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An integrated model based on deep kernel extreme learning machine and variational mode decomposition for day-ahead electricity load forecasting

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Abstract

Accurate short-term electricity load forecasts are critical for the secure and economic operation of power systems. This paper presents a computationally efficient and powerful three-stage model to accurately forecast short-term electricity load. Variational mode decomposition (VMD) was used in the first stage to extract features from the historical load signal. The stacked kernel extreme learning machine (KELM)-based auto-encoders were utilized in the unsupervised feature learning-based second stage. In the third stage, the high-order learned features were used as the inputs for the KELM-based regression model. The proposed deep KELM architecture combines stacked KELM-based auto-encoders and a KELM-based regression model to forecast short-term electricity load effectively. In order to examine the performance improvement of the proposed forecasting model, several performance comparison tests were realized using publicly available electricity load and day-ahead forecast data from the Turkish transmission system operator (TSO). The proposed model results were compared with state-of-the-art deep extreme learning machine (ELM) architectures as well as the benchmark forecasting models based on original ELM, KELM, artificial neural network (ANN), support vector machine (SVM), and regression tree (RT). The comparison results indicated that the proposed model outperformed state-of-the-art architectures and was significantly more successful than the TSO model.

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Data availability

The author confirms that the datasets analyzed during the current study are available in public repositories (see reference [30]).

Abbreviations

AE:

Auto-encoder

ANN:

Artificial neural network

ARMA:

Auto-regressive moving average

BP:

Backpropagation

CSO:

Chicken swarm optimizer

DBM:

Deep Boltzmann machines

DBN:

Deep belief networks

EKF:

Extended Kalman filter

ELM:

Extreme learning machine

ELMAE:

ELM-based AE

EMD:

Empirical mode decomposition

FISTA:

Fast iterative shrinkage-thresholding algorithm

GOA:

Grasshopper optimization algorithm

HELM:

Hierarchical ELM

HW:

Holt–Winters

IMF:

Intrinsic mode function

KELM:

Kernel ELM

KELMAE:

KELM-based AE

LT:

Long term

MA:

Moving average

MABC:

Modified artificial bee colony

MAE:

Mean absolute error

MAPE:

Mean absolute percentage error

MATLAB:

Matrix laboratory

MLELM:

Multilayer ELM

MLKELM:

Multilayer KELM

MP:

Moore–Penrose

MT:

Medium term

PSO:

Particle swarm optimization

R:

Correlation coefficient

RBF:

Radial basis function

RBM:

Restricted Boltzmann machines

RMSE:

Root mean squared error

RT:

Regression tree

SDPSO:

Switching delayed PSO

SLFN:

Single-hidden-layer feed-forward NN

ST:

Short term

SVM:

Support vector machine

TSO:

Transmission system operator

UKF:

Unscented Kalman filter

VMD:

Variational mode decomposition

WT:

Wavelet transform

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  1. Department of Electricity and Energy, Vocational School of Elbistan, Kahramanmaras Istiklal University, 46300, Kahramanmaras, Turkey

    Ceyhun Yıldız

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Yıldız, C. An integrated model based on deep kernel extreme learning machine and variational mode decomposition for day-ahead electricity load forecasting. Neural Comput & Applic 35, 18763–18781 (2023). https://doi.org/10.1007/s00521-023-08702-x

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