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Multi-step least squares support vector machine modeling approach for forecasting short-term electricity demand with application

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Abstract

Electricity demand forecasting plays a crucial role in the operation of electrical power systems because it can provide management decisions related to load switching and power grid. Thus, there have been models developed to estimate the electricity demand. However, inaccurate demand forecasting may raise the operating cost of electric power sector, which means that it would waste considerable money. In this paper, a novel modeling framework was proposed for forecasting electricity demand. Sample entropy was developed to identify the nonlinearity and uncertainty in the original time series, after that redundant noise was removed through a decomposition technique. Besides, the most optimal modes of original series and the optimal input form of the model were determined by the feature selection method. Finally, electricity demand series can be conducted forecasting through least squares support vector machine tuned by multi-objective sine cosine optimization algorithm. The case studies of Australia demonstrated that the proposed framework can ensure high accuracy and strong stability. Thus, it can be considered as a useful tool for electricity demand forecasting.

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Abbreviations

MBE:

Mean bias error

QLD:

Queensland

SE:

Sample entropy

SA:

South Australia

NSW:

New South Wales

VMs:

Variational modes

MAE:

Mean absolute error

SCA:

Sine–cosine algorithm

PSR:

Phase space reconstruction

SVM:

Support vector machine

ANN:

Artificial neural network

LSSVM:

Least squares support vector machine

ADMM:

Alternate direction method of multipliers

ARIMA:

Autoregressive integrated moving average

SARIMA:

Multi-objective sine cosine algorithm

VMD:

Variational mode decomposition

SE:

Sample entropy

VM:

Variational modes

DWT:

Discrete wavelet transform

NWP:

Numerical weather prediction

EMD:

Ensemble mode decomposition

SVR:

Support vector regression

THI:

Theil inequality coefficient

MAPE:

Mean absolute percentage error

PACF:

Partial autocorrelation function

VMD:

Variational mode decomposition

BPNN:

Backpropagation neural network

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Authors and Affiliations

  1. School of Economics and Management, Hebei University of Technology, Tianjin, 300401, China

    Ranran Li

  2. Chinese Academy of Social Sciences, Beijing, 100732, China

    Xueli Chen

  3. Lithuanian Institute of Agrarian Economics, Vilnius, Lithuania

    Tomas Balezentis & Dalia Streimikiene

  4. Shanghai University of Finance and Economics, Shanghai, 200433, China

    Zhiyong Niu

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  1. Ranran Li
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  5. Zhiyong Niu
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Correspondence to Zhiyong Niu.

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Li, R., Chen, X., Balezentis, T. et al. Multi-step least squares support vector machine modeling approach for forecasting short-term electricity demand with application. Neural Comput & Applic 33, 301–320 (2021). https://doi.org/10.1007/s00521-020-04996-3

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