Publication: Long Term Load Forecasting using Grey Wolf Optimizer - Artificial Neural Network
| dc.citedby | 4 | |
| dc.contributor.author | Yasin Z.M. | en_US |
| dc.contributor.author | Salim N.A. | en_US |
| dc.contributor.author | Ab Aziz N.F. | en_US |
| dc.contributor.authorid | 57211410254 | en_US |
| dc.contributor.authorid | 36806685300 | en_US |
| dc.contributor.authorid | 57221906825 | en_US |
| dc.date.accessioned | 2023-05-29T07:23:06Z | |
| dc.date.available | 2023-05-29T07:23:06Z | |
| dc.date.issued | 2019 | |
| dc.description | Electric power plant loads; Heuristic methods; Learning algorithms; Neural networks; Particle swarm optimization (PSO); Wind; Accurate prediction; Electrical load; Learning rates; Load forecasting; Long-term load forecasting; Mean absolute percentage error; Meta-heuristic techniques; Optimizers; Forecasting | en_US |
| dc.description.abstract | This paper presents a new technique namely Grey Wolf Optimizer- Artificial Neural Network (GWO-ANN) as a technique to forecast electrical load. GWO is a meta heuristic technique inspired by the hierarchy of leadership of the grey wolf hunting mechanism in nature. Four types of grey wolves such as alpha, beta, delta, and omega are employed for simulating the leadership hierarchy. In addition, the three main steps of hunting, searching for prey, encircling are also imitated in the algorithm. GWO is utilized to determine the optimal momentum rate and learning rate of ANN for accurate prediction. In the ANN configuration, the temperature, humidity, wind speed, maximum power, and average power were used as the input data. While total power was used as the output data. ANN is trained by adjusting the parameters of momentum rate and learning rate until the output data matches the actual data. The performance of GWO-ANN was compared to the performance of ANN and Particle Swarm Optimization - Artificial Neural Network (PSO-ANN). The results showed GWO-ANN provide better result in terms of the Mean Absolute Percentage Error (MAPE) and coefficients of determination (R2) as compared to other methods. � 2019 IEEE. | en_US |
| dc.description.nature | Final | en_US |
| dc.identifier.ArtNo | 8952051 | |
| dc.identifier.doi | 10.1109/ICOM47790.2019.8952051 | |
| dc.identifier.scopus | 2-s2.0-85078834809 | |
| dc.identifier.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85078834809&doi=10.1109%2fICOM47790.2019.8952051&partnerID=40&md5=8e145860bb7b0af181c030a946e958fe | |
| dc.identifier.uri | https://irepository.uniten.edu.my/handle/123456789/24378 | |
| dc.publisher | Institute of Electrical and Electronics Engineers Inc. | en_US |
| dc.source | Scopus | |
| dc.sourcetitle | 2019 7th International Conference on Mechatronics Engineering, ICOM 2019 | |
| dc.title | Long Term Load Forecasting using Grey Wolf Optimizer - Artificial Neural Network | en_US |
| dc.type | Conference Paper | en_US |
| dspace.entity.type | Publication |