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Electrical load forecasting is vitally important to modern power system planning, operation, and control. In this paper, by focusing on historical load data and calendar factors, we present a hybrid method using period refinement scheme and adaptive strategy for building peak hour period and off-peak hour period models in day-of-week for one-day-ahead for load forecasting. They are evaluated using...
In the pursuit of high precision in a modern CNC machining system, it is significant to eliminate thermal error. The paper first makes a brief outline of the characteristics and the training methods of neural networks. And it is successful to apply the neural network model to model the thermal error in the CNC machine linear feed system. The expected results is achieved that the maximum prediction...
Traditional neural network approaches for traffic flow forecasting are usually single task learning (STL) models, which do not take advantage of the information provided by related tasks. In contrast to STL, multitask learning (MTL) has the potential to improve generalization by transferring information in training signals of extra tasks. In this paper, MTL based neural networks are used for traffic...
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