Abstract
The power load data from nearby cities are significantly correlated because they share the same hidden variables as well as the underlying noises. A multi-task Gaussian process method for non-stationary time series prediction is introduced and applied to the power load forecasting problem in this paper. The prediction accuracies are effectively improved due to the additional information provided by the related data sets. A novel algorithm for prediction is developed to reduce the computational complexity of the multi-task Gaussian Process method. The algorithm's prediction precision and efficiency are validated by a real world short-term power load data sets.
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