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

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Abstract

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.

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Abbreviations

STLF:

Short-term load forecasting

ANN:

Artificial neural network

BPNN:

Back propagation neural network

SVR:

Support vector regression

SOM:

Self-organized map

RBF:

Radial basis function

ARIMA:

Autoregressive integrated moving average

SARIMA:

Seasonal autoregressive integrated moving average

EMD:

Empirical mode decomposition

EEMD:

Ensemble empirical mode decomposition

IMF:

Intrinsic mode function

WT:

Wavelet transform

PSO:

Particle swarm optimization

SSO:

Shark smell optimization

FPA:

Flower pollination algorithm

HBMO:

Honey bee mating optimization

MA:

Memetic algorithm

MAE:

Mean absolute error

RMSE:

Root-mean-square error

MAPE:

Mean absolute percentage error

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Acknowledgements

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).

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Authors and Affiliations

  1. School of Information Science and Engineering, Lanzhou University, Lanzhou, 730000, China

    Lina Pan, Xiaosu Feng, Fawen Sang, Longjie Li & Xiaoyun Chen

  2. School of Educational Science and Technology, Northwest University for Nationalities, Lanzhou, 730000, China

    Mingwei Leng

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  1. Lina Pan
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Correspondence to Xiaoyun Chen.

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Pan, L., Feng, X., Sang, F. et al. An improved back propagation neural network based on complexity decomposition technology and modified flower pollination optimization for short-term load forecasting. Neural Comput & Applic 31, 2679–2697 (2019). https://doi.org/10.1007/s00521-017-3222-2

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