{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T10:31:39Z","timestamp":1785407499388,"version":"3.56.0"},"reference-count":82,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T00:00:00Z","timestamp":1775606400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002341","name":"Academy of Finland","doi-asserted-by":"crossref","award":["350696 The Harvest project"],"award-info":[{"award-number":["350696 The Harvest project"]}],"id":[{"id":"10.13039\/501100002341","id-type":"DOI","asserted-by":"crossref"}]},{"award":["350696 The Harvest project"],"award-info":[{"award-number":["350696 The Harvest project"]}],"id":[{"id":"https:\/\/ror.org\/05k73zm37","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computation"],"abstract":"<jats:p>Nowadays, solar energy is becoming one of the most popular sources of renewable energy worldwide. Traditional fossil fuels cause pollution and climate change, while solar power offers a clean and sustainable alternative. However, effective planning requires accurate prediction of the amount of solar energy that can be produced. Prediction accuracy directly depends on two factors: the model\u2019s hyperparameters and the feature set. In this study, we use boosting models, such as LightGBM, XGBoost, and CatBoost, to forecast solar power production. The prediction horizon is 60 min, which corresponds to short-term forecasting. Model tuning is performed using the NSGA-II multi-objective optimization algorithm. In this study, NSGA-II simultaneously tunes hyperparameters and a feature set of boosting models. We aim to enhance the performance of the NSGA-II algorithm in the early stages using the proposed method to generate the initial population. The initialization is based on an ensemble of filtering methods. The proposed approach promotes faster convergence in the early stages of the algorithm compared to the traditional initialization method. The results of numerical experiments are proven by the Wilcoxon test.<\/jats:p>","DOI":"10.3390\/computation14040089","type":"journal-article","created":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T09:49:44Z","timestamp":1775641784000},"page":"89","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Feature-Based Population Initialization for Evolutionary Optimization of Machine Learning Models in Short-Term Solar Power Forecasting"],"prefix":"10.3390","volume":"14","author":[{"given":"Aleksei","family":"Vakhnin","sequence":"first","affiliation":[{"name":"Department of Environmental and Biological Sciences, University of Eastern Finland, Yliopistonranta 1E, 70210 Kuopio, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Harri","family":"Niska","sequence":"additional","affiliation":[{"name":"Department of Environmental and Biological Sciences, University of Eastern Finland, Yliopistonranta 1E, 70210 Kuopio, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9305-0864","authenticated-orcid":false,"given":"Anders V.","family":"Lindfors","sequence":"additional","affiliation":[{"name":"Finnish Meteorological Institute, Meteorological Research, 00560 Helsinki, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9532-2285","authenticated-orcid":false,"given":"Mikko","family":"Kolehmainen","sequence":"additional","affiliation":[{"name":"Department of Environmental and Biological Sciences, University of Eastern Finland, Yliopistonranta 1E, 70210 Kuopio, Finland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,4,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"125104","DOI":"10.1016\/j.renene.2025.125104","article-title":"Getting Brighter: Impacts of Improved Day-Ahead Solar Forecasts in High-Solar, High-Storage Electricity Systems","volume":"259","author":"Kahrl","year":"2025","journal-title":"Renew. Energy"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"40820","DOI":"10.1109\/ACCESS.2023.3270041","article-title":"Machine learning based solar photovoltaic power forecasting: A review and comparison","volume":"11","author":"Gaboitaolelwe","year":"2023","journal-title":"IEEE Access"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Tsai, W.-C., Tu, C.-S., Hong, C.-M., and Lin, W.-M. (2023). A Review of State-of-the-Art and Short-Term Forecasting Models for Solar PV Power Generation. Energies, 16.","DOI":"10.20944\/preprints202305.1534.v1"},{"key":"ref_4","unstructured":"IRENA (2026, January 07). Renewable Capacity Statistics; Processed by Our World in Data. Total Solar Capacity. Available online: https:\/\/ourworldindata.org\/grapher\/installed-solar-pv-capacity."