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      Deep-Learning Prediction Model with Serial Two-Level Decomposition Based on Bayesian Optimization

      1 , 2 , 3 , 1 , 2 , 3 , 1 , 2 , 3 , 1 , 2 , 3 , 1 , 2 , 3 , 1 , 2 , 3
      Complexity
      Hindawi Limited

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          Abstract

          The power load prediction is significant in a sustainable power system, which is the key to the energy system’s economic operation. An accurate prediction of the power load can provide a reliable decision for power system planning. However, it is challenging to predict the power load with a single model, especially for multistep prediction, because the time series load data have multiple periods. This paper presents a deep hybrid model with a serial two‐level decomposition structure. First, the power load data are decomposed into components; then, the gated recurrent unit (GRU) network, with the Bayesian optimization parameters, is used as the subpredictor for each component. Last, the predictions of different components are fused to achieve the final predictions. The power load data of American Electric Power (AEP) were used to verify the proposed predictor. The results showed that the proposed prediction method could effectively improve the accuracy of power load prediction.

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          A hybrid annual power load forecasting model based on generalized regression neural network with fruit fly optimization algorithm

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            The damping iterative parameter identification method for dynamical systems based on the sine signal measurement

            Ling Xu (2016)
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              Gaussian Process Regression With Automatic Relevance Determination Kernel for Calendar Aging Prediction of Lithium-Ion Batteries

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                Author and article information

                Contributors
                Journal
                Complexity
                Complexity
                Hindawi Limited
                1076-2787
                1099-0526
                September 14 2020
                September 14 2020
                : 2020
                : 1-14
                Affiliations
                [1 ]School of Artificial Intelligence, Beijing Technology and Business University, Beijing 100048, China
                [2 ]China Light Industry Key Laboratory of Industrial Internet and Big Data, Beijing Technology and Business University, Beijing 100048, China
                [3 ]Beijing Key Laboratory of Big Data Technology for Food Safety, Beijing Technology and Business University, Beijing 100048, China
                Article
                10.1155/2020/4346803
                38dda7e9-ea60-4788-b21c-e87b90de1faa
                © 2020

                http://creativecommons.org/licenses/by/4.0/

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