{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T03:12:09Z","timestamp":1772593929604,"version":"3.50.1"},"reference-count":131,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2025,3,17]],"date-time":"2025-03-17T00:00:00Z","timestamp":1742169600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"CS-OGET, the Faculty of Engineering, Eduardo Mondlane University","award":["Nr.5-09\/2029\/CS-OGET"],"award-info":[{"award-number":["Nr.5-09\/2029\/CS-OGET"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Energies"],"abstract":"<jats:p>Because of variations in the amount of solar energy that reaches the Earth\u2019s surface, the output of solar power plants can undergo significant variability in the electricity generated. To solve this conundrum, modeling the parametric forecast of short-scale solar energy across Mozambique\u2019s Mid-North region was the goal of this study. The parametric model applied consists of machine learning models based on the parametric analysis of all atmospheric, geographic, climatic, and spatiotemporal elements that impact the fluctuation in solar energy. It highlights the essential importance of the exact management of the interferential power density of each parameter influencing the availability of super solar energy. It enhances the long and short forecasts, estimates and scales, and geographic location, and provides greater precision, compared to other forecasting models. We selected eleven Mid-North region sites that collected data between 2019 and 2021 for the validation sample. The findings demonstrate a significant connection in the range of 0.899 to 0.999 between transmittances and irradiances caused by aerosols, water vapor, evenly mixed gases, and ozone. Uniformly mixed gases exhibit minimal attenuation, with a transmittance of about 0.985 in comparison to other atmospheric constituents. Despite the increased precision obtained by parameterization, the area still offers potential for solar application, with average values of 25% and 51% for clear skies and intermediate conditions, respectively. The estimated solar energy allows the model to be evaluated in any reality since it is within the theoretical irradiation spectrum under clear skies.<\/jats:p>","DOI":"10.3390\/en18061469","type":"journal-article","created":{"date-parts":[[2025,3,17]],"date-time":"2025-03-17T07:49:57Z","timestamp":1742197797000},"page":"1469","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Modeling Parametric Forecasts of Solar Energy over Time in the Mid-North Area of Mozambique"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9534-1160","authenticated-orcid":false,"given":"Fernando Ven\u00e2ncio","family":"Mucomole","sequence":"first","affiliation":[{"name":"CS-OGET\u2014Center of Excellence of Studies in Oil and Gas Engineering and Technology, Faculty of Engineering, Eduardo Mondlane University, Mozambique Avenue Km 1.5, Maputo 257, Mozambique"},{"name":"CPE\u2014Centre of Research in Energies, Faculty of Sciences, Eduardo Mondlane University, Main Campus No. 3453, Maputo 257, Mozambique"},{"name":"Department of Physics, Faculty of Sciences, Eduardo Mondlane University, Main Campus No. 3453, Maputo 257, Mozambique"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7532-3993","authenticated-orcid":false,"given":"Carlos Augusto Santos","family":"Silva","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Instituto Superior T\u00e9cnico, University of Lisbon, 1600-214 Lisbon, Portugal"}]},{"given":"Louren\u00e7o L\u00e1zaro","family":"Magaia","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Informatics, Faculty of Science, Eduardo Mondlane University, Main Campus No. 3453, Maputo 257, Mozambique"}]}],"member":"1968","published-online":{"date-parts":[[2025,3,17]]},"reference":[{"key":"ref_1","unstructured":"Iqbal, M. (1983). An Introduction to Solar Radiation, Academic Press."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"60","DOI":"10.54536\/ajenr.v3i1.2430","article-title":"A Systematic Review on the Accessibility of Spatial and Temporal Variability of Solar Energy Availability on a Short Scale Measurement","volume":"3","author":"Mucomole","year":"2024","journal-title":"Am. J. Energy Nat. Resour."},{"key":"ref_3","unstructured":"Duffie, J.A., and Beckman, W.A. (1980). Solar Engineering of Thermal Processes, Wiley."