TY - GEN
T1 - A Particle Swarm Optimization-Long-Short Term Memory (PSO-LSTM) Hybrid Model for Forecasting Global Horizontal Solar Radiation
AU - Abisoye, Blessing Olatunde
AU - Sun, Yanxia
AU - Zenghui, Wang
N1 - Publisher Copyright:
© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2025.
PY - 2025
Y1 - 2025
N2 - Electrical energy demands require clean and sustainable energy production. Solar power output as a clean energy depends on solar radiation and other meteorological features. The variability of solar radiation and other meteorological features impaired solar energy output, significantly affecting the economics of intelligent grids and microgrid operation reliability. This study proposed a hybrid system that comprises a particle swarm optimization (PSO) algorithm and Long-Short Term Memory (LSTM). The PSO algorithm was employed to conduct the adaptive parameter adjustment of the LSTM models. The study benchmarked the proposed hybrid PSO-LSTM model with LSTM-based Genetic Algorithm (GA), Grey Wolf Optimization (GWO), and Ant Colony Optimization (ACO) models. Four performance evaluation metrics were utilized to compare forecasting accuracy models: R-Square, RMSE, MAE, and MAPE. Comparing the developed model, PSO-LSTM, R2 (0.950643), RMSE (60.304873), and MAE (25.442901) with the benchmarked model, it was discovered that the PSO-LSTM is superior. Its full implementation will assist in integrating solar systems into the national grids and enable the stakeholders and policymakers to make effective decisions.
AB - Electrical energy demands require clean and sustainable energy production. Solar power output as a clean energy depends on solar radiation and other meteorological features. The variability of solar radiation and other meteorological features impaired solar energy output, significantly affecting the economics of intelligent grids and microgrid operation reliability. This study proposed a hybrid system that comprises a particle swarm optimization (PSO) algorithm and Long-Short Term Memory (LSTM). The PSO algorithm was employed to conduct the adaptive parameter adjustment of the LSTM models. The study benchmarked the proposed hybrid PSO-LSTM model with LSTM-based Genetic Algorithm (GA), Grey Wolf Optimization (GWO), and Ant Colony Optimization (ACO) models. Four performance evaluation metrics were utilized to compare forecasting accuracy models: R-Square, RMSE, MAE, and MAPE. Comparing the developed model, PSO-LSTM, R2 (0.950643), RMSE (60.304873), and MAE (25.442901) with the benchmarked model, it was discovered that the PSO-LSTM is superior. Its full implementation will assist in integrating solar systems into the national grids and enable the stakeholders and policymakers to make effective decisions.
KW - Global Horizontal Radiation
KW - LSTM
KW - Metaheuristic Algorithms
UR - https://www.scopus.com/pages/publications/105013190389
U2 - 10.1007/978-3-031-94439-0_17
DO - 10.1007/978-3-031-94439-0_17
M3 - Conference contribution
AN - SCOPUS:105013190389
SN - 9783031944383
T3 - Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
SP - 291
EP - 308
BT - Pan-African Artificial Intelligence and Smart Systems - 3rd Pan-African Conference, PAAISS 2024, Proceedings
A2 - Ngatched, Telex M. N.
A2 - Woungang, Isaac
A2 - Tapamo, Jules-Raymond
A2 - Viriri, Serestina
PB - Springer Science and Business Media Deutschland GmbH
T2 - 3rd Pan-African Conference on Artificial Intelligence and Smart Systems Conference, PAAISS 2024
Y2 - 4 December 2024 through 6 December 2024
ER -
