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

Daniel Vassallo, Raghavendra Krishnamurthy, Harindra J. S. Fernando

Wind energy science 2021 article cited by 27

Source Open-access PDF More in Climate & Environment

importance 5/10 ai assisted Energy Load and Power Forecasting wind energy forecasting stage: research

Problem

Wind speed forecasts suffer errors from atmospheric non-linearities, and it was unclear which atmospheric variables help predict that error.

Why it matters

Accurate wind speed and power forecasting matters for wind energy operations.

What the AI did

A hybrid model uses ARIMA for an initial wind speed forecast, then a random forest predicts the ARIMA error from exogenous atmospheric variables.

Results

Streamwise wind speed, time of day, turbulence intensity, turbulent heat flux, vertical velocity, and wind direction found particularly useful in unison

Impact

Identified physics-based variables that improve forecasting-error prediction.

Record

Venue	Wind energy science
Published	2021
Work type	article
Authors	Daniel Vassallo, Raghavendra Krishnamurthy, Harindra J. S. Fernando
Citations	27 (OpenAlex, at import time)
References	50
Model or method	Random forest combined with ARIMA (hybrid model)
Organization	not stated in source
Primary source	https://doi.org/10.5194/wes-6-295-2021
Open access	gold
Keywords	wind speed forecasting, random forest, ARIMA, atmospheric variables, forecasting error

Importance rating

5 / 10

real but narrower advance

Accurate wind speed/power forecasting matters for wind energy operations, a real but narrower operational advance; error reduced by up to 5% vs bias-corrected ARIMA.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

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