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

Mojtaba Ahmadieh Khanesar, Jingyi Lu, Thomas Smith and David Branson
Additional contact information
Mojtaba Ahmadieh Khanesar: Faculty of Engineering, University of Nottingham, Nottingham NG7 2RD, UK
Jingyi Lu: Faculty of Engineering, University of Nottingham, Nottingham NG7 2RD, UK
Thomas Smith: Faculty of Engineering, University of Nottingham, Nottingham NG7 2RD, UK
David Branson: Faculty of Engineering, University of Nottingham, Nottingham NG7 2RD, UK

Energies, 2021, vol. 14, issue 12, 1-18

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 system 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 the literature, namely: SVR, ANN, ELM, EEMD-ELM-GOA, EEMD-ELM-DA, EEMD-ELM-PSO and EEMD-ELM-GWO.

Keywords: electrical load prediction; interval type-2 Atanassov intuitionist fuzzy logic system; ridge least square algorithm; gravitational search algorithm (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: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/1996-1073/14/12/3591/pdf (application/pdf)
https://www.mdpi.com/1996-1073/14/12/3591/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:14:y:2021:i:12:p:3591-:d:576223

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().