Abstract
Finding an alternative renewable energy source instead of using traditional energy such as electricity or gas is an important research trend and challenge. This paper presents a new hybrid algorithm that uses Krill Herd (KH) optimization algorithm and Adaptive Neuro-Fuzzy Inference System (ANFIS) to be able to fit for wind speed forecasting, which is an essential step to generate wind power. ANFIS’s parameters are optimized using KH. The proposed model called (Krill-ANFIS). This model is compared with three models basic ANFIS, PSO-ANFIS, and GA-ANFIS. Krill-ANFIS proved that it can be used as an efficient predictor for the wind speed as well as it can achieve high results and performance measures of root mean square error (RMSE), Coefficient of determination \(R^{2}\) and average absolute percent relative error (AAPRE).
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Ahmed, K. et al. (2017). A Hybrid Krill-ANFIS Model for Wind Speed Forecasting. In: Hassanien, A., Shaalan, K., Gaber, T., Azar, A., Tolba, M. (eds) Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2016. AISI 2016. Advances in Intelligent Systems and Computing, vol 533. Springer, Cham. https://doi.org/10.1007/978-3-319-48308-5_35
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DOI: https://doi.org/10.1007/978-3-319-48308-5_35
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