Levy flight-particle swarm optimization-assisted BiLSTM + dropout deep learning model for short-term load forecasting

Kiruthiga, D.; Manikandan, V

doi:10.1007/s00521-022-07751-y

Cited by 9 publications

(2 citation statements)

References 20 publications

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“…The traditional dung beetle optimization algorithm in the population initialization stage adopts the way of generating random numbers to initialize the population position, which leads to the mixed position of dung beetles in the population, and at the same time, it cannot traverse all the positions in the environment, which leads to its poor optimization search and low convergence speed. Considering that the initial position in the charging process of EV has high spatio-temporal randomness, this paper proposes to introduce Bernoulli mapping in the initialization stage of the population [37], and the mathematical expression of Bernoulli mapping can be expressed as:…”

Section: Improvement Of Dbo Optimization Algorithmmentioning

confidence: 99%

Short-Term Load Forecasting of Electric Vehicle Charging Stations Accounting for Multifactor IDBO Hybrid Models

Tang,

Wang,

Qiu

et al. 2024

Energies

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The charging behavior of electric vehicle users is highly stochastic, which makes the short-term prediction of charging load at electric vehicle charging stations difficult. In this paper, a data-driven hybrid model optimized by the improved dung beetle optimization algorithm (IDBO) is proposed to address the problem of the low accuracy of short-term prediction. Firstly, the charging station data are preprocessed to obtain clear and organized load data, and the input feature matrix is constructed using factors such as temperature, date type, and holidays. Secondly, the optimal CNN-BiLSTM model is constructed using convolutional neural network (CNN) and Bi-directional Long Short-Term Memory (BiLSTM), which realizes the feature extraction of the input matrix and better captures the hidden patterns and regularities in it. Then, methods such as Bernoulli mapping are used to improve the DBO algorithm and its hyperparameters; for example, hidden neurons of the hybrid model are tuned to further improve the model prediction accuracy. Finally, a simulation experiment platform is established based on MATLAB R2023a to validate the example calculations on the historical data of EV charging stations in the public dataset of ANN-DATA, and comparative analyses are carried out. The results show that compared with the traditional models such as CNN, BiLSTM and PSO-CNN-BiLSTM, the coefficient of determination of the model exceeds 0.8921 and the root mean square error is maintained at about 4.413 on both the training and test sets, which proves its effectiveness and stability.

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Section: Improvement Of Dbo Optimization Algorithmmentioning

confidence: 99%

Short-Term Load Forecasting of Electric Vehicle Charging Stations Accounting for Multifactor IDBO Hybrid Models

Tang,

Wang,

Qiu

et al. 2024

Energies

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“…For dam intelligent prediction model, it should comprehensively consider the two-way dynamic relationship between input and output at different times and use the new monitoring value to reverse correct the prediction value to improve the prediction effect. Bidirectional long-and short-term neural network (BiLSTM) is an advanced two-way deep learning neural network improved by the LSTM model and can achieve better prediction results than LSTM model [27,28].…”

Section: Forget Gatementioning

confidence: 99%

Dam Safety Evaluation Method after Extreme Load Condition Based on Health Monitoring and Deep Learning

Song

Liu

Yang

2023

Sensors

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The safety operation of dams after extreme load is an important frontier research topic in the field of dam engineering. The dam health monitoring provides a reliable data basis for a safety evaluation after extreme loads. This study proposes a novel data-driven fusion model for a dam safety evaluation after extreme load based on monitoring data derived by sensors. First, the relationship between dam environmental quantity and effect quantity is deeply excavated based on bidirectional long short-term memory (BiLSTM) network, which is a deeply improved LSTM model. Aiming at the parameter optimization problem of BiLSTM model, sparrow search algorithm (SSA), which is an advanced optimization algorithm, is integrated. Second, conducting the constructed SSA-BiLSTM model to estimate the change law of dam effect quantity after the extreme load. Finally, the Mann–Whitney U-test theory is introduced to establish the evaluation criterion of the dam safety state. Project case shows that the multiple quantitative prediction accuracy evaluation indicators of the proposed method are significantly superior to the comparison method, with mean absolute percentage error (MAPE) and mean absolute error (MAE) values decreasing by 30.5% and 27.8%, respectively, on average. The proposed model can accurately diagnose the dam safety state after the extreme load compared with on-site inspection results of the engineering department, which provides a new method for dam safety evaluation.

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Testing and Evaluation of Evolutionary Computing

Zhang,

Wang

2023

Intelligent Information Processing With Matlab

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Levy flight-particle swarm optimization-assisted BiLSTM + dropout deep learning model for short-term load forecasting

Cited by 9 publications

References 20 publications

Short-Term Load Forecasting of Electric Vehicle Charging Stations Accounting for Multifactor IDBO Hybrid Models

Short-Term Load Forecasting of Electric Vehicle Charging Stations Accounting for Multifactor IDBO Hybrid Models

Dam Safety Evaluation Method after Extreme Load Condition Based on Health Monitoring and Deep Learning

Testing and Evaluation of Evolutionary Computing

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