Abstract
Abstract. 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 R2 value of 0.84 with true wind speed.
Highlights
Global wind power capacity reached almost 600 GW at the end of 2018 (GWEC, 2019), making wind energy a vital component of international electricity markets
Since wind power production is heavily reliant upon environmental conditions, improvements in wind speed forecasting would allow for more reliable wind power forecasts
This study shows that the forecasting improvement, which comes from prediction of non-linear exogenous error ε, can be directly attributed to prudent feature engineering
Summary
Global wind power capacity reached almost 600 GW at the end of 2018 (GWEC, 2019), making wind energy a vital component of international electricity markets. Integrating wind power into an existing electrical grid is difficult because of wind resource intermittency and forecasting complexity. Forecasting accuracy depends on site conditions, surrounding terrain, and local meteorology. Numerical weather prediction models (NWPs) fail at such complex sites due to a lack of appropriate parameterization schemes suitable for local conditions (Akish et al, 2019; Bianco et al, 2019; Olson et al, 2019; Stiperski et al, 2019; Bodini et al, 2020). Statistical models and computational learning systems (such as an artificial neural network or random forest) are likely better suited to provide accurate power forecasts. Since wind power production is heavily reliant upon environmental conditions, improvements in wind speed forecasting would allow for more reliable wind power forecasts
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