A Fuzzy Seasonal Long Short-Term Memory Network for Wind Power Forecasting

Liao, Chin-Wen; Wang, I-Chi; Lin, Kuo-Ping; Lin, Yen-Sheng

doi:10.3390/math9111178

Cited by 9 publications

(4 citation statements)

References 26 publications

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“…Over the past decade, researchers have relentlessly conducted extensive research on sentiment extraction or sentiment categorization. It is the process [6] by which, using various text classification approaches, we may extract and categorize feelings from a particular document, paragraph, phrase, or clause. The technique of extracting opinions, sentiments, attitudes, emotions, etc.…”

Section: A Contextmentioning

confidence: 99%

A Voting classification approach for Sentiment Extraction from Bengali text

2023

IJETER

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Sentiment extraction is one of the most challenging tasks in Natural Language Processing (NLP). It is essential for analysing consumer and user feedback on social media sites and in the commercial world. Finding sentiments or emotions in raw text data and identifying their polarity, or whether they are positive or negative, is the main objective of sentiment extraction. This area has been the focus of various research projects for English and other significant natural languages. In this article, we offer a voting classification method that uses a variety of machine learning classifiers to extract sentiment from Bengali language text. We explored Logistic Regression, Decision Tree Classifier, Random Forest Classifier, Support Vector Classifier, Multinomial Nave Base and Ridge Classifier, and lastly, we used a voting classification strategy to extract sentiments from social media comments.

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Section: A Contextmentioning

confidence: 99%

A Voting classification approach for Sentiment Extraction from Bengali text

2023

IJETER

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“…Ren et al (Ren et al, 2014) proposed an IS-PSO-BP wind speed forecasting model, which achieved good forecasting performance. Liao et al (Liao et al, 2021) introduces fuzzy seasonal index into fuzzy LSTM model has better performance in terms of forecasting accuracy.…”

Section: Introductionmentioning

confidence: 99%

A combined model based on secondary decomposition technique and grey wolf optimizer for short-term wind power forecasting

Zheng²,

Lu³

2023

Front. Energy Res.

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Short-term wind power forecasting plays an important role in wind power generation systems. In order to improve the accuracy of wind power forecasting, many researchers have proposed a large number of wind power forecasting models. However, traditional forecasting models ignore data preprocessing and the limitations of a single forecasting model, resulting in low forecasting accuracy. Aiming at the shortcomings of the existing models, a combined forecasting model based on secondary decomposition technique and grey wolf optimizer (GWO) is proposed. In the process of forecasting, firstly, the complete ensemble empirical mode decomposition adaptive noise (CEEMDAN) and wavelet transform (WT) are used to preprocess the wind power data. Then, least squares support vector machine (LSSVM), extreme learning machine (ELM) and back propagation neural network (BPNN) are established to forecast the decomposed components respectively. In order to improve the forecasting performance, the parameters in LSSVM, ELM, and BPNN are tuned by GWO. Finally, the GWO is used to determine the weight coefficient of each single forecasting model, and the weighted combination is used to obtain the final forecasting result. The simulation results show that the forecasting model has better forecasting performance than other forecasting models.

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“…In [13], a method combining the fuzzy decomposition method with long short-term memory (LSTM) was proposed, which proved the superiority of the proposed method. In [14], considering seasonal factors, an extreme learning machine was constructed. The proposed model can effectively improve the prediction accuracy of an experimental analysis.…”

Section: Introductionmentioning

confidence: 99%

A Short-Term Prediction Model of Wind Power with Outliers: An Integration of Long Short-Term Memory, Ensemble Empirical Mode Decomposition, and Sample Entropy

Zhang

Shao

et al. 2023

Sustainability

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Wind power generation is a type of renewable energy that has the advantages of being pollution-free and having a wide distribution. Due to the non-stationary characteristics of wind power caused by atmospheric chaos and the existence of outliers, the prediction effect of wind power needs to be improved. Therefore, this study proposes a novel hybrid prediction method that includes data correlation analyses, power decomposition and reconstruction, and novel prediction models. The Pearson correlation coefficient is used in the model to analyze the effects between meteorological information and power. Furthermore, the power is decomposed into different sub-models by ensemble empirical mode decomposition. Sample entropy extracts the correlations among the different sub-models. Meanwhile, a long short-term memory model with an asymmetric error loss function is constructed considering outliers in the power data. Wind power is obtained by stacking the predicted values of subsequences. In the analysis, compared with other methods, the proposed method shows good performance in all cases.

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A Fuzzy Seasonal Long Short-Term Memory Network for Wind Power Forecasting

Cited by 9 publications

References 26 publications

A Voting classification approach for Sentiment Extraction from Bengali text

A Voting classification approach for Sentiment Extraction from Bengali text

A combined model based on secondary decomposition technique and grey wolf optimizer for short-term wind power forecasting

A Short-Term Prediction Model of Wind Power with Outliers: An Integration of Long Short-Term Memory, Ensemble Empirical Mode Decomposition, and Sample Entropy

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