},{"key":"ref_5","first-page":"629","article-title":"An overview of solar power (PV systems) integration into electricity grids","volume":"2","author":"Nwaigwe","year":"2019","journal-title":"Mater. Sci. Energy Technol."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"102740","DOI":"10.1016\/j.asej.2024.102740","article-title":"Advancing solar energy integration: Unveiling XAI insights for enhanced power system management and sustainable future","volume":"15","author":"Nallakaruppan","year":"2024","journal-title":"Ain Shams Eng. J."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1016\/j.solener.2016.06.073","article-title":"On recent advances in PV output power forecast","volume":"136","author":"Raza","year":"2016","journal-title":"Sol. Energy"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Etxegarai, G., Zapirain, I., Camblong, H., Ugartemendia, J., Hernandez, J., and Curea, O. (2022). Photovoltaic Energy Production Forecasting in a Short Term Horizon: Comparison between Analytical and Machine Learning Models. Appl. Sci., 12.","DOI":"10.3390\/app122312171"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"912","DOI":"10.1016\/j.rser.2017.08.017","article-title":"Forecasting of photovoltaic power generation and model optimization: A review","volume":"81","author":"Das","year":"2018","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"719","DOI":"10.1007\/s42835-023-01378-2","article-title":"A comprehensive review on ensemble solar power forecasting algorithms","volume":"18","author":"Rahimi","year":"2023","journal-title":"J. Electr. Eng. Technol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1010","DOI":"10.1016\/j.renene.2023.01.118","article-title":"Accurate one-step and multistep forecasting of very short-term PV power using LSTM-TCN model","volume":"205","author":"Limouni","year":"2023","journal-title":"Renew. Energy"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"142","DOI":"10.1016\/j.renene.2017.01.019","article-title":"Very short term load forecasting of a distribution system with high PV penetration","volume":"106","author":"Sepasi","year":"2017","journal-title":"Renew. Energy"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1049\/rpg2.12209","article-title":"A novel hybrid ensemble LSTM-FFNN forecasting model for very short-term and short-term PV generation forecasting","volume":"16","author":"Kothona","year":"2022","journal-title":"IET Renew. Power Gener."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.renene.2016.01.039","article-title":"Day-ahead forecasting of solar power output from photovoltaic plants in the American Southwest","volume":"91","author":"Larson","year":"2016","journal-title":"Renew. Energy"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"112766","DOI":"10.1016\/j.enconman.2020.112766","article-title":"A day-ahead PV power forecasting method based on LSTM-RNN model and time correlation modification under partial daily pattern prediction framework","volume":"212","author":"Wang","year":"2020","journal-title":"Energy Convers. Manag."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Nespoli, A., Ogliari, E., Leva, S., Massi Pavan, A., Mellit, A., Lughi, V., and Dolara, A. (2019). Day-ahead photovoltaic forecasting: A comparison of the most effective techniques. Energies, 12.","DOI":"10.3390\/en12091621"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2300","DOI":"10.1016\/j.renene.2021.08.038","article-title":"Day-ahead to week-ahead solar irradiance prediction using convolutional long short-term memory networks","volume":"179","author":"Cheng","year":"2021","journal-title":"Renew. Energy"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"834","DOI":"10.1016\/j.matpr.2021.10.223","article-title":"Design and optimization of photovoltaic system with a week ahead power forecast using autoregressive artificial neural networks","volume":"52","author":"Mughal","year":"2022","journal-title":"Mater. Today Proc."