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Long short-term memory-singular spectrum analysis-based model for electric load forecasting

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Abstract

Electrical load forecasting is a key player in building sustainable power systems and helps in efficient system planning. However, the irregular and noisy behavior in the observed data makes it difficult to achieve better forecasting accuracy. To handle this, we propose a new model, named singular spectrum analysis-long short- term memory (SSA-LSTM). SSA is a signal processing technique used to eliminate the noisy components of a skewed load series. LSTM model uses the outcome of SSA to forecast the final load. We have used five publicly available datasets from the Australian Energy Market Operator (AEMO) repository to assess the performance of the proposed model. The proposed model has superior forecasting accuracy compared to other existing state-of-the-art methods [persistence, autoregressive (AR), AR-exogenous, ARMA-exogenous (ARMAX), support vector regression (SVR), random forest (RF), artificial neural network (ANN), deep belief network (DBN), empirical mode decomposition (EMD-SVR), EMD-ANN, ensemble DBN, and dynamic mode decomposition (DMD)] for half-hourly and one day ahead load forecasting using RMSE and MAPE error metrics.

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Abbreviations

AEMO:

Australian Energy Market Operator repository

AR:

Autoregressive model

ARMAX:

Autoregressive Moving Average exogenous

SVR:

Support Vector Regression

RF:

Random Forest

ANN:

Artificial Neural Network

DBN:

Deep Belief Network

EMD-SVR:

Empirical Mode Decomposition-Support Vector Regression

EMD-ANN:

Empirical Mode Decomposition-Artificial Neural Network

EDBN:

ensemble Deep Belief Network

DMD:

Dynamic Mode Decomposition

STLF:

Short-Term Load Forecasting

RBM:

Restricted Boltzmann Machines

IMFs:

Intrinsic Mode Functions

VMD:

Variational Mode Decomposition

SVD:

Singular Value Decomposition

INFS:

Integrated Nonlinear Feature Selection

ANFIS:

Adaptive Neuro-Fuzzy Inference System

SOFM:

self-organizing feature map

\(f_t\) :

Forget gate layer of LSTM

\(i_t\) :

Input gate layer of LSTM

\(C_t\) :

Cell state of LSTM

\(o_t\) :

Output gate of LSTM

\(h_t\) :

Final output of LSTM

Y :

Hankel matrix having equal elements on the diagonals

L :

Window size in constructing the Hankel matrix

EVG:

Eigenvalue Grouping

EV:

Eigenvalues

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Indian Institute of Technology, Patna, Bihar, India

    Neeraj Neeraj, Jimson Mathew, Mayank Agarwal & Ranjan Kumar Behera

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  1. Neeraj Neeraj
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  2. Jimson Mathew
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Neeraj, N., Mathew, J., Agarwal, M. et al. Long short-term memory-singular spectrum analysis-based model for electric load forecasting. Electr Eng 103, 1067–1082 (2021). https://doi.org/10.1007/s00202-020-01135-y

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  • DOI: https://doi.org/10.1007/s00202-020-01135-y

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