Abstract
Probabilistic wind power forecasting has become an important tool for optimal economic dispatch and unit commitment of modern power systems with significant renewable energy penetrations. Ensemble forecasting based on Monte Carlo simulation has been widely adopted by grid operators, but other probabilistic approaches, such as multistep iterative wind power forecasting have not yet been fully explored. The associated uncertainty analysis is an important yet challenging issue in this area. This paper proposes to use an analytic interval forecasting framework to estimate the forecasting uncertainty and its propagation with multisteps for two wind farms based on the temporally local Gaussian process (TLGP) model. The key findings confirm that TLGP forecasting not only has better accuracy but is also more reliable and sharp than other benchmark models. This paper provides an innovative analytical framework for iterative multistep interval forecasts.
Highlights
A CCURATE wind power forecasting plays a key role in modern power systems to mitigate the impacts of the stochastic and variable nature of wind energy [1], [2]
This paper mainly focuses on the uncertainty propagation of the temporally local Gaussian process (TLGP) for multi-step iterative forecasting, aiming to provide another innovative way for reliable interval forecasts of wind power generation
It is worth noting the interval forecasts with. Both TLGP and Gaussian Process (GP) are convenient for interval forecasting by nature with no need to calculate each of the single quantile numerically
Summary
A CCURATE wind power forecasting plays a key role in modern power systems to mitigate the impacts of the stochastic and variable nature of wind energy [1], [2]. This paper proposes an iterative interval forecast method based on a variant of Gaussian Process It analyses how the uncertainty propagates and accumulates with iterative multi-step forecasting for the first time and the analytical expression of the uncertainty for each prediction horizon is derived. Considering the computing-efficiency of the iterative forecasting, this TLGP based interval forecast will benefit the real-time power system operation and management due to its high accuracy, reliability and efficiency. TLGP is a non-parametric method proposed to adapt to the time-varying characteristic of the wind power forecasting, to enhance the local forecasting accuracy of the Gaussian Process (GP) and to reduce the computational demand [18]. We provide the Taylor expansion used to estimate the mean value and the variance under a random input
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