This paper addresses important challenges in wind energy prediction caused by outliers in wind data, which distort the wind turbine power curve and lead to inaccurate performance assessments and suboptimal operation s...
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This paper addresses important challenges in wind energy prediction caused by outliers in wind data, which distort the wind turbine power curve and lead to inaccurate performance assessments and suboptimal operation strategies. The major difficulty here is detecting and eliminating these outliers from complex wind datasets, as inaccurate data can significantly impact forecasting and related activities. To overcome this challenge, the paper proposes a hybrid model combining fuzzy C-means clustering, Mahalanobis distance, and Artificial Neural Networks (ANN) to detect and remove outliers far more accurately than any individual method or other traditional hybrid method, decreasing false alarms and misses. It improves data quality and boosts the reliability of turbine performance analysis, resource assessment, and forecasting, supporting more efficient and sustainable wind-power operations. The results show (1) that the proposed hybrid model achieves 15.4% more accuracy than the other traditional hybrid models in detecting and removing outliers. (2) The proposed hybrid model gives an overall ≈ 116.1% improvement in outlier-detection accuracy over the individual models. (3) Adding the ANN to the proposed hybrid model boosts the outlier-detection accuracy to about a 69.5% relative improvement. (4) Detecting and cleaning outliers by the proposed hybrid model cuts the RMSE from 2.38 to 1.27, reducing prediction error by 46.6%. (5) The advanced hybrid model used in this study for comparison purposes achieves nearly identical accuracy to the proposed hybrid model; it reduces RMSE by ∼0.015 and MAPE by ∼0.04 pp and boosts R² by ∼0.001 while maintaining almost perfect outlier detection (99 % vs. 100 %). Although the advanced model offers a marginal edge in reconstruction quality, the lightweight, scalable proposed hybrid model remains better appropriate for real-world deployment due to its lower computational overhead and more straightforward maintenance.
Deep learning has been successfully applied to many tasks such as image classification, feature learning, Text classification (sentiments analysis or opinion mining) etc. However, little research has focused on extrac...
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ISBN:
(纸本)9781509060146
Deep learning has been successfully applied to many tasks such as image classification, feature learning, Text classification (sentiments analysis or opinion mining) etc. However, little research has focused on extracting polarity of sentiments expressed in text using a multimodal architecture. In other words, no researches take in consideration the multimodal nature of human behaviors before classifying sentiments. The representation of a person (also call User Modeling in some domains such as Intelligent Tutoring Systems) is an important feature to take in consideration if one wants to extract subjective information such as the polarity of sentiments expressed by the person. To design an effective representation of a user, it is important to consider all sources of data informing about its current state. We present a user-sensitive deep multimodal architecture which takes advantage of deep learning and user data to extract a rich latent representation of a user. This rich latent representation mainly helps in text classification tasks. The architecture consists of the combination of a longshort-termmemory (LSTM), LSTM-Auto-Encoder, Convolutional Neural Networks and multiple Deep Neural Networks, in order to support the multimodality of data. The resulting model has been tested on a public multimodal dataset and is able to achieve best results compared to state-of-the-art algorithms for a similar task: detection of opinion polarity. The results suggest that the latent representation learnt from multimodal data helps in the discrimination of polarity of opinion.
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