Using an improved Neural Network with Bacterial Foraging Optimization algorithm for Load Shedding

Nguyen, Hoang Minh Vu; Phung, Trieu Tan; Le, Trong Nghia; Nguyen, Ngoc Au; Nguyen, Quang Tien; Nguyen, Phuong Nam

doi:10.1109/icsse58758.2023.10227214

2023 International Conference on System Science and Engineering (ICSSE) 2023

DOI: 10.1109/icsse58758.2023.10227214

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Using an improved Neural Network with Bacterial Foraging Optimization algorithm for Load Shedding

Hoang Minh Vu Nguyen,

Trieu Tan Phung,

Trong Nghia Le

et al.

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2024

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Improved Bacterial Foraging Optimization Algorithm with Machine Learning-Driven Short-Term Electricity Load Forecasting: A Case Study in Peninsular Malaysia

Zaini,

Sulaima,

Razak

et al. 2024

Algorithms

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Accurate electricity demand forecasting is crucial for ensuring the sustainability and reliability of power systems. Least square support vector machines (LSSVM) are well suited to handle complex non-linear power load series. However, the less optimal regularization parameter and the Gaussian kernel function in the LSSVM model have contributed to flawed forecasting accuracy and random generalization ability. Thus, these parameters of LSSVM need to be chosen appropriately using intelligent optimization algorithms. This study proposes a new hybrid model based on the LSSVM optimized by the improved bacterial foraging optimization algorithm (IBFOA) for forecasting the short-term daily electricity load in Peninsular Malaysia. The IBFOA based on the sine cosine equation addresses the limitations of fixed chemotaxis constants in the original bacterial foraging optimization algorithm (BFOA), enhancing its exploration and exploitation capabilities. Finally, the load forecasting model based on LSSVM-IBFOA is constructed using mean absolute percentage error (MAPE) as the objective function. The comparative analysis demonstrates the model, achieving the highest determination coefficient (R2) of 0.9880 and significantly reducing the average MAPE value by 28.36%, 27.72%, and 5.47% compared to the deep neural network (DNN), LSSVM, and LSSVM-BFOA, respectively. Additionally, IBFOA exhibits faster convergence times compared to BFOA, highlighting the practicality of LSSVM-IBFOA for short-term load forecasting.

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Improved Bacterial Foraging Optimization Algorithm with Machine Learning-Driven Short-Term Electricity Load Forecasting: A Case Study in Peninsular Malaysia

Zaini,

Sulaima,

Razak

et al. 2024

Algorithms

View full text Add to dashboard Cite

show abstract

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Using an improved Neural Network with Bacterial Foraging Optimization algorithm for Load Shedding

Cited by 1 publication

References 10 publications

Improved Bacterial Foraging Optimization Algorithm with Machine Learning-Driven Short-Term Electricity Load Forecasting: A Case Study in Peninsular Malaysia

Improved Bacterial Foraging Optimization Algorithm with Machine Learning-Driven Short-Term Electricity Load Forecasting: A Case Study in Peninsular Malaysia

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