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Soft Computing

, Volume 22, Issue 12, pp 4099–4112 | Cite as

A multiple time series-based recurrent neural network for short-term load forecasting

  • Bing Zhang
  • Jhen-Long Wu
  • Pei-Chann Chang
Methodologies and Application
  • 670 Downloads

Abstract

Electricity, an indispensable resource in daily life and industrial production, is hard to store, so accurate short-term load forecasting (STLF) plays a vital role in resource allocation, capital budgeting of power companies, energy deployment and government control. In recent decades, the strong dependency relationships of time series have been considered in many researches, but the discrete information has not proven to be very useful in their experiments. In general, while discrete information is weak, it can provide macro trends compared to the micro trends of continuous information. In this research, we aim to combine macro and micro information by continuous and discrete time series to generate multiple time series (MTS). The MTS comprise four information sequences: short-term, cycle, long short-term and cross-long short-term. These MTS are used to build a STLF system using a recurrent neural network (RNN) model that can learn sequential information between continuous and discrete series. Therefore, the RNN model with MTS information can improve the forecasting performance for short-term load forecasting. The experimental results show that our proposed forecasting system outperforms the state-of-the-art approach.

Keywords

Multiple time series Deep learning Short-term load forecasting Recurrent neural networks Long short-term memory Gated recurrent units 

Notes

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.School of Information Science & EngineeringYun-Nan UniversityYun ZanChina
  2. 2.Institute of Information ScienceAcademia SinicaTaipeiTaiwan, ROC
  3. 3.Innovation Center for Big Data and Digital Convergence, Department of Information ManagementYuan Ze UniversityTaoyuanTaiwan, ROC
  4. 4.Software SchoolNanchang UniversityNanchangChina
  5. 5.Office of Academic AffairsZhuhai College of Beijing Institute of TechnologyZhuhaiChina

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