nach oben

Evolutionary Intelligence

Erschienen in:

09.01.2019 | Special Issue

Short-term electrical load forecasting method based on stacked auto-encoding and GRU neural network

verfasst von: Kang Ke, Sun Hongbin, Zhang Chengkang, Carl Brown

Erschienen in: Evolutionary Intelligence | Ausgabe 3/2019

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

With the rapid development of smart grid, to solve the power enterprises’ requirement in short-term load forecasting, this paper proposes a short-term electrical load forecasting method based on stacked auto-encoding and GRU (Gated recurrent unit) neural network. Firstly, the method input historical data which contains power load, weather information, and holiday information, and use auto-encoding to compress the historical data; and then, the multi-layer GRU is used to construct the model to predict the power load. The experiment results show, compared with traditional models, the proposed method can effectively predict the daily variation of power load and have lower prediction error and higher precision.

Vorheriger Artikel Research on cloud computing in the resource sharing system of university library services

Nächster Artikel Chemical reaction optimization: survey on variants

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Jetcheva JG, Majidpour M, Chen WP (2014) Neural network model ensembles for building-level electricity load forecasts. Energy Build 84:214–223CrossRef

Mulyadi Y, Abdullah AG, Rohmah KA (2017) Optimize short term load forcasting anomalous based feed forward backpropagation. IOP Conf Ser Mater Sci Eng 180(1):12–76

Akkar HA, Ali WH (2018) Estimation load forecasting based on the intelligent systems. Al-Nahrain J Eng Sci 21(2):285–291CrossRef

Hong T, Wilson J, Xie J (2014) Long term probabilistic load forecasting and normalization with hourly information. IEEE Trans Smart Grid 5(1):456–462CrossRef

Chen F, Xu J, Wang C (2015) Research on building cooling and heating load prediction model on user’s side in energy internet system. Proc CSEE 35(14):3678–3684

Hinton GE, Osindero S, Teh YW (2006) A fast learning algorithm for deep belief nets. Neural Comput 18(7):1527–1554MathSciNetCrossRefMATH

Brown RH, Vitullo SR, Corliss GF et al (2015) Detrending daily natural gas consumption series to improve short-term forecasts. In: Power and energy society general meeting, IEEE. IEEE, pp 1–5

Brown RH, Vitullo SR, Corliss GF, Adya M, Kaefer PE, Povinelli RJ (2015) Detrending daily natural gas consumption series to improve short-term forecasts. In: Power and energy society general meeting, 2015 IEEE (pp 1–5). IEEE

Hong T, Fan S (2016) Probabilistic electric load forecasting: A tutorial review. Int J Forecast 32(3):914–938CrossRef

10.

Peng C, Liu G, Sun H (2012) Wind speed forecasting based on wavelet decomposition and differential evolution-support vector machine for wind farms. Electric Power Autom Equip 32(1):9–13

11.

Sun H, Xie B, Tian Y, Li Z (2017) Forecasting of short-term power load of SecRPSO-SVM based on data-driven. J Syst Simul 29(8):1829–1836

12.

Zhang Y, Hu B, Li S, Luo Y (2017) The short-term power load forecasting model of combing ILMD and ESN based on similar days searching. J Zhengzhou Univ (Nat Sci Edition) 49(2):121–127

13.

Xiao L, Wang J, Hou R, Wu J (2015) A combined model based on data pre-analysis and weight coefficients optimization for electrical load forecasting. Energy 82:524–549CrossRef

14.

Quan H, Srinivasan D, Khosravi A (2014) Short-term load and wind power forecasting using neural network-based prediction intervals. IEEE Trans Neural Netw Learn Syst 25(2):303–315CrossRef

15.

Shi J, Tan T, Guo J, Liu Y, Zhang J (2018) Multi-task learning based on deep architecture for various types of load forecasting in regional energy system integration. Power Syst Technol 42(3):698–707

16.

Xu Y, Fang L, Zhao D, Wang K (2017) Electricity consumption prediction based on LSTM neural networks. Power Systems Big Data 20(8):25–29

17.

Zhang P, Ma X, Zhang W, Lin S, Chen H, Yirun AL, Xiao G (2015) Multimodal fusion for sensor data using stacked autoencoders. In: 2015 IEEE tenth international conference on intelligent sensors, sensor networks and information processing (ISSNIP), pp 1–2. IEEE

18.

Shi X, Zhu Y, Ning X, Wang LW, Sun G, Chen GQ (2016) Transformer fault diagnosis based on deep auto-encoder network. Electric Power Autom Equip 36(5):122–126

19.

Li Y, Huang J, Wang H, Zhong N (2017) Study of emotion recognition based on fusion multi-modal bio-signal with SAE and LSTM recurrent neural network. J Commun 38(12):109–120

20.

Graves A, Schmidhuber J (2005) Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural Netw 18(5–6):602–610CrossRef

21.

Gers FA, Schmidhuber J, Cummins F (1999) Learning to forget: continual prediction with LSTM. In: 9th international conference on artificial neural networks: ICANN ’99, pp 850–855, ICANN

22.

Greff K, Srivastava RK, Koutník J, Steunebrink BR, Schmidhuber J (2017) LSTM: a search space odyssey. IEEE Trans Neural Netw Learn Syst 28(10):2222–2232MathSciNetCrossRef

23.

Niu Z, Yu Z, Li B, Tang W (2018) Short-term wind power forecasting model based on deep gated recurrent unit neural network. Electric Power Autom Equip 38(5):36–42

Titel: Short-term electrical load forecasting method based on stacked auto-encoding and GRU neural network
verfasst von: Kang Ke
Sun Hongbin
Zhang Chengkang
Carl Brown
Publikationsdatum: 09.01.2019
Verlag: Springer Berlin Heidelberg
Erschienen in: Evolutionary Intelligence / Ausgabe 3/2019
Print ISSN: 1864-5909
Elektronische ISSN: 1864-5917
DOI: https://doi.org/10.1007/s12065-018-00196-0

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Weitere Artikel der Ausgabe 3/2019

A risk-driven multi-objective evolutionary approach for selecting software requirements

Oppositional elephant herding optimization with dynamic Cauchy mutation for multilevel image thresholding

Imbalanced data classification algorithm with support vector machine kernel extensions

Implementation of self adaptive mutation factor and cross-over probability based differential evolution algorithm for node localization in wireless sensor networks

Research on cloud computing in the resource sharing system of university library services

FPGA logic design method based on multi resolution image real time acquisition system