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.solener.2020.01.034","article-title":"Random forest regression for improved mapping of solar irradiance at high latitudes","volume":"198","author":"Babar","year":"2020","journal-title":"Sol. Energy"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1035","DOI":"10.1016\/j.egypro.2011.12.1051","article-title":"Historical Weather Data Supported Hybrid Renewable Energy Forecasting using Artificial Neural Network (ANN)","volume":"14","author":"Hossain","year":"2012","journal-title":"Energy Procedia"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1169","DOI":"10.1016\/j.rser.2016.06.001","article-title":"Models for forecasting growth trends in renewable energy","volume":"77","author":"Tsai","year":"2017","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1761","DOI":"10.1016\/j.energy.2006.11.010","article-title":"Predicting electricity energy consumption: A comparison of regression analysis, decision tree and neural networks","volume":"32","author":"Tso","year":"2007","journal-title":"Energy"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"27","DOI":"10.54536\/ajenr.v2i1.1311","article-title":"Temporal Variability of Solar Energy Availability in the Conditions of the Southern Region of Mozambique","volume":"2","author":"Mucomole","year":"2023","journal-title":"Am. J. Energy Nat. Resour."},{"key":"ref_9","first-page":"1","article-title":"Implicit hybrid block methods for solving second, third and fourth orders ordinary differential equations directly","volume":"48","author":"Abolarin","year":"2022","journal-title":"Ital. J. Pure Appl. Math."},{"key":"ref_10","unstructured":"IEA, IRENA, UNSD, World Bank, and WHO (2023, December 16). Tracking SDG 7: The Energy Progress Report. World Bank, Washington DC. \u00a9 World Bank, 2023. Available online: https:\/\/cdn.who.int\/media\/docs\/default-source\/air-pollution-documents\/air-quality-and-health\/sdg7-report2023-full-report_web.pdf?sfvrsn=669e8626_3&download=true."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"2424","DOI":"10.1016\/j.renene.2006.12.017","article-title":"A multiple correlation between different solar parameters in Medina, Saudi Arabia","volume":"32","author":"Benghanem","year":"2007","journal-title":"Renew. Energy"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"21","DOI":"10.54536\/ajenr.v3i1.2569","article-title":"Quantifying the Variability of Solar Energy Fluctuations at High\u2013Frequencies through Short-Scale Measurements in the East\u2013Channel of Mozambique Conditions","volume":"3","author":"Mucomole","year":"2024","journal-title":"Am. J. Energy Nat. Resour."},{"key":"ref_13","first-page":"100241","article-title":"Hyper-temporal variability analysis of solar insolation with respect to local seasons","volume":"15","author":"Kumar","year":"2019","journal-title":"Remote Sens. Appl. Soc. Environ."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"459","DOI":"10.1016\/S0038-092X(96)00122-3","article-title":"Estimation of convective mass transfer in solar distillation systems","volume":"57","author":"Kumar","year":"1996","journal-title":"Sol. Energy"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"539","DOI":"10.1016\/j.rser.2017.04.107","article-title":"Forecasting of solar energy with application for a growing economy like India: Survey and implication","volume":"78","author":"Mohanty","year":"2017","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_16","first-page":"100128","article-title":"Ambient temperature and solar irradiance forecasting prediction horizon sensitivity analysis","volume":"6","author":"Bosman","year":"2021","journal-title":"Mach. Learn. Appl."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Al-Ali, E.M., Hajji, Y., Said, Y., Hleili, M., Alanzi, A.M., Laatar, A.H., and Atri, M. (2023). Solar Energy Production Forecasting Based on a Hybrid CNN-LSTM-Transformer Model. Mathematics, 11.","DOI":"10.3390\/math11030676"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Kuo, P.-H., and Huang, C.-J. (2018). A Green Energy Application in Energy Management Systems by an Artificial Intelligence-Based Solar Radiation Forecasting Model. Energies, 11.","DOI":"10.3390\/en11040819"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/S0038-092X(97)00074-1","article-title":"Parametric model of solar cooker performance","volume":"62","author":"Funk","year":"1998","journal-title":"Sol. Energy"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1107","DOI":"10.1007\/s40745-020-00319-4","article-title":"Generalized Regression Neural Network Model Based Estimation of Global Solar Energy Using Meteorological Parameters","volume":"10","author":"Sridharan","year":"2023","journal-title":"Ann. Data Sci."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"13489","DOI":"10.1002\/er.6679","article-title":"A holistic review on energy forecasting using big data and deep learning models","volume":"45","author":"Devaraj","year":"2021","journal-title":"Int. J. Energy Res."