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Ultra-Short-Term Load Demand Forecast Model Framework Based on Deep Learning

Energies (Basel), 2020-09, Vol.13 (18), p.4900 [Peer Reviewed Journal]

2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1996-1073 ;EISSN: 1996-1073 ;DOI: 10.3390/en13184900

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  • Title:
    Ultra-Short-Term Load Demand Forecast Model Framework Based on Deep Learning
  • Author: Li, Hongze ; Liu, Hongyu ; Ji, Hongyan ; Zhang, Shiying ; Li, Pengfei
  • Subjects: Accuracy ; Algorithms ; Artificial intelligence ; Convolution ; Deep learning ; Demand ; Economic forecasting ; Errors ; Feature extraction ; Forecasting ; gate recurrent unit ; long short-term memory ; Machine learning ; Mathematical models ; Meteorological data ; Neural networks ; Optimization ; Parameters ; Prediction models ; Risk reduction ; Short term memory ; Time series ; Training ; ultra-short-term load forecast
  • Is Part Of: Energies (Basel), 2020-09, Vol.13 (18), p.4900
  • Description: Ultra-short-term load demand forecasting is significant to the rapid response and real-time dispatching of the power demand side. Considering too many random factors that affect the load, this paper combines convolution, long short-term memory (LSTM), and gated recurrent unit (GRU) algorithms to propose an ultra-short-term load forecasting model based on deep learning. Firstly, more than 100,000 pieces of historical load and meteorological data from Beijing in the three years from 2016 to 2018 were collected, and the meteorological data were divided into 18 types considering the actual meteorological characteristics of Beijing. Secondly, after the standardized processing of the time-series samples, the convolution filter was used to extract the features of the high-order samples to reduce the number of training parameters. On this basis, the LSTM layer and GRU layer were used for modeling based on time series. A dropout layer was introduced after each layer to reduce the risk of overfitting. Finally, load prediction results were output as a dense layer. In the model training process, the mean square error (MSE) was used as the objective optimization function to train the deep learning model and find the optimal super parameter. In addition, based on the average training time, training error, and prediction error, this paper verifies the effectiveness and practicability of the load prediction model proposed under the deep learning structure in this paper by comparing it with four other models including GRU, LSTM, Conv-GRU, and Conv-LSTM.
  • Publisher: Basel: MDPI AG
  • Language: English
  • Identifier: ISSN: 1996-1073
    EISSN: 1996-1073
    DOI: 10.3390/en13184900
  • Source: GFMER Free Medical Journals
    ROAD: Directory of Open Access Scholarly Resources
    ProQuest Central
    DOAJ Directory of Open Access Journals
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