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Electrical load modeling and forecasting are critically important in the electrical network and smart grid. The sparse Bayesian Learning (SBL) algorithm can be utilized to model and forecast the electrical load behavior. The SBL algorithm can solve a sparse weight vector with respect to a kernel matrix for modeling electricity consumption. However, traditional SBL can only handle an electricity consumption...
The data traffic for wireless metering in smart grid is considered. A dynamic spectrum allocation scheme is proposed for the power load prediction. Two approaches, namely batch mode allocation and sequential allocation, are proposed with various criteria. Numerical results show that the total cost can be significantly reduced while the performance of load prediction is guaranteed.
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