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Jawaid, F., and NazirJunejo, K. (2016, January 24\u201326). Predicting daily mean solar power using machine learning regression techniques. Proceedings of the 2016 Sixth International Conference on Innovative Computing Technology (INTECH), Dublin, Ireland.","DOI":"10.1109\/INTECH.2016.7845051"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2837","DOI":"10.1016\/j.egyr.2023.09.097","article-title":"Predictive modeling of PV solar power plant efficiency considering weather conditions: A comparative analysis of artificial neural networks and multiple linear regression","volume":"10","author":"Sahin","year":"2023","journal-title":"Energy Rep."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2864","DOI":"10.1016\/j.rser.2012.01.064","article-title":"A review of solar energy modeling techniques","volume":"16","author":"Khatib","year":"2012","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"3179","DOI":"10.1029\/97JC02328","article-title":"Sensitivity of a wave model to wind variability","volume":"103","author":"Ponce","year":"1998","journal-title":"J. Geophys. Res. Ocean."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"120109","DOI":"10.1016\/j.energy.2021.120109","article-title":"Prediction of solar energy guided by pearson correlation using machine learning","volume":"224","author":"Jebli","year":"2021","journal-title":"Energy"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"810","DOI":"10.1016\/j.jclepro.2018.08.207","article-title":"Predictive modelling for solar thermal energy systems: A comparison of support vector regression, random forest, extra trees and regression trees","volume":"203","author":"Ahmad","year":"2018","journal-title":"J. Clean. Prod."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"607","DOI":"10.1016\/j.enpol.2014.05.053","article-title":"Using smart meter data to estimate demand response potential, with application to solar energy integration","volume":"73","author":"Dyson","year":"2014","journal-title":"Energy Policy"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"116891","DOI":"10.1016\/j.apenergy.2021.116891","article-title":"An adaptive short-term forecasting method for the energy yield of flat-plate solar collector systems","volume":"293","author":"Unterberger","year":"2021","journal-title":"Appl. Energy"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"100788","DOI":"10.1016\/j.esr.2021.100788","article-title":"Energy demand and production forecasting in Pakistan","volume":"39","author":"Raza","year":"2022","journal-title":"Energy Strategy Rev."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"119476","DOI":"10.1016\/j.jclepro.2019.119476","article-title":"Long short-term memory recurrent neural network for modeling temporal patterns in long-term power forecasting for solar PV facilities: Case study of South Korea","volume":"250","author":"Jung","year":"2020","journal-title":"J. Clean. Prod."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1274","DOI":"10.1049\/iet-rpg.2016.1043","article-title":"Weather forecasting error in solar energy forecasting","volume":"11","author":"Sangrody","year":"2017","journal-title":"IET Renew. Power Gener."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Mucomole, F.V., Silva, C.A.S., and Magaia, L.L. (2024). Regressive and Spatio-Temporal Accessibility of Variability in Solar Energy on a Short Scale Measurement in the Southern and Mid Region of Mozambique. Energies, 17.","DOI":"10.3390\/en17112613"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/j.solener.2014.10.016","article-title":"A suite of metrics for assessing the performance of solar power forecasting","volume":"111","author":"Zhang","year":"2015","journal-title":"Sol. Energy"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1049\/iet-esi.2018.0011","article-title":"Comparison of intelligent modelling techniques for forecasting solar energy and its application in solar PV based energy system","volume":"1","author":"Perveen","year":"2019","journal-title":"IET Energy Syst. Integr."},{"key":"ref_36","unstructured":"(2023, December 15). Energypedia, Energy Access Situation in Mozambique. Available online: https:\/\/energypedia.info\/wiki\/Situa%C3%A7%C3%A3o_de_Acesso_%C3%A0_Energia_em_Mo%C3%A7ambique."