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"165207","DOI":"10.1016\/j.ijleo.2020.165207","article-title":"One month-ahead forecasting of mean daily global solar radiation using time series models","volume":"219","author":"Belmahdi","year":"2020","journal-title":"Optik"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"e28898","DOI":"10.1016\/j.heliyon.2024.e28898","article-title":"Machine learning forecasting of solar PV production using single and hybrid models over different time horizons","volume":"10","author":"Asiedu","year":"2024","journal-title":"Heliyon"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"135744","DOI":"10.1016\/j.energy.2025.135744","article-title":"Assessing solar-to-PV power conversion models: Physical, ML, and hybrid approaches across diverse scales","volume":"323","author":"Li","year":"2025","journal-title":"Energy"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.solener.2016.06.069","article-title":"Review of photovoltaic power forecasting","volume":"136","author":"Antonanzas","year":"2016","journal-title":"Sol. Energy"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"127807","DOI":"10.1016\/j.energy.2023.127807","article-title":"Machine learning for forecasting a photovoltaic (PV) generation system","volume":"278","author":"Scott","year":"2023","journal-title":"Energy"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Di Leo, P., Ciocia, A., Malgaroli, G., and Spertino, F. (2025). Advancements and Challenges in Photovoltaic Power Forecasting: A Comprehensive Review. Energies, 18.","DOI":"10.20944\/preprints202502.2234.v1"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1016\/j.pecs.2013.06.002","article-title":"Solar forecasting methods for renewable energy integration","volume":"39","author":"Inman","year":"2013","journal-title":"Prog. Energy Combust. Sci."},{"key":"ref_26","unstructured":"Gueymard, C. (2004). High performance model for clear-sky irradiance and illuminance. Proceedings of the 2004 Solar Conference, Portland, OR, USA, 11\u201314 July 2004, American Solar Energy Society (ASES)."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"332","DOI":"10.1016\/j.solener.2016.03.017","article-title":"Validation of models that estimate the clear sky global and beam solar irradiance","volume":"132","author":"Ineichen","year":"2016","journal-title":"Sol. Energy"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"272","DOI":"10.1016\/j.solener.2007.04.008","article-title":"REST2: High-performance solar radiation model for cloudless-sky irradiance, illuminance, and photosynthetically active radiation\u2013Validation with a benchmark dataset","volume":"82","author":"Gueymard","year":"2008","journal-title":"Sol. Energy"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Abuella, M., and Chowdhury, B. (2015). Solar power probabilistic forecasting by using multiple linear regression analysis. Proceedings of the SoutheastCon 2015, Fort Lauderdale, FL, USA, 9\u201312 April 2015, IEEE.","DOI":"10.1109\/SECON.2015.7132869"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Souza, G., Santos, R., and Saraiva, E. (2022). A log-logistic predictor for power generation in photovoltaic systems. Energies, 15.","DOI":"10.3390\/en15165973"},{"key":"ref_31","first-page":"0114","article-title":"Forecasting of total daily solar energy generation using ARIMA: A case study","volume":"Volume 2019","author":"Atique","year":"2019","journal-title":"Proceedings of the 2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, NV, USA, 7\u20139 January 2019"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Kudelas, D., Tau\u0161ov\u00e1, M., Tau\u0161, P., Gab\u00e1niov\u00e1, \u013d., and Ko\u0161\u010do, J. (2019). Investigation of operating parameters and degradation of photovoltaic panels in a photovoltaic power plant. Energies, 12.","DOI":"10.3390\/en12193631"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1004","DOI":"10.1016\/j.egyr.2023.07.042","article-title":"Forecasting solar energy production: A comparative study of machine learning algorithms","volume":"10","author":"Ledmaoui","year":"2023","journal-title":"Energy Rep."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Benti, N.E., Chaka, M.D., and Semie, A.G. (2023). Forecasting renewable energy generation with machine learning and deep learning: Current advances and future prospects. Sustainability, 15.","DOI":"10.20944\/preprints202303.0451.v1"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"e26088","DOI":"10.1016\/j.heliyon.2024.e26088","article-title":"Renewable energy sources integration via machine learning modelling: A systematic literature review","volume":"10","author":"Alazemi","year":"2024","journal-title":"Heliyon"},{"key":"ref_36","unstructured":"Ogundepo, E., and Fokoue, E. (2019). A Comprehensive Empirical Demonstration of the No Free Lunch Theorem in Statistical Machine Learning. [Master\u2019s Thesis, African Institute for Mathematical Sciences (AIMS)]."