A Combined Model of Convolutional Neural Network and Bidirectional Long Short-Term Memory with Attention Mechanism for Load Harmonics Forecasting

Excellence M. Kuyumani (), Ali N. Hasan and Thokozani C. Shongwe
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Excellence M. Kuyumani: Faculty of Engineering and the Built Environment, University of Johannesburg, Johannesburg 2092, South Africa
Ali N. Hasan: Faculty of Engineering and the Built Environment, University of Johannesburg, Johannesburg 2092, South Africa
Thokozani C. Shongwe: Faculty of Engineering and the Built Environment, University of Johannesburg, Johannesburg 2092, South Africa

Energies, 2024, vol. 17, issue 11, 1-23

Abstract: In the increasingly complex and dynamic electrical power system, forecasting harmonics is key to developing and ensuring a clean power supply. The traditional methods have achieved some degree of success. However, they often fail to forecast complex and dynamic harmonics, highlighting the serious need to improve the forecasting performance. Precise forecasting of electrical power system harmonics is challenging and demanding, owing to the increased frequency with harmonic noise. The occurrence of harmonics is stochastic in nature; it has taken a long time for the development of dependable and efficient models. Several machine learning and statistical methods have produced positive results with minimal errors. To improve the prognostic accuracy of the power supply system, this study proposes an organic hybrid combination of a convolutional neural network (CNN) and bidirectional long short-term memory (BiLSTM) with the attention mechanism (AM) method (CNN-BiLSTM-AM) to forecast load harmonics. CNN models intricate non-linear systems with multi-dimensionality aspects. LSTM performs better when dealing with exploding gradients in time series data. Bi-LSTM has two LSTM layers: one layer processes data in the onward direction and the other in the regressive direction. Bi-LSTM uses both preceding and subsequent data, and as a result, it has better performance compared to RNN and LSTM. AM’s purpose is to make desired features outstanding. The CNN-BiLSTM-AM method performed better than the other five methods, with a prediction accuracy of 92.366% and a root mean square error (RMSE) of 0.000000222.

Keywords: AM; Bi-LSTM CNN; harmonics; machine learning; RMSE (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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