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Using a proper model to characterize a time series is crucial in making accurate predictions. In this work we use time-varying autoregressive process (TVAR) to describe non-stationary time series and model it as a mixture of multiple stable autoregressive (AR) processes. We introduce a new model selection technique based on Gap statistics to learn the appropriate number of AR filters needed to model...
This paper presents three methods for the mapping model for provincial wind power prediction. After correlation analysis of the historical data, several wind farms' output power are found to be principal related to the global provincial wind power. For the first method, curve fitting and weighted average values are used to establish the mapping model. The second method is based on multiple linear...
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