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Online training algorithms based single multiplicative neuron model for energy consumption forecasting

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  • Wu, Xuedong
  • Mao, Jianxu
  • Du, Zhaoping
  • Chang, Yanchao

Abstract

Although traditional approaches can yield accurate forecasting results of energy consumption, they may suffer from several limitations such as the need for large dataset and the linear assumption. Two novel hybrid dynamic approaches, which are based on the SMN (single multiplicative neuron) model and the iterated nonlinear filters, have been proposed for forecasting energy consumption with small dataset and nonlinearity in our study. The forecasting models are established by using the weights and the biases of SMN model to present the state vector and the output of SMN model to present the observation equation, and the input vector to the SMN model is composed of the known energy consumption values with a rolling mechanism. The SMN model has advantages of better approximation capabilities, simpler network structures and faster learning algorithms. The nonlinear filters can deal with additive noises and can update model parameters when a new observation data arrives due to their iterative algorithm structure. Two case studies of energy consumption have been used to demonstrate the reliability of the proposed models, and the experimental results have indicated that the proposed approaches outperform existing models in forecasting energy consumption.

Suggested Citation

  • Wu, Xuedong & Mao, Jianxu & Du, Zhaoping & Chang, Yanchao, 2013. "Online training algorithms based single multiplicative neuron model for energy consumption forecasting," Energy, Elsevier, vol. 59(C), pages 126-132.
    • Handle: RePEc:eee:energy:v:59:y:2013:i:c:p:126-132
    DOI: 10.1016/j.energy.2013.06.068
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    References listed on IDEAS

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    1. Tso, Geoffrey K.F. & Yau, Kelvin K.W., 2007. "Predicting electricity energy consumption: A comparison of regression analysis, decision tree and neural networks," Energy, Elsevier, vol. 32(9), pages 1761-1768.
    2. Pao, H.T., 2009. "Forecasting energy consumption in Taiwan using hybrid nonlinear models," Energy, Elsevier, vol. 34(10), pages 1438-1446.
    3. Lee, Yi-Shian & Tong, Lee-Ing, 2012. "Forecasting nonlinear time series of energy consumption using a hybrid dynamic model," Applied Energy, Elsevier, vol. 94(C), pages 251-256.
    4. Christof Koch, 1997. "Computation and the single neuron," Nature, Nature, vol. 385(6613), pages 207-210, January.
    5. Ekonomou, L., 2010. "Greek long-term energy consumption prediction using artificial neural networks," Energy, Elsevier, vol. 35(2), pages 512-517.
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    Cited by:

    1. Wang, Delu & Wang, Yadong & Song, Xuefeng & Liu, Yun, 2018. "Coal overcapacity in China: Multiscale analysis and prediction," Energy Economics, Elsevier, vol. 70(C), pages 244-257.
    2. Atif Maqbool Khan & Magdalena Osińska, 2021. "How to Predict Energy Consumption in BRICS Countries?," Energies, MDPI, vol. 14(10), pages 1-21, May.
    3. Zeng, Chunlei & Wu, Changchun & Zuo, Lili & Zhang, Bin & Hu, Xingqiao, 2014. "Predicting energy consumption of multiproduct pipeline using artificial neural networks," Energy, Elsevier, vol. 66(C), pages 791-798.
    4. Suat Ozturk & Feride Ozturk, 2018. "Forecasting Energy Consumption of Turkey by Arima Model," Journal of Asian Scientific Research, Asian Economic and Social Society, vol. 8(2), pages 52-60, February.

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