Wind Power Forecasting Based on LSTM Improved by EMD-PCA-RF

Wang, Dongyu; Cui, Xiwen; Niu, Dongxiao

doi:10.3390/su14127307

Cited by 21 publications

(12 citation statements)

References 41 publications

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“…This technique gives good results for analyzing wind speed data series. By using the EMD algorithm, complex time series data can be decomposed into a limited number of Intrinsic Mode Functions (IMFs) (Wang et al 2022b). EMD process has the following procedure:…”

Section: Empirical Mode Decomposition (Emd)mentioning

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: Empirical Mode Decomposition (Emd)mentioning

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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“…X X [13] Wang LSTM improved by EMD-PCA-RF. X X [14] It should be noted that the research works on electrical power prediction cited above mainly focused on consumption rather than production.…”

Section: [11]mentioning

confidence: 99%

Deep Learning Algorithms to Predict Output Electrical Power of an Industrial Steam Turbine

Fakir

Ennawaoui

Mouden

2022

ASI

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Among the levers carried in the era of Industry 4.0, there is that of using Artificial Intelligence models to serve the energy interests of industrial companies. The aim of this paper is to estimate the active electrical power generated by industrial units that self-produce electricity. To do this, we conduct a case study of the historical data of the variables influencing this parameter to support the construction of three analytical models three analytical models based on Deep Learning algorithms, which are Long Short-Term Memory (LSTM), Convolutional Neural Network (CNN), as well as the hybrid CNN algorithm coupled with LSTM (CNN-LSTM). Subsequently, and thanks to the evaluation of the created models through three mathematical metrics which are Root Mean Square Error (RMSE), Mean Square Error (MSE), and the variance score (R-squared), we were able to make a comparative study between these models. According to the results of this comparison, we attested that the hybrid model is the one that gives the best prediction results, with the following findings: the variance score was about 98.29%, the value of RMSE was exactly 0.1199 MW, and for MSE the error was equal to 0.0143 MW. The obtained results confirm the reliability of the hybrid model, which can help industrial managers save energy by acting upstream of the process parameters influencing the target variable and avoiding substantial energy bills.

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“…The closer the value is to 1, the more information is retained. When the value is 0, the data are forgotten [24]. The following formula can describe the principle of traditional LSTM:…”

Section: Model Introductionmentioning

confidence: 99%

Wind Power Short-Term Forecasting Method Based on LSTM and Multiple Error Correction

2023

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To improve the accuracy of short-term wind power prediction, a short-term wind power prediction model based on the LSTM model and multiple error correction is proposed. First, an affine wind power correction model based on assimilative migration is established to reduce the errors caused by false positives from the initial data. Then, a self-moving window LSTM prediction model based on the improved particle swarm optimization algorithm was established. By improving the particle swarm optimization algorithm, the optimal hidden neuron number and the optimal learning rate of the LSTM model were calculated to enhance the model’s accuracy. Definitively, the idea of error feedback prediction is used to correct the initial prediction error, and the prediction error is fed back to the LSTM model to reduce the error caused by the calculation of the LSTM model. By starting from the initial data error, model accuracy error, and model prediction error, multiple error correction of wind power is realized to improve the model accuracy. The simulation results show that the method improves the model’s prediction accuracy by using assimilative transfer and error feedback, contributing to the economic operation and sustainable development of the power system. Unlike traditional improvement ideas, the proposed improvement ideas do not involve the inherent characteristics of the original prediction methods. This method does not need to introduce other auxiliary methods and has good universality.

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Wind Power Forecasting Based on LSTM Improved by EMD-PCA-RF

Cited by 21 publications

References 41 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

Deep Learning Algorithms to Predict Output Electrical Power of an Industrial Steam Turbine

Wind Power Short-Term Forecasting Method Based on LSTM and Multiple Error Correction

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