Abstract
With the increasing penetration of wind power in modern power systems, sound challenges have emerged for system operators due to the uncertain nature of wind power. Deterministic point forecasting has become less effective to power system operators in terms of information accuracy and reliability. Unlike the conventional methods, a granule-based interval forecasting approach is proposed in this paper, which effectively considers the uncertainties involved in the original time series and regression models, other than only generating a plausible yet less reliable value. By incorporating Extreme Learning Machine (ELM) into the granular model construction, a specific interval can be simply obtained by granular outputs at extremely fast speed. Case studies based on 1-min wind speed time series demonstrate the feasibility of this approach.
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Chai, S., Jia, Y., Xu, Z., Dong, Z. (2016). The Granule-Based Interval Forecast for Wind Speed. In: Cao, J., Mao, K., Wu, J., Lendasse, A. (eds) Proceedings of ELM-2015 Volume 2. Proceedings in Adaptation, Learning and Optimization, vol 7. Springer, Cham. https://doi.org/10.1007/978-3-319-28373-9_22
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DOI: https://doi.org/10.1007/978-3-319-28373-9_22
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