A comparison of wind forecasting methods for Norwegian on-shore wind : a perspective into the nuances in wind speed to power conversion and the economic costs associated with wind forecast accuracy

2020

This paper examines various short-term forecasting methods to forecast hourly wind energy production

in Norway. Performance of forecasting methods were compared across different months,

through different evaluation metrics, to analyze the uniformity and dependability of methods. More

than a decade of hourly wind speed data spanning across 69 locations along the Norwegian coast

were utilized in the study.

Given the upcoming integration of EU and Nordic intraday electricity markets into the Cross-

Border Intraday market (XBID), the study focuses on one-hour-ahead forecasting to be in alignment

with the operations of intraday electricity market. With the final objective of predicting power

production, a customized loss function, Power Curve Conversion Error with penalty, which takes

into account both wind speed to power conversion and economic cost associated with over and under

forecast, is included as part of the evaluation metrics to capture the true value of each model’s

predictions.

Forecasting methods undertaken consist of a mix of Statistical and Machine-Learning methods,

with Naive forecasting used as the overall benchmark model. Other Statistical methods are

ARIMA, and ARIMAX which includes the use of seasonal and time of day dummies. In terms

of ML methods, Gradient Boosted Trees, Extremely Randomized Forest, and Neural Network

are selected. Finally, a hybrid model of ARIMAX and Extremely Randomized Forest is also

formulated. These methods are then evaluated on multiple evaluation metrics, namely: RMSE,

MAE, Classification Accuracy, and the Power Curve Conversion Error with penalty.

The general implication of the study reveals that accuracy of models are consistent with their

required computational intensity, with ML models outperforming statistical methods in most situations.

The findings also suggest the Hybrid model to be the most suitable forecasting method for

one-hour-ahead forecast under almost all evaluation metrics employed. This conclusion holds true

for wind power forecasting under different seasons of the year as well.