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mdpi.com
article
https://www.mdpi.com/2076-3417/14/8/3270
No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. Editors select a small number of articles recently published in the journal that they believe will be particularly interesting to readers, or important in the respective research area. The aim is to provide a snapshot of some of the most exciting work published in the various research areas of the journal. In this study, we leverage SCADA data from diverse wind turbines to predict power output, employing advanced time series methods, specifically Functional Neural Networks (FNN) and Long Short-Term Memory (LSTM) networks. In this study, we leverage SCADA data from 13 wind turbines located in a wind farm in India to predict the power output of the wind turbines, employing advanced time series methods, specifically Functional Neural Networks (FNN) [16,17] and Long Short-Term Memory (LSTM) networks [18]. We follow this the results of the power output prediction for the wind turbines and the deterioration detection for them in Section 3.
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agris.fao.org
article
https://agris.fao.org/search/en/providers/122436/records/6759c1860ce2cede71ca…
In this study, we leverage SCADA data from diverse wind turbines to predict power output, employing advanced time series methods, specifically Functional
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asmedigitalcollection.asme.org
article
https://asmedigitalcollection.asme.org/gasturbinespower/article/145/12/121005…
In this work, we predict the gas turbine power, blade-path temperature, and other performance variables using time series analysis.
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greensolver.net
article
https://greensolver.net/articles/wind-farm-performance-analysis
By plotting a time series of production data, measured at a wind turbine considered as reference (WTG1) against another series of production
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researchgate.net
research
https://www.researchgate.net/publication/376104857_RNN-Based_Time_Series_Anal…
This study proposes a deep learning (DL) approach based on recurrent neural networks (RNNs) for long-term wind power forecasting utilizing
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nature.com
article
https://www.nature.com/articles/s41598-026-49373-7
# Enhancing wind and solar energy forecasting through time-series feature engineering and ensemble machine learning. Accurate short- and medium-term forecasting of renewable energy generation is essential for ensuring grid stability, operational planning, and efficient energy management. This study presents a comprehensive time-series forecasting framework for wind and solar power production based on systematic feature engineering and advanced machine learning and deep learning models. The framework integrates lagged production variables, rolling-window statistics, calendar features, and cyclic temporal encodings to capture temporal dependencies and seasonal patterns. Forecasting horizons ranging from 1 h to 24 h are investigated using an expanding-window time-series cross-validation strategy to ensure robust and leakage-free evaluation. Experiments are conducted on a multi-year renewable energy production dataset comprising hourly wind and solar power generation records collected between 2020 and 2025. A broad set of baseline models, including statistical approaches such as ARIMA, machine-learning regressors such XGBoost, LightGBM, and CatBoost, and sequence learning architectures such as LSTM, are implemented and evaluated under identical preprocessing and validation protocols.
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sciencedirect.com
article
https://www.sciencedirect.com/science/article/abs/pii/S0306261925012656
In wind power forecasting [1], [2], time series prediction typically involves estimating future generation capacity based on historical data
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iit.comillas.edu
research
https://www.iit.comillas.edu/documentacion/workingpaper/IIT-17-102A/Performan…
12TH WORKSHOP ON INDUSTRIAL SYSTEMS AND ENERGY TECHNOLOGIES (JOSITE2017), MADRID, SPAIN 1 Performance Analysis and Anomaly Detection in Wind Turbines based on Neural Networks and Principal Component Analysis Peyman Mazidi, Student Member, IEEE, Lina Bertling Tjernberg, Senior Member, IEEE, and Miguel A. Sanz-Bobi, Senior Member, IEEE Abstract—This paper proposes an approach for maintenance management of wind turbines based on their life. The proposed approach uses performance analysis and anomaly detection (PAAD) which can detect anomalies and point out the origin of the detected anomalies. This PAAD algorithm utilizes neural network (NN) technique in order to detect anomalies in the performance of the wind turbine (system layer), and then applies principal component analysis (PCA) technique to uncover the root of the detected anomalies (component layer). To validate the accuracy of the proposed algorithm, SCADA data obtained from online condition monitoring of a wind turbine are utilized. Reducing time and cost of maintenance and increasing availability and in return profits in form of savings are some of the benefits of the proposed PAAD algorithm.