Electrical Load Prediction using Interval Type-2 Atanassov Intuitionist Fuzzy System: Gravitational Search Algorithm Tuning approach

Khanesar, Mojtaba Ahmedieh; Lu, Jingyi; Smith, Thomas; Branson, David

Abstract

Establishing accurate electrical load prediction is vital for pricing and power system management. However, the unpredictable behavior of private and industrial users results in uncertainty in these power systems. Furthermore, the utilization of renewable energy sources, which are often variable in their production rates, also increases the complexity making predictions even more difficult. In this paper an interval type-2 intuitionist fuzzy logic systems whose parameters are trained in a hybrid fashion using gravitational search algorithms with the ridge least square algorithm is presented for short term prediction of electrical loading. Simulation results are provided to compare the performance of the proposed approach with that of state-of-the-art electrical load prediction algorithms for Poland, and five regions of Australia. The simulation results demonstrate the superior performance of the proposed approach over seven different current state-of-the-art prediction algorithms in literature, namely: SVR, ANN, ELM, EEMD-ELM-GOA, EEMD-ELM-DA, EEMD-ELM-PSO and EEMD-ELM-GWO.

Citation

Khanesar, M. A., Lu, J., Smith, T., & Branson, D. (2021). Electrical Load Prediction using Interval Type-2 Atanassov Intuitionist Fuzzy System: Gravitational Search Algorithm Tuning approach. Energies, 14(12), 1-18. https://doi.org/10.3390/en14123591

• Journal Article Type: Article
• Acceptance Date: Jun 11, 2021
• Online Publication Date: Jun 16, 2021
• Publication Date: Jun 2, 2021
• Deposit Date: Jun 14, 2021
• Publicly Available Date: Jun 14, 2021
• Journal: Energies
• Electronic ISSN: 1996-1073
• Publisher: MDPI
• Peer Reviewed: Peer Reviewed
• Volume: 14
• Issue: 12
• Article Number: 3591
• Pages: 1-18
• DOI: https://doi.org/10.3390/en14123591
• Public URL: https://nottingham-repository.worktribe.com/output/5684842
• Publisher URL: https://www.mdpi.com/1996-1073/14/12/3591