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Adam, S.P., Alexandropoulos, S.A.N., Pardalos, P.M., and Vrahatis, M.N. (2019). No Free Lunch Theorem: A Review. Approximation and Optimization, Springer.","DOI":"10.1007\/978-3-030-12767-1_5"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"112772","DOI":"10.1016\/j.rser.2022.112772","article-title":"Benefits of physical and machine learning hybridization for photovoltaic power forecasting","volume":"168","author":"Mayer","year":"2022","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Niccolai, A., Dolara, A., and Ogliari, E. (2021). Hybrid PV power forecasting methods: A comparison of different approaches. Energies, 14.","DOI":"10.3390\/en14020451"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"175871","DOI":"10.1109\/ACCESS.2020.3025860","article-title":"Photovoltaic Power Forecasting with a Hybrid Deep Learning Approach","volume":"8","author":"Li","year":"2020","journal-title":"IEEE Access"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"569249","DOI":"10.1155\/2014\/569249","article-title":"A novel hybrid model for short-term forecasting in PV power generation","volume":"2014","author":"Wu","year":"2014","journal-title":"Int. J. Photoenergy"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"4557","DOI":"10.1049\/iet-gtd.2018.5847","article-title":"Hybrid method for short-term photovoltaic power forecasting based on deep convolutional neural network","volume":"12","author":"Zang","year":"2018","journal-title":"IET Gener. Transm. Distrib."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Ali, Y.A., Awwad, E.M., Al-Razgan, M., and Maarouf, A. (2023). Hyperparameter Search for Machine Learning Algorithms for Optimizing the Computational Complexity. Processes, 11.","DOI":"10.3390\/pr11020349"},{"key":"ref_44","first-page":"281","article-title":"Random search for hyper-parameter optimization","volume":"13","author":"Bergstra","year":"2012","journal-title":"J. Mach. Learn. Res."},{"key":"ref_45","first-page":"1551","article-title":"Hyperparameter optimization: Comparing genetic algorithm against grid search and Bayesian optimization","volume":"Volume 2021","author":"Alibrahim","year":"2021","journal-title":"Proceedings of the 2021 IEEE Congress on Evolutionary Computation (CEC), Krak\u00f3w, Poland, 28 June\u20141 July 2021"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1140\/epjc\/s10052-021-08950-y","article-title":"Evolutionary algorithms for hyperparameter optimization in machine learning for application in high energy physics","volume":"81","author":"Tani","year":"2021","journal-title":"Eur. Phys. J. C"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"S\u00e1nchez-Maro\u00f1o, N., Alonso-Betanzos, A., and Tombilla-Sanrom\u00e1n, M. (2007). Filter methods for feature selection\u2013A comparative study. Proceedings of the Intelligent Data Engineering and Automated Learning\u2014IDEAL 2007, Birmingham, UK, 16\u201319 December 2007, Springer.","DOI":"10.1007\/978-3-540-77226-2_19"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"101340","DOI":"10.1016\/j.elerap.2023.101340","article-title":"Feature selection for Turkish crowdfunding projects using filtering and wrapping methods","volume":"62","author":"Kilinc","year":"2023","journal-title":"Electron. Commer. Res. Appl."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"5605","DOI":"10.1007\/s11831-022-09778-9","article-title":"A comprehensive review on multi-objective optimization techniques: Past, present and future","volume":"29","author":"Sharma","year":"2022","journal-title":"Arch. Comput. Methods Eng."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"366","DOI":"10.1007\/BF01584098","article-title":"Linear programming with multiple objective functions: Step method (STEM)","volume":"1","author":"Benayoun","year":"1971","journal-title":"Math. Program."},{"key":"ref_51","unstructured":"Schaffer, J.D. (1985). Multiple objective optimization with vector evaluated genetic algorithms. Proceedings of the 1st International Conference on Genetic Algorithms, Lawrence Erlbaum Associates Inc."},{"key":"ref_52","first-page":"416","article-title":"Genetic Algorithms for Multiobjective Optimization: Formulation, Discussion and Generalization","volume":"Volume 93","author":"Fonseca","year":"1993","journal-title":"Proceedings of the 5th International Conference on Genetic Algorithms"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1162\/evco.1994.2.3.221","article-title":"Multiobjective function optimization using non-dominated sorting genetic algorithms","volume":"2","author":"Srinivas","year":"1995","journal-title":"Evol. Comput."