Original scientific paper
https://doi.org/10.37798/202170389
Deep neural network configuration sensitivity analysis in wind power forecasting
Josip Đaković
orcid.org/0000-0002-3214-5197
; HEP - Distribution System Operator, Slavonski Brod, Croatia
Bojan Franc
; Univesity of Zagreb Faculty of electrical engineering and computing, Zagreb, Croatia
Igor Kuzle
orcid.org/0000-0001-8992-4098
; Univesity of Zagreb Faculty of electrical engineering and computing, Zagreb, Croatia
Yongqian Liu
; North China Electric Power University, New Energy School, Beijing, China
Abstract
The trend toward increasing integration of wind farms into the power system is a challenge for transmission and distribution system operators and electricity market operators. The variability of electricity generation from wind farms increases the requirements for flexibility needed for the reliable and stable operation of the power system. Operating a power system with a high share of renewables requires advanced generation and consumpti-on forecasting methods to ensure the reliable and economical operation of the system. Installed wind power capacities require advanced techniques to monitor and control such data-rich power systems. The rapid development of advanced artificial neural networks and data processing capabilities offers numerous potential applications. The effectiveness of advanced deep recurrent neural networks with long-term memory is constantly being demonstrated for learning complex temporal sequence-to-sequence dependencies. This paper presents the application of deep learning methods to wind power production forecasting. The models are trained using historical wind farm generation measurements and NWP weather forecasts for the areas of Croatian wind farms. Furthermore, a comparison of the accuracy of the proposed models with currently used forecasting tools is presented.
Keywords
Hrčak ID:
289134
URI
Publication date:
16.8.2022.
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