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:: Volume 8, Issue 2 (12-2019) ::
ieijqp 2019, 8(2): 65-74 Back to browse issues page
Short term electric load prediction based on deep neural network and wavelet transform and input selection
Farshid Keynia * 1, Gholamreza Memarzadeh
Abstract:   (3076 Views)
Electricity demand forecasting is one of the most important factors in the planning, design, and operation of competitive electrical systems. However, most of the load forecasting methods are not accurate. Therefore, in order to increase the accuracy of the short-term electrical load forecast, this paper proposes a hybrid method for predicting electric load based on a deep neural network with a wavelet transform and input selection. Based on the entropy function. Also, in order to demonstrate the strength of the proposed method, the PJM electricity market and one of the Kerman substations load data in 1395 were used and the results of which emphasized the efficiency of the proposed method in predicting the electric load for production planning And distribution.
Keywords: Load prediction, deep neural network, wavelet transform, input selection, entropy.
Full-Text [PDF 1439 kb]   (1494 Downloads)    
Type of Study: Research |
Received: 2019/01/6 | Accepted: 2019/09/14 | Published: 2019/11/27
References
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keynia F, memarzadeh G. Short term electric load prediction based on deep neural network and wavelet transform and input selection. ieijqp 2019; 8 (2) :65-74
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Volume 8, Issue 2 (12-2019) Back to browse issues page
نشریه علمی- پژوهشی کیفیت و بهره وری صنعت برق ایران Iranian Electric Industry Journal of Quality and Productivity
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