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1109\/4235.797969","article-title":"Multiobjective evolutionary algorithms: A comparative case study and the strength Pareto approach","volume":"3","author":"Zitzler","year":"1999","journal-title":"IEEE Trans. Evol. Comput."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"57757","DOI":"10.1109\/ACCESS.2021.3070634","article-title":"A comprehensive review on NSGA-II for multi-objective combinatorial optimization problems","volume":"9","author":"Verma","year":"2021","journal-title":"IEEE Access"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"2285","DOI":"10.1007\/s11831-021-09663-x","article-title":"A review of multi-objective optimization: Methods and algorithms in mechanical engineering problems","volume":"29","author":"Pereira","year":"2022","journal-title":"Arch. Comput. Methods Eng."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Kazimipour, B., Li, X., and Qin, A.K. (2014). A review of population initialization techniques for evolutionary algorithms. Proceedings of the IEEE Congress on Evolutionary Computation (CEC), Beijing, China, 6\u201311 July 2014, IEEE.","DOI":"10.1109\/CEC.2014.6900618"},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Agushaka, J.O., and Ezugwu, A.E. (2022). Initialisation approaches for population-based metaheuristic algorithms: A comprehensive review. Appl. Sci., 12.","DOI":"10.3390\/app12020896"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"3361","DOI":"10.1111\/itor.13237","article-title":"Initialization of metaheuristics: Comprehensive review, critical analysis, and research directions","volume":"30","author":"Sarhani","year":"2023","journal-title":"Int. Trans. Oper. Res."},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Huang, D., Liu, Z., and Wu, D. (2023). Research on ensemble learning-based feature selection method for time-series prediction. Appl. Sci., 14.","DOI":"10.3390\/app14010040"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Mera-Gaona, M., L\u00f3pez, D.M., Vargas-Canas, R., and Neumann, U. (2021). Framework for the ensemble of feature selection methods. Appl. Sci., 11.","DOI":"10.3390\/app11178122"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Seijo-Pardo, B., Bol\u00f3n-Canedo, V., Porto-D\u00edaz, I., and Alonso-Betanzos, A. (2015). Ensemble feature selection for rankings of features. Advances in Computational Intelligence, Proceedings of the 13th International Work-Conference on Artificial Neuarl Networks, IWANN 2015, Palma de Mallorca, Spain, 10\u201312 June 2015, Springer.","DOI":"10.1007\/978-3-319-19222-2_3"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Solano, E.S., Dehghanian, P., and Affonso, C.M. (2022). Solar radiation forecasting using machine learning and ensemble feature selection. Energies, 15.","DOI":"10.3390\/en15197049"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"6907","DOI":"10.1038\/s41598-025-91282-8","article-title":"An adaptive ensemble feature selection technique for model-agnostic diabetes prediction","volume":"15","author":"Natarajan","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3653025","article-title":"A population initialization method based on similarity and mutual information in evolutionary algorithm for bi-objective feature selection","volume":"4","author":"Cai","year":"2024","journal-title":"ACM Trans. Evol. Learn."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"1","DOI":"10.32604\/jbd.2021.010364","article-title":"A new population initialization of particle swarm optimization method based on pca for feature selection","volume":"3","author":"Wang","year":"2021","journal-title":"J. Big Data"},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1007\/s11063-024-11553-9","article-title":"A feature selection method based on feature-label correlation information and self-adaptive MOPSO","volume":"56","author":"Han","year":"2024","journal-title":"Neural Process. Lett."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"1047","DOI":"10.1016\/j.egyr.2021.09.167","article-title":"Photovoltaic power prediction of LSTM model based on Pearson feature selection","volume":"7","author":"Chen","year":"2021","journal-title":"Energy Rep."