Long-term Wind Power Forecasting using Tree-based Learning Algorithms

• Published in: IEEE access Vol. 8; p. 1
• Main Authors: Ahmadi, Amirhossein, Nabipour, Mojtaba, Mohammadi-ivatloo, Behnam, Amani, Ali Moradi, Rho, Seungmin, Piran, Md. Jalil
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Algorithms; Forecasting; Geographical locations; Long-term; Machine learning; Machine learning algorithms; Mathematical models; Model accuracy; Predictive models; Regression; Sampling; Wind energy; Wind forecasting; Wind power; Wind power forecasting; Wind power generation; Wind speed
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.3017442

The intermittent and uncertain nature of wind places a premium on accurate wind power forecasting for the reliable and efficient operation of power grids with large-scale wind power penetration. Herein, six-month-ahead wind power forecasting models were developed using tree-based learning algorithms. Three models were developed to investigate the impact of input data on forecasting accuracy. The first model was trained with the average and standard deviation of wind speed values measured at a height of 40 m with a 10-min sampling time. To evaluate the impact of sampling time on model performance, a second model was trained with wind speed values measured at a height of 40 m with 1-h, 12-h, and 24-h sampling times. To assess the effect of measuring height on model accuracy, the third model was trained with wind speed values measured at 40 m extrapolated from values measured at heights of 30 m and 10 m. Experiments revealed that using longer time intervals and height extrapolation leads to considerable accuracy degradation in forecasted models. Finally, to study the generalization ability of the forecasted models, they were tested against wind data measured at heights and locations different from what the models had been trained with. Simulation results substantiated that tree-based learning algorithms can be successfully adopted not only for long-term wind power forecasting, but for potential wind power forecasting at different heights and geographical locations.