TY - GEN
T1 - Enhanced Prediction of Solar Irradiance Using a Hybrid Approach Based on the Crow Search Algorithm and Extreme Learning Machine Network
AU - Madhiarasan, Manoharan
AU - Belmahdi, Brahim
AU - Louzazni, Mohamed
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024
Y1 - 2024
N2 - Solar energy has a higher degree of volatility due to climatic constraints and scenarios. The efficient and successful deployment of solar energy necessitates an accurate and robust prediction method for predicting solar irradiance (GHI: Global Horizontal Irradiance). Inappropriate selection of Extreme Learning Machine network (ELMN) parameters creates the generalization issue, computational burden and unnecessary complexity. To address the issue of optimizing ELMN parameters. This research work addresses the issue with the development of a hybrid prediction approach (CSA-ELMN) combination of the Crow Search Algorithm (CSA) and Extreme Learning Machine Network (ELMN). The novel aspect of this investigation is using a crow search algorithm during the extreme learning machine training phase to optimize synaptic connection weights, bias and hidden layer neurons, which have been successfully evaluated in the Solar Irradiance (GHI) predictions application. Four statistical indices, including the mean square error (MSE), mean absolute percentage error (MAPE), root mean square error (RMSE), and mean relative error (MRE), were computed to assess the proposed hybrid prediction model (CSA-ELMN). The findings of the CSA-ELM approach shows that it improves GHI prediction precision compared to other traditional and hybrid approaches.
AB - Solar energy has a higher degree of volatility due to climatic constraints and scenarios. The efficient and successful deployment of solar energy necessitates an accurate and robust prediction method for predicting solar irradiance (GHI: Global Horizontal Irradiance). Inappropriate selection of Extreme Learning Machine network (ELMN) parameters creates the generalization issue, computational burden and unnecessary complexity. To address the issue of optimizing ELMN parameters. This research work addresses the issue with the development of a hybrid prediction approach (CSA-ELMN) combination of the Crow Search Algorithm (CSA) and Extreme Learning Machine Network (ELMN). The novel aspect of this investigation is using a crow search algorithm during the extreme learning machine training phase to optimize synaptic connection weights, bias and hidden layer neurons, which have been successfully evaluated in the Solar Irradiance (GHI) predictions application. Four statistical indices, including the mean square error (MSE), mean absolute percentage error (MAPE), root mean square error (RMSE), and mean relative error (MRE), were computed to assess the proposed hybrid prediction model (CSA-ELMN). The findings of the CSA-ELM approach shows that it improves GHI prediction precision compared to other traditional and hybrid approaches.
KW - Crow Search Algorithm
KW - Extreme Learning Machine network
KW - Hidden Neuron
KW - Hybrid Approach
KW - Optimization
KW - Prediction
KW - Solar Energy
KW - Synaptic Weight
UR - http://www.scopus.com/inward/record.url?scp=85190440475&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-54674-7_4
DO - 10.1007/978-3-031-54674-7_4
M3 - Article in proceedings
AN - SCOPUS:85190440475
SN - 9783031546730
T3 - Lecture Notes in Networks and Systems
SP - 60
EP - 78
BT - The 17th International Conference Interdisciplinarity in Engineering - Inter-Eng 2023 Conference Proceedings - Volume 3
A2 - Moldovan, Liviu
A2 - Gligor, Adrian
PB - Springer Science and Business Media Deutschland GmbH
T2 - 17th International Conference on Interdisciplinarity in Engineering, INTER-ENG 2023
Y2 - 5 October 2023 through 6 October 2023
ER -