Comparative study of data-driven short-term wind power forecasting approaches for the Norwegian Arctic region

• Published in: Journal of Renewable and Sustainable Energy
• Main Authors: Chen, Hao, Birkelund, Yngve, Anfinsen, Stian Normann, Yuan, Fuqing
• Format: Article in Journal/Newspaper
• Language: English
• Published: American Institute of Physics 2021
• Subjects: Arctic
• Online Access: https://hdl.handle.net/10037/24533; https://doi.org/10.1063/5.0038429

This paper conducts a systemic comparative study on univariate and multivariate wind power forecasting for five wind farms inside the Arctic area. The development of wind power in the Arctic can help reduce greenhouse gas emissions in this environmentally fragile region. In practice, wind power forecasting is essential to maintain the grid balance and optimize electricity generation. This study first applies various learning methods for wind power forecasting. It comprehensively compares the performance of models categorized by whether considering weather factors in the Arctic. Nine different representative types of machine-learning algorithms make several univariate time series forecasting, and their performance is evaluated. It is demonstrated that machine-learning approaches have an insignificant advantage over the persistence method in the univariate situation. With numerical weather prediction wind data and wind power data as inputs, the multivariate forecasting models are established and made one to six h in advance predictions. The multivariate models, especially with the advanced learning algorithms, show their edge over the univariate model based on the same algorithm. Although weather data are mesoscale, they can contribute to improving the wind power forecasting accuracy. Moreover, these results are generally valid for the five wind farms, proving the models' effectiveness and universality in this regional wind power utilization. Additionally, there is no clear evidence that predictive model performance is related to wind farms' topographic complexity.