Global optimality conditions and optimization methods for constrained polynomial programming problems

Wu, Zhiyou; Tian, Jing; Ugon, Julien; Zhang, Liang

doi:10.1016/j.amc.2015.04.040

Cited by 2 publications

(1 citation statement)

References 24 publications

(40 reference statements)

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“…Therefore, it is necessary to improve the reliability and the forecasting accuracy of smart grids [18][19][20]. Reliable EL forecasting is able to decrease energy consumption and reduce environmental pollution [21][22][23][24].…”

Section: Introductionmentioning

confidence: 99%

Research and Application of Hybrid Forecasting Model Based on an Optimal Feature Selection System—A Case Study on Electrical Load Forecasting

Dong

Wang

et al. 2017

Energies

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Abstract:The process of modernizing smart grid prominently increases the complexity and uncertainty in scheduling and operation of power systems, and, in order to develop a more reliable, flexible, efficient and resilient grid, electrical load forecasting is not only an important key but is still a difficult and challenging task as well. In this paper, a short-term electrical load forecasting model, with a unit for feature learning named Pyramid System and recurrent neural networks, has been developed and it can effectively promote the stability and security of the power grid. Nine types of methods for feature learning are compared in this work to select the best one for learning target, and two criteria have been employed to evaluate the accuracy of the prediction intervals. Furthermore, an electrical load forecasting method based on recurrent neural networks has been formed to achieve the relational diagram of historical data, and, to be specific, the proposed techniques are applied to electrical load forecasting using the data collected from New South Wales, Australia. The simulation results show that the proposed hybrid models can not only satisfactorily approximate the actual value but they are also able to be effective tools in the planning of smart grids.

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