Utilizing physics-based input features within a machine learning model to predict wind speed forecasting error [electronic resource]

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Tác giả:

Ngôn ngữ: eng

Ký hiệu phân loại: 333.9 Other natural resources

Thông tin xuất bản: Richland, Wash. : Oak Ridge, Tenn. : Pacific Northwest National Laboratory (U.S.) ; Distributed by the Office of Scientific and Technical Information, U.S. Dept. of Energy, 2021

Mô tả vật lý: Size: p. 295-309 : , digital, PDF file.

Bộ sưu tập: Metadata

ID: 257105

 Machine learning is quickly becoming a commonly used technique for wind speed and power forecasting. Many machine learning methods utilize exogenous variables as input features, but there remains the question of which atmospheric variables are most beneficial for forecasting, especially in handling non-linearities that lead to forecasting error. This question is addressed via creation of a hybrid model that utilizes an autoregressive integrated moving-average�(ARIMA) model to make an initial wind speed forecast followed by a random forest model that attempts to predict the ARIMA forecasting error using knowledge of exogenous atmospheric variables. Variables conveying information about atmospheric stability and turbulence as well as inertial forcing are found to be useful in dealing with non-linear error prediction. Streamwise wind speed, time of day, turbulence intensity, turbulent heat flux, vertical velocity, and wind direction are found to be particularly useful when used in unison for hourly and 3 h timescales. The prediction accuracy of the developed ARIMA?random forest hybrid model is compared to that of the persistence and bias-corrected ARIMA models. The ARIMA?random forest model is shown to improve upon the latter commonly employed modeling methods, reducing hourly forecasting error by up to 5 % below that of the bias-corrected ARIMA model and achieving an R<
 sup>
 2<
 /sup>
 �value of�0.84 with true wind speed.
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