RETRACTED ARTICLE: Hourly day-ahead wind power
   forecasting with the EEMD-CSO-LSTM-EFG deep learning technique

Devi, A. Shobana; Maragatham, G.; Boopathi, K.; Rangaraj, A. G.

doi:10.1007/s00500-020-04680-7

Cited by 55 publications

(28 citation statements)

References 35 publications

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“…LSTM can deal with the data with “sequence” properties like time series data, such as daily stock price trend and time domain waveform of mechanical vibration signals. Also, it can process data like the data of natural language with sequence properties consisting of ordered words [ 25 ].…”

Section: Construction Of Prediction Models and Scheme Designmentioning

confidence: 99%

[Retracted] Pop Music Trend and Image Analysis Based on Big Data Technology

Ren

2021

Computational Intelligence and Neuroscience

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With people’s pursuit of music art, a large number of singers began to analyze the trend of music in the future and create music works. Firstly, this study introduces the theory of music pop trend analysis, big data mining technology, and related algorithms. Then, the autoregressive integrated moving (ARIM), random forest, and long-term and short-term memory (LSTM) algorithms are used to establish the image analysis and prediction model, analyze the music data, and predict the music trend. The test results of the three models show that when the singer’s songs are analyzed from three aspects: collection, download, and playback times, the LSTM model can predict well the playback times. However, the LSTM model also has some defects. For example, the model cannot accurately predict some songs with large data fluctuations. At the same time, there is no big data gap between the playback times predicted by the ARIM model image analysis and the actual playback times, showing the allowable error fluctuation range. A comprehensive analysis shows that compared with the ARIM algorithm and random forest algorithm, the LSTM algorithm can predict the music trend more accurately. The research results will help many singers create songs according to the current and future music trends and will also make traditional music creation more information-based and modern.

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Section: Construction Of Prediction Models and Scheme Designmentioning

confidence: 99%

[Retracted] Pop Music Trend and Image Analysis Based on Big Data Technology

Ren

2021

Computational Intelligence and Neuroscience

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“…en, the classification model of LSTM followed by softmax [15] is used to compare the classification effects of the same topic in the upper and lower sentences, and the model accuracy index is shown in Table 1.…”

Section: Supplier Topic Induction Rulesmentioning

confidence: 99%

Evaluation Model of Power Operation and Maintenance Based on Text Emotion Analysis

Wan¹,

Liu

Han

et al. 2021

Mathematical Problems in Engineering

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The application of data mining technology in power field mainly focuses on the application of power defect text and dispatching text. However, the power operation and maintenance data contains a lot of information about power equipment suppliers. Taking the operation and maintenance text involving power equipment suppliers as an example, this paper summarizes the theme of operation and maintenance text and studies the evaluation model of power equipment suppliers. The next sentence prediction analysis model of single round dialogue text based on transformer bidirectional encoder prediction and cosine similarity weighting is proposed, which can effectively divide the topic of dialogue text. Aiming at the semantic richness and complexity of power operation and maintenance text, a supplier evaluation model based on text emotion analysis is proposed. Based on the expansion of the entries and attributes of the existing power ontology dictionary, the dialogue emotion analysis rules are established to realize the normal evaluation of power equipment suppliers.

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“…The proposed approach was tested on five windfarms and produced reasonable outcomes. Devi et al [44] also used ensemble strategy mainly focused on improving the forecasting performance using LSTM-EFG model combined with cuckoo search optimization and ensemble empirical mode decomposition. In general, the ensemble methods are quite helpful in improving forecasting results by combining multiple models but their improved performance is due to the reduction in the variance component of forecasting errors generated by the participating models.…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting renewable energy for environmental resilience through computational intelligence

et al. 2021

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Wind power forecasting plays a key role in the design and maintenance of wind power generation which can directly help to enhance environment resilience. Offshore wind power forecasting has become more challenging due to their operation in a harsh and multi-faceted environment. In this paper, the data generated from offshore wind turbines are used for power forecasting purposes. First, fragmented data is filtered and Deep Auto-Encoding is used to select high dimensional features. Second, a mixture of the CNN and LSTM models is used to train prominent wind features and further improve forecasting accuracy. Finally, the CNN-LSTM deep learning hybrid model is fine-tuned with various parameters for reliable forecasting of wind energy on three different offshore Windfarms. A state-of-the-art comparison against existing models is presented based on root mean square error (RMSE) and mean absolute error (MAE) respectively. The forecasting analyses indicate that the proposed CNN-LSTM strategy is quite successful for offshore wind turbines by retaining the lowest RMSE and MAE along with high forecasting accuracy. The experimental findings will be helpful to design environment resilient energy transition pathways.

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RETRACTED ARTICLE: Hourly day-ahead wind power forecasting with the EEMD-CSO-LSTM-EFG deep learning technique

Cited by 55 publications

References 35 publications

[Retracted] Pop Music Trend and Image Analysis Based on Big Data Technology

[Retracted] Pop Music Trend and Image Analysis Based on Big Data Technology

Evaluation Model of Power Operation and Maintenance Based on Text Emotion Analysis

Forecasting renewable energy for environmental resilience through computational intelligence

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