A New Precipitation Prediction Method Based on CEEMDAN-IWOA-BP Coupling

Liu, Fuping; Liu, Ying; Yang, Cheng‐Hong; Lai, Ruixun

doi:10.1007/s11269-022-03277-z

Cited by 10 publications

(2 citation statements)

References 33 publications

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“…Using this algorithm to optimize the hyperparameters of the TCN-Attention model, the optimal prediction is finally obtained. In paper [39], IWOA optimizes the initial weights and thresholds of the back-propagation (BP) neural network, which speeds up the iteration speed of the BP neural network and enhances the optimization ability and robustness of the model. In paper [40], IWOA is used to find the optimal parameter values of pulse coupled neural network (PCNN) to optimize PCNN.…”

Section: Source Vantage Drawbackmentioning

confidence: 99%

Load Day-Ahead Automatic Generation Control Reserve Capacity Demand Prediction Based on the Attention-BiLSTM Network Model Optimized by Improved Whale Algorithm

Li,

Liang

et al. 2024

Energies

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Load forecasting is a research hotspot in academia; in the context of new power systems, the prediction and determination of load reserve capacity is also important. In order to adapt to new forms of power systems, a day-ahead automatic generation control (AGC) reserve capacity demand prediction method based on the Fourier transform and the attention mechanism combined with a bidirectional long and short-term memory neural network model (Attention-BiLSTM) optimized by an improved whale optimization algorithm (IWOA) is proposed. Firstly, based on the response time, Fourier transform is used to refine the distinction between various types of load reserve demand, and the power of the AGC reserve band is calculated using Parseval’s theorem to obtain the reserve capacity demand sequence. The maximum mutual information coefficient method is used to explore the relevant influencing factors of the AGC reserve sequence concerning the data characteristics of the AGC reserve sequence. Then, the historical daily AGC reserve demand sequences with relevant features are input into the Attention-BiLSTM prediction model, and the improved whale algorithm is used to automatically find the optimal hyperparameters to obtain better prediction results. Finally, the arithmetic simulation results show that the model proposed in this paper has the best prediction performance with the upper (0.8810) and lower (0.6651) bounds of the coefficient of determination (R2) higher than the other models, and it has the smallest mean absolute percentage error (MAPE) and root mean square error (RMSE).

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Section: Source Vantage Drawbackmentioning

confidence: 99%

Load Day-Ahead Automatic Generation Control Reserve Capacity Demand Prediction Based on the Attention-BiLSTM Network Model Optimized by Improved Whale Algorithm

Li,

Liang

et al. 2024

Energies

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“…The results indicate that modifying the model's parameters using optimization algorithms can improve the model's prediction accuracy and efficiency. Liu et al [23] utilized the improved whale optimization algorithm (IWOA) to similarly optimize the parameters of BP neural network prediction model, thereby establishing the combined prediction model CEEMDAN-IWOA-BP. Experimental results indicate that the IWOA is preferable to the WOA, the genetic algorithm (GA), and the PSO when optimizing the parameters of BP neural network for predicting water quality characteristics.…”

Section: Introductionmentioning

confidence: 99%

A Combined Model for Water Quality Prediction Based on VMD-TCN-ARIMA Optimized by WSWOA

Zuo,

Gou,

Wang

et al. 2023

Water

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With environmental degradation and water scarcity becoming increasingly serious, it is urgent to carry out effective management of water resources. The key task of water environment monitoring is to conduct statistics and analysis of changes in water quality characteristics. Aiming to address the problem of the strong fluctuation and strong temporal correlation of water quality characteristics prediction, a new framework for water quality prediction based on variational mode decomposition–temporal convolutional networks–autoregressive integrated moving average (VMD-TCN-ARIMA) optimized by weighted swarm the whale search algorithm (WSWOA) algorithm is proposed. First, the WSWOA was proposed by introducing the two-weighted-factor perturbation strategy and the particle swarm search method based on the whale optimization algorithm (WOA), which effectively improves the convergence speed and global search capabilities. Second, to adaptively decompose the original water quality sequences, the VMD algorithm optimized by WSWOA was utilized, which can extract features and reduce noise in the original sequence. Furthermore, the TCN-ARIMA combined model is proposed for time series analysis. The combined model is introduced to assign different algorithms to the decomposed components to reduce prediction error and modeling effort. In comparison to VMD-TCN model, the experimental results have shown that on the data of water quality characteristic dissolved oxygen (DO), the proposed model’s root mean square error (RMSE) and computational time is reduced by 41.05% and 26.06%, further improving the accuracy and efficiency of prediction.

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A Hybrid Particle Swarm Optimization-Genetic Algorithm for Multiobjective Reservoir Ecological Dispatching

Wu,

Shen,

Wei

et al. 2024

Water Resour Manage

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Reservoir ecological dispatching is a complex system problem with multi-objective, multiplecriteria and multiple-phase. This study establishes a multi-objective ecological dispatching model in Changchun city of Yinma River Basin based on the water demand of social economic development, river ecology, and the constraint of reservoir characteristic parameters. Taking the advantages of particle swarm optimisation (PSO) and genetic algorithm (GA), a PSO-GA hybrid algorithm is proposed and applied to solve the schemes of ecological dispatching model considering different ecological flow requirements. The annual mean scheduling results show that the three scheduling schemes basically achieve the objectives of river ecological base flow scheduling. While for the ecological suitable flow, the guarantee rate of Dehui section in RGOS1/2/3 scheme is 79.79%/87.95%/96.08%, and that of Nongan section is 82.98%/90.85%/96.45%. The scheduling results of typical years show that the water security situation in the study area is not optimistic, but the river ecological environment can be greatly improved by reservoir ecological dispatching. Finally, the high quality and stable search performance of the hybrid PSO-GA proposed in this study is verified by comparing with other algorithms. The mean value and standard of the objective function of the 20 simulation results calculated by PSO-GA are 97.75% and 0.11 respectively, which are better than other algorithms.

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A New Precipitation Prediction Method Based on CEEMDAN-IWOA-BP Coupling

Cited by 10 publications

References 33 publications

Load Day-Ahead Automatic Generation Control Reserve Capacity Demand Prediction Based on the Attention-BiLSTM Network Model Optimized by Improved Whale Algorithm

Load Day-Ahead Automatic Generation Control Reserve Capacity Demand Prediction Based on the Attention-BiLSTM Network Model Optimized by Improved Whale Algorithm

A Combined Model for Water Quality Prediction Based on VMD-TCN-ARIMA Optimized by WSWOA

A Hybrid Particle Swarm Optimization-Genetic Algorithm for Multiobjective Reservoir Ecological Dispatching

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