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"819","DOI":"10.1016\/j.renene.2015.09.011","article-title":"Benefits of solar forecasting for energy imbalance markets","volume":"86","author":"Kaur","year":"2016","journal-title":"Renew. Energy"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"446","DOI":"10.1016\/j.solener.2014.12.014","article-title":"A benchmarking of machine learning techniques for solar radiation forecasting in an insular context","volume":"112","author":"Lauret","year":"2015","journal-title":"Sol. Energy"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"705","DOI":"10.1016\/j.apenergy.2005.06.003","article-title":"An adaptive wavelet-network model for forecasting daily total solar-radiation","volume":"83","author":"Mellit","year":"2006","journal-title":"Appl. Energy"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"122104","DOI":"10.1016\/j.jclepro.2020.122104","article-title":"Neuro-fuzzy resource forecast in site suitability assessment for wind and solar energy: A mini review","volume":"269","author":"Adedeji","year":"2020","journal-title":"J. Clean. Prod."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"374","DOI":"10.1016\/j.energy.2014.04.011","article-title":"Estimation of 5-min time-step data of tilted solar global irradiation using ANN (Artificial Neural Network) model","volume":"70","author":"Dahmani","year":"2014","journal-title":"Energy"},{"key":"ref_42","first-page":"864","article-title":"Artificial neural network models for global solar energy and photovoltaic power forecasting over India","volume":"47","author":"Perveen","year":"2020","journal-title":"Energy Sources Part Recovery Util. Environ. Eff."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jclepro.2019.01.096","article-title":"Artificial neural network forecast of monthly mean daily global solar radiation of selected locations based on time series and month number","volume":"216","author":"Ozoegwu","year":"2019","journal-title":"J. Clean. Prod."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1114","DOI":"10.1016\/j.renene.2021.09.030","article-title":"Modelling global solar irradiance for any location on earth through regression analysis using high-resolution data","volume":"180","author":"Arumugham","year":"2021","journal-title":"Renew. Energy"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1016\/S0038-092X(99)00017-1","article-title":"Daily Insolation Forecasting Using a Multi-Stage Neural Network","volume":"66","author":"Kemmoku","year":"1999","journal-title":"Sol. Energy"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1097","DOI":"10.1016\/j.rser.2015.05.049","article-title":"Evaluation and performance comparison of different models for the estimation of solar radiation","volume":"50","author":"Teke","year":"2015","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Mustafa, M., and Malik, M.O.F. (2023). Factors Hindering Solar Photovoltaic System Implementation in Buildings and Infrastructure Projects: Analysis through a Multiple Linear Regression Model and Rule-Based Decision Support System. Buildings, 13.","DOI":"10.3390\/buildings13071786"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/0038-092X(75)90014-6","article-title":"A method of simulation of solar processes and its application","volume":"17","author":"Klein","year":"1975","journal-title":"Sol. Energy"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"537","DOI":"10.1016\/j.solener.2013.09.016","article-title":"Predicting solar irradiance with all-sky image features via regression","volume":"97","author":"Fu","year":"2013","journal-title":"Sol. Energy"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"8169510","DOI":"10.1155\/2022\/8169510","article-title":"Interval Prediction of Photovoltaic Power Using Improved NARX Network and Density Peak Clustering Based on Kernel Mahalanobis Distance","volume":"2022","author":"Chen","year":"2022","journal-title":"Complexity"},{"key":"ref_51","unstructured":"Wenham, S.R., Green, M.A., Watt, M.E., Corkish, R., and Sproul, A. (2011). Applied Photovoltaics, Routledge. [3rd ed.]."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Twidell, J., and Weir, T. (2015). Renewable Energy Resources, Routledge. [3rd ed.].","DOI":"10.4324\/9781315766416"},{"key":"ref_53","unstructured":"Sengupta, E.M., Habte, A., Gueymard, C., Wilbert, S., Renne, D., and Stoffel, T. (2015). Best Practices Handbook for the Collection and Use of Solar Resource Data for Solar Energy Applications, added, National Renewable Energy Laboratory. [2nd ed.]."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"871","DOI":"10.1016\/j.renene.2018.08.044","article-title":"Solar radiation forecasting using artificial neural network and random forest methods: Application to normal beam, horizontal diffuse and global components","volume":"132","author":"Benali","year":"2019","journal-title":"Renew. Energy"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Abuella, M., and Chowdhury, B. (2015, January 4\u20136). Solar power forecasting using artificial neural networks