Accurate forecasting of renewable energy is crucial for grid stability, efficient scheduling, and reliable power system operation. However, photovoltaic (PV) and wind systems are highly nonlinear and intermittent due to changing environmental conditions, making prediction challenging. This study proposes an optimization-driven framework for PV parameter estimation and wind power curve modeling using real-world datasets, including data from the 226.8 MW Anantapur Wind Farm and experimental PV measurements. It highlights that the effectiveness of metaheuristic algorithms is problem-specific, with performance varying based on model and data characteristics. A comparative analysis using metrics such as RMSE, convergence behavior, and reliability shows that Leader Harris Hawks Optimization performs best for PV parameter estimation, while Harris Hawks Optimization is superior for wind modeling. The framework improves prediction accuracy and provides a scalable solution for integrating renewable energy into modern power systems.
AhmadianfarIHeidariAANoshadianS, et al. (2022) INFO: an efficient optimization algorithm based on weighted mean of vectors. Expert Systems with Applications195: 116516. https://doi.org/10.1016/j.eswa.2022.116516
2.
AhmedRSreeramVMishraY, et al. (2020) A review and evaluation of the state-of-the-art in PV solar power forecasting: techniques and optimization. Renewable and Sustainable Energy Reviews124: 109792. https://doi.org/10.1016/j.rser.2020.109792
3.
AmiriMHMehrabi HashjinNMontazeriM, et al. (2024) Hippopotamus optimization algorithm: a novel nature-inspired optimization algorithm. Scientific Reports14: 5032. https://doi.org/10.1038/s41598-024-54910-3
AslamSHerodotouHMohsinSM, et al. (2021) A survey on deep learning methods for power load and renewable energy forecasting in smart microgrids. Renewable and Sustainable Energy Reviews144: 110992. https://doi.org/10.1016/j.rser.2021.110992
6.
CalasanMAbdel AleemSHEZobaaAF (2020) On the root mean square error (RMSE) calculation for parameter estimation of photovoltaic models: a novel exact analytical solution based on Lambert W function. Energy Conversion and Management210: 112716. https://doi.org/10.1016/j.enconman.2020.112716
7.
FoleyAMLeahyPGMarvugliaA, et al. (2012) Current methods and advances in forecasting of wind power generation. Renewable Energy37(1): 1–8. https://doi.org/10.1016/j.renene.2011.05.033
8.
Gallego-CastilloCCuerva-TejeroALópez-GarcíaO (2015) A review on the recent history of wind power ramp forecasting. Renewable and Sustainable Energy Reviews52: 1148–1157. https://doi.org/10.1016/j.rser.2015.07.154
9.
HeidariAAMirjaliliSFarisH, et al. (2019) Harris hawks optimization: algorithm and applications. Future Generation Computer Systems97: 849–872. https://doi.org/10.1016/j.future.2019.02.028
10.
HillDCMcMillanDBellKRW, et al. (2012) Application of auto-regressive models to U.K. wind speed data for power system impact studies. IEEE Transactions on Sustainable Energy3(1): 134–141. https://doi.org/10.1109/tste.2011.2163324
11.
HollandJH (1992) Adaptation in natural and artificial systems: an introductory analysis with applications to biology. In: Control, and Artificial Intelligence. MIT Press.
12.
InmanRHPedroHTCCoimbraCFM (2013) Solar forecasting methods for renewable energy integration. Progress in Energy and Combustion Science39(6): 535–576. https://doi.org/10.1016/j.pecs.2013.06.002
13.
JordehiAR (2016) Parameter estimation of solar photovoltaic (PV) cells: a review. Renewable and Sustainable Energy Reviews61: 354–371. https://doi.org/10.1016/j.rser.2016.03.049
14.
JungJBroadwaterRP (2014) Current status and future advances for wind speed and power forecasting. Renewable and Sustainable Energy Reviews31: 762–777. https://doi.org/10.1016/j.rser.2013.12.054
15.
KennedyJEberhartR (1995) Particle swarm optimization. In: Proceedings of the IEEE International Conference on Neural Networks, Vol. 4, pp. 1942–1948.
16.
LiSGongWGuQ (2021) A comprehensive survey on meta-heuristic algorithms for parameter extraction of photovoltaic models. Renewable and Sustainable Energy Reviews141: 110828. https://doi.org/10.1016/j.rser.2021.110828
17.
LydiaMKumarSSSelvakumarAI, et al. (2014) A comprehensive review on wind turbine power curve modeling techniques. Renewable and Sustainable Energy Reviews30: 452–460. https://doi.org/10.1016/j.rser.2013.10.030
18.
LydiaMSelvakumarAIKumarSS, et al. (2015) Wind resource estimation using wind speed and power curve models. Renewable Energy83: 425–434. https://doi.org/10.1016/j.renene.2015.04.045
19.
ManwellJFMcGowanJGRogersAL (2009) Wind Energy Explained: Theory, Design and Application. John Wiley & Sons.
20.
MellitAKalogirouSA (2008) Artificial intelligence techniques for photovoltaic applications: a review. Progress in Energy and Combustion Science34(5): 574–632. https://doi.org/10.1016/j.pecs.2008.01.001
MohsinSMMaqsoodTMadaniSA (2022) Solar and wind energy forecasting for green and intelligent migration of traditional energy sources. Sustainability14(23): 16317. https://doi.org/10.3390/su142316317
24.
PedroHTCCoimbraCFM (2012) Assessment of forecasting techniques for solar power production with no exogenous inputs. Solar Energy86(7): 2017–2028. https://doi.org/10.1016/j.solener.2012.04.004
25.
PillaiDSRajasekarN (2018) Metaheuristic algorithms for PV parameter identification: a comprehensive review with an application to threshold setting for fault detection in PV systems. Renewable and Sustainable Energy Reviews82(3): 3503–3525. https://doi.org/10.1016/j.rser.2017.10.107
26.
RenYSuganthanPNSrikanthN (2015) Ensemble methods for wind and solar power forecasting—A state-of-the-art review. Renewable and Sustainable Energy Reviews50: 82–91. https://doi.org/10.1016/j.rser.2015.04.081
27.
SatyanarayanaGCNaiduCVRamakrishnaSSVS (2019) Wind energy assessment over the Andhra Pradesh and Telangana regions. Meteorological Applications26(4): 581–592.
28.
SohoniVGuptaSCNemaRK (2016) A critical review on wind turbine power curve modelling techniques and their applications in wind based energy systems. Journal of Energy2016: 1–18. https://doi.org/10.1155/2016/8519785
29.
SomanSSZareipourHMalikO, et al. (2010) A review of wind power and wind speed forecasting methods with different time Horizons. In: Proceedings of the 2010 North American Power Symposium (NAPS), pp. 1–8.
30.
TascikaraogluAUzunogluM (2014) A review of combined approaches for prediction of short-term wind speed and power. Renewable and Sustainable Energy Reviews34: 243–254. https://doi.org/10.1016/j.rser.2014.03.033
31.
VoyantCNottonGKalogirouS, et al. (2017) Machine learning methods for solar radiation forecasting: a review. Renewable Energy105: 569–582. https://doi.org/10.1016/j.renene.2016.12.095
32.
WangJSongYLiuF, et al. (2016) Analysis and application of forecasting models in wind power integration: a review of multi-step-ahead wind speed forecasting models. Renewable and Sustainable Energy Reviews60: 960–981. https://doi.org/10.1016/j.rser.2016.01.114
33.
WangHZhangNDuE, et al. (2022) A comprehensive review for wind, solar, and electrical load forecasting methods. Global Energy Interconnection5(1): 9–30. https://doi.org/10.1016/j.gloei.2022.04.002
34.
ZendehboudiABaseerMASaidurR (2018) Application of support vector machine models for forecasting solar and wind energy resources: a review. Journal of Cleaner Production199: 272–285. https://doi.org/10.1016/j.jclepro.2018.07.164
35.
ZhangYWangJWangX (2014) Review on probabilistic forecasting of wind power generation. Renewable and Sustainable Energy Reviews32: 255–270. https://doi.org/10.1016/j.rser.2014.01.033