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Shi, M., Yin, R., Zhang, P., Tian, J., and Wang, Y. (2020). Ultra short-term forecast of photovoltaic generation based on time-division LSTM neural networks. Proceedings of the 2020 12th IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC), Nanjing, China, 20\u201323 September 2020, IEEE.","DOI":"10.1109\/APPEEC48164.2020.9220460"},{"key":"ref_70","first-page":"36","article-title":"A comparison of the Pearson, Spearman rank and Kendall tau correlation coefficients using quantitative variables","volume":"20","author":"Essam","year":"2022","journal-title":"Asian J. Probab. Stat."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Sulaiman, M.A., and Labadin, J. (2015). Feature selection based on mutual information. Proceedings of the 2015 9th International Conference on IT in Asia (CITA), Sarawak, Malaysia, 4\u20135 August 2015, IEEE.","DOI":"10.1109\/CITA.2015.7349827"},{"key":"ref_72","first-page":"2769","article-title":"Measuring and testing dependence by correlation of distances","volume":"35","author":"Rizzo","year":"2007","journal-title":"Ann. Stat."},{"key":"ref_73","doi-asserted-by":"crossref","unstructured":"Gretton, A., Bousquet, O., Smola, A., and Sch\u00f6lkopf, B. (2005). Measuring statistical dependence with Hilbert\u2013Schmidt norms. Proceedings of the Algorithmic Learning Theory, 16th International Conference, ALT 2005, Singapore, 8\u201311 October 2005, Springer.","DOI":"10.1007\/11564089_7"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1016\/j.renene.2023.02.130","article-title":"Evaluating neural network models in site-specific solar PV forecasting using numerical weather prediction data and weather observations","volume":"207","author":"Brester","year":"2023","journal-title":"Renew. Energy"},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"89497","DOI":"10.1109\/ACCESS.2020.2990567","article-title":"Pymoo: Multi-objective optimization in Python","volume":"8","author":"Blank","year":"2020","journal-title":"IEEE Access"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"1087","DOI":"10.1016\/j.egyr.2022.02.251","article-title":"Probabilistic solar irradiance forecasting based on XGBoost","volume":"8","author":"Li","year":"2022","journal-title":"Energy Rep."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"207","DOI":"10.1016\/j.ref.2023.06.009","article-title":"Improving solar PV prediction performance with RF\u2013CatBoost ensemble","volume":"46","author":"Banik","year":"2023","journal-title":"Renew. Energy Focus"},{"key":"ref_78","doi-asserted-by":"crossref","unstructured":"Peng, Y., Wang, S., Chen, W., Ma, J., Wang, C., and Chen, J. (2023). LightGBM-integrated PV power prediction based on multi-resolution similarity. Processes, 11.","DOI":"10.3390\/pr11041141"},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Chen, T. (2016). XGBoost: A scalable tree boosting system. arXiv.","DOI":"10.1145\/2939672.2939785"},{"key":"ref_80","unstructured":"Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A.V., and Gulin, A. (2018). CatBoost: Unbiased boosting with categorical features. Proceedings of the 32nd international Conference on Neural Information Processing Systems, Montreal, QC, Canada, 3\u20138 December 2018, Curran Associates Inc."},{"key":"ref_81","unstructured":"Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., and Liu, T.Y. (2017). LightGBM: A highly efficient gradient boosting decision tree. Proceedings of the 31st Conference on Neural Information Processing Systems (NIPS 2017), Long Beach, CA, USA, 4\u20139 December 2017, Curran Associates Inc."},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Akiba, T., Sano, S., Yanase, T., Ohta, T., and Koyama, M. (2019). Optuna: A next-generation hyperparameter optimization framework. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Anchorage, AK, USA, 4\u20138 August 2019, Association for Computing Machinery.","DOI":"10.1145\/3292500.3330701"}],"container-title":["Computation"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-3197\/14\/4\/89\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T04:24:26Z","timestamp":1775795066000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-3197\/14\/4\/89"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,8]]},"references-count":82,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2026,4]]}},"alternative-id":["computation14040089"],"URL":"https:\/\/doi.org\/10.3390\/computation14040089","relation":{},"ISSN":["2079-3197"],"issn-type":[{"value":"2079-3197","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,8]]}}}