Wind Speed Forecast Based on the LSTM Neural Network Optimized by the Firework Algorithm

Shao, Bilin; Song, Dan; Bian, Genqing; Zhao, Yu

doi:10.1155/2021/4874757

Cited by 32 publications

(13 citation statements)

References 33 publications

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“…Hyperparameter tuning is a paramount aspect of ML and has been tested in the context of wind power forecasting using metaheuristic algorithms. Previous works (Shao et al 2021) proposed a firework algorithm-based approach to optimize hyperparameters of LSTM neural networks for wind power forecasting. The authors demonstrated that the proposed approach achieved higher forecasting accuracy compared to other optimization techniques, indicating the importance of hyperparameter tuning for accurate wind power forecasting.…”

Section: Overview Of Research Background and Literature Reviewmentioning

confidence: 99%

Optimizing long-short-term memory models via metaheuristics for decomposition aided wind energy generation forecasting

Pavlov-Kagadejev,

Jovanovic,

Bacanin

et al. 2024

Artif Intell Rev

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Power supply from renewable energy is an important part of modern power grids. Robust methods for predicting production are required to balance production and demand to avoid losses. This study proposed an approach that incorporates signal decomposition techniques with Long Short-Term Memory (LSTM) neural networks tuned via a modified metaheuristic algorithm used for wind power generation forecasting. LSTM networks perform notably well when addressing time-series prediction, and further hyperparameter tuning by a modified version of the reptile search algorithm (RSA) can help improve performance. The modified RSA was first evaluated against standard CEC2019 benchmark instances before being applied to the practical challenge. The proposed tuned LSTM model has been tested against two wind production datasets with hourly resolutions. The predictions were executed without and with decomposition for one, two, and three steps ahead. Simulation outcomes have been compared to LSTM networks tuned by other cutting-edge metaheuristics. It was observed that the introduced methodology notably exceed other contenders, as was later confirmed by the statistical analysis. Finally, this study also provides interpretations of the best-performing models on both observed datasets, accompanied by the analysis of the importance and impact each feature has on the predictions.

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Section: Overview Of Research Background and Literature Reviewmentioning

confidence: 99%

Optimizing long-short-term memory models via metaheuristics for decomposition aided wind energy generation forecasting

Pavlov-Kagadejev,

Jovanovic,

Bacanin

et al. 2024

Artif Intell Rev

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“…A neural network approach can also be used in the prediction of the stochastic time series. In study [25], a firework long short-term memory network is proposed for wind speed prediction. Considering the non-stationary characteristics of the sea wave, empirical mode decomposition is employed as the data preprocessing technique for the long shortterm memory network (EMD-LSTM) in [26].…”

Section: Literature Review For the Stochastic Time Series Predictionmentioning

confidence: 99%

Heavy Tail and Long-Range Dependence for Skewed Time Series Prediction Based on a Fractional Weibull Process

Song,

Chen,

Zio

2023

Fractal Fract

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In this paper, a fractional Weibull process is utilized in a predictive stochastic differential equation model to allow for skewness and heavy-tailed characteristics. To this aim, a fractional Weibull process with non-Gaussian characteristics and a long memory effect is proposed to drive the predictive stochastic differential equation. The difference iterative forecasting model is proposed as its stochastic difference scheme. The consistency, stability, and convergence of the model are analyzed. In the proposed model, variational mode decomposition is utilized as the data preprocessing approach to separate the stationary and non-stationary components. Actual wind speed data and stock price data are employed in two separate case studies.

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“…LSTM is a powerful timeseries prediction algorithm used for genetics [23], windpower prediction [24,25], text processing [26], and human action prediction.…”

Section: Lstmmentioning

confidence: 99%

LSTM input timestep optimization using simulated annealing for wind power predictions

Muneeb

2022

PLoS ONE

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Wind energy is one of the renewable energy sources like solar energy, and accurate wind power prediction can help countries deploy wind farms at particular locations yielding more electricity. For any prediction problem, determining the optimal time step (lookback) information is of primary importance, and using information from previous timesteps can improve the prediction scores. This article uses simulated annealing to find an optimal time step for wind power prediction. Finding an optimal timestep is computationally expensive and may require brute-forcing to evaluate the deep learning model at each time. This article uses simulated annealing to find an optimal time step for wind power prediction. The computation time was reduced from 166 hours to 3 hours to find an optimal time step for wind power prediction with a simulated annealing-based approach. We tested the proposed approach on three different wind farms with a training set of 50%, a validation set of 25%, and a test set of 25%, yielding MSE of 0.0059, 0.0074, and 0.010 for each wind farm. The article presents the results in detail, not just the mean square root error.

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Wind Speed Forecast Based on the LSTM Neural Network Optimized by the Firework Algorithm

Cited by 32 publications

References 33 publications

Optimizing long-short-term memory models via metaheuristics for decomposition aided wind energy generation forecasting

Optimizing long-short-term memory models via metaheuristics for decomposition aided wind energy generation forecasting

Heavy Tail and Long-Range Dependence for Skewed Time Series Prediction Based on a Fractional Weibull Process

LSTM input timestep optimization using simulated annealing for wind power predictions

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