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Short-Term Load Forecasting Using EEMD-DAE with Enhanced CNN in Smart Grid

Conference paper
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Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1150)

Abstract

Traditional grid moves toward Smart Grid (SG). In traditional grids, electricity was wasted in generation-transmission-distribution. SG is introduced to solve prior issues. In smart grids, how to utilize massive smart meter’s data in order to improve and promote the efficiency and viability of both generation and demand side is a compelling issue. A good forecasting model makes an acceptable use of all characteristics of the electric loads’ data and also reduces dimensionality of that data. Many data-driven methods have been proposed in the literature for load forecasting. In this paper, EEMD based ECNN model is proposed to forecast load of electricity using AEMO data. From the results, ECNN outperforms benchmark methods especially by applying EEMD for decomposition and DAE for feature extraction.

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.COMSATS University IslamabadIslamabadPakistan

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