provided an integrated CNN-LSTM-multi layer perceptron (MLP) model for solar irradiance prediction. This integrated model was superior to the traditional models in terms of prediction accuracy and robustness.…”

Section: Literature Reviewmentioning

confidence: 99%

Photovoltaic Solar Power Prediction Using iPSO-Based Data Clustering and AdaLSTM Network

Liu,

Li,

Xue

2024

Energies

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Due to the increasing integration of photovoltaic (PV) solar power into power systems, the prediction of PV solar power output plays an important role in power system planning and management. This study combines an optimized data clustering method with a serially integrated AdaLSTM network to improve the accuracy and robustness of PV solar power prediction. During the data clustering process, the Euclidean distance-based clustering centroids are optimized by an improved particle swarm optimization (iPSO) algorithm. For each obtained data cluster, the AdaLSTM network is utilized for model training, in which multiple LSTMs are serially combined together through the AdaBoost algorithm. For PV power prediction tasks, the inputs of the testing set are classified into the nearest data cluster by the K-nearest neighbor (KNN) method, and then the corresponding AdaLSTM network of this cluster is used to perform the prediction. Case studies from two real PV stations are used for prediction performance evaluation. Results based on three prediction horizons (10, 30 and 60 min) demonstrate that the proposed model combining the optimized data clustering and AdaLSTM has higher prediction accuracy and robustness than other comparison models. The root mean square error (RMSE) of the proposed model is reduced, respectively, by 75.22%, 73.80%, 67.60%, 66.30%, and 64.85% compared with persistence, BPNN, CNN, LSTM, and AdaLSTM without clustering (Case A, 30 min prediction). Even compared with the model combining the K-means clustering and AdaLSTM, the RMSE can be reduced by 10.75%.

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Hourly stepwise forecasting for solar irradiance using integrated hybrid models CNN-LSTM-MLP combined with error correction and VMD

Cited by 41 publications

References 53 publications

Load Forecasting for the Laser Metal Processing Industry Using VMD and Hybrid Deep Learning Models

Load Forecasting for the Laser Metal Processing Industry Using VMD and Hybrid Deep Learning Models

Ensemble Framework for Multistep Carbon Emission Prediction: An Improved Bi-LSTM Model Based on the Bayesian Optimization Algorithm and Two-Stage Decomposition

Photovoltaic Solar Power Prediction Using iPSO-Based Data Clustering and AdaLSTM Network

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