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• W4387230459 abstract "The restored interest now receives renewable energy due to the global decline in greenhouse gas emanations and fossil fuel combustion. The fasted growing energy source, wind energy generation, is recognized as a clean energy source that has grown fast and is used extensively in wind power-producing facilities. This study’s short-term wind speed estimations are made using a multivariate model based on an artificial neural network (ANN) that combines several local measurements, including wind speed, wind direction, LV active power, and theoretical power curve. The dataset was received from Turkey’s SCADA system at 10-min intervals, and the actual data validated the expected performance. The research took wind speed into account as an input parameter and created a multivariate model. To perform prediction outcomes on time series data, an algorithm such as an artificial neural network (ANN) is utilized. The experiment verdicts reveal that the ANN algorithm produces reliable predicting results when metrics like 0.693 for MSE, 0.833 for RMSE and 0.96 for R-squared or Co-efficient of determination are considered." @default.
• W4387230459 created "2023-10-02" @default.
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• W4387230459 date "2023-09-30" @default.
• W4387230459 modified "2023-10-12" @default.
• W4387230459 title "A New ANN Technique for Short-Term Wind Speed Prediction Based on SCADA System Data in Turkey" @default.
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• W4387230459 doi "https://doi.org/10.3390/atmos14101516" @default.
• W4387230459 hasPublicationYear "2023" @default.
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