An improved back propagation neural network based on complexity decomposition technology and modified flower pollination optimization for short-term load forecasting

  • Lina Pan
  • Xiaosu Feng
  • Fawen Sang
  • Longjie Li
  • Mingwei Leng
  • Xiaoyun Chen
Original Article


Accurate short-term load forecasting (STLF) is crucial for reliable operation of a power system. Back propagation neural network (BPNN) is widely used in the forecasting field because of its powerful approximation capability. However, due to a variety of unstable factors, electrical time series often exhibit highly noisy and nonlinear characteristics. Usually, a large deviation will be produced when employing single BPNN to capture the complex data pattern. To solve this problem, this paper proposes a new hybrid forecasting approach that combines ensemble empirical mode decomposition (EEMD), chaotic self-adaptive flower pollination algorithm (CSFPA) and BPNN. EEMD is employed to decompose the original load series with the purpose of reducing the forecasting complexity. Developed CSFPA uses logistic equation to produce the chaotic initial population. In addition, aiming at providing a better optimization capability, CSFPA calculates the self-adaptive switch probability at each iteration. The best initial weights and biases of BPNN are provided by the optimization result of CSFPA. The performance of the proposed method is validated by two real-world load data sets from different electricity markets. The numerical results demonstrate that the proposed method outperforms three advanced methods; it is an effective and promising method for STLF.


Short-term load forecasting Hybrid forecasting method Complexity decomposition Back propagation neural network Modified flower pollination algorithm 



Short-term load forecasting


Artificial neural network


Back propagation neural network


Support vector regression


Self-organized map


Radial basis function


Autoregressive integrated moving average


Seasonal autoregressive integrated moving average


Empirical mode decomposition


Ensemble empirical mode decomposition


Intrinsic mode function


Wavelet transform


Particle swarm optimization


Shark smell optimization


Flower pollination algorithm


Honey bee mating optimization


Memetic algorithm


Mean absolute error


Root-mean-square error


Mean absolute percentage error



This research was supported by the National Natural Science Foundation of China (No. 61602225) and Introduce Talents Science Projects for Northwest University for Nationalities (No. xbmuyjrcs201616).

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.


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Copyright information

© The Natural Computing Applications Forum 2017

Authors and Affiliations

  • Lina Pan
    • 1
  • Xiaosu Feng
    • 1
  • Fawen Sang
    • 1
  • Longjie Li
    • 1
  • Mingwei Leng
    • 2
  • Xiaoyun Chen
    • 1
  1. 1.School of Information Science and EngineeringLanzhou UniversityLanzhouChina
  2. 2.School of Educational Science and TechnologyNorthwest University for NationalitiesLanzhouChina

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