A Novel Short Electric Load Forecasting Approach Using a Multivariate Transformer Neural Network and CEEMDAN

Elsayed, Basel Mokhtar; Eladany, Mai Mahmoud; Elfar, Medhat Hegazy; ElDesouky, Azza Ahmed

doi:10.1109/mepcon58725.2023.10462386

2023 24th International Middle East Power System Conference (MEPCON) 2023

DOI: 10.1109/mepcon58725.2023.10462386

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A Novel Short Electric Load Forecasting Approach Using a Multivariate Transformer Neural Network and CEEMDAN

Basel Mokhtar Elsayed,

Mai Mahmoud Eladany,

Medhat Hegazy Elfar

et al.

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2024

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Enhanced Short-Term Load Forecasting: Error-Weighted and Hybrid Model Approach

Yu,

Sun,

et al. 2024

Energies

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To tackle the challenges of high variability and low accuracy in short-term electricity load forecasting, this study introduces an enhanced prediction model that addresses overfitting issues by integrating an error-optimal weighting approach with an improved ensemble forecasting framework. The model employs a hybrid algorithm combining grey relational analysis and radial kernel principal component analysis to preprocess the multi-dimensional input data. It then leverages an ensemble of an optimized deep bidirectional gated recurrent unit (BiGRU), an enhanced long short-term memory (LSTM) network, and an advanced temporal convolutional neural network (TCN) to generate predictions. These predictions are refined using an error-optimal weighting scheme to yield the final forecasts. Furthermore, a Bayesian-optimized Bagging and Extreme Gradient Boosting (XGBoost) ensemble model is applied to minimize prediction errors. Comparative analysis with existing forecasting models demonstrates superior performance, with an average absolute percentage error (MAPE) of 1.05% and a coefficient of determination (R2) of 0.9878. These results not only validate the efficacy of our proposed strategy, but also highlight its potential to enhance the precision of short-term load forecasting, thereby contributing to the stability of power systems and supporting societal production needs.

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Enhanced Short-Term Load Forecasting: Error-Weighted and Hybrid Model Approach

Yu,

Sun,

et al. 2024

Energies

View full text Add to dashboard Cite

show abstract

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A Novel Short Electric Load Forecasting Approach Using a Multivariate Transformer Neural Network and CEEMDAN

Cited by 1 publication

References 16 publications

Enhanced Short-Term Load Forecasting: Error-Weighted and Hybrid Model Approach

Enhanced Short-Term Load Forecasting: Error-Weighted and Hybrid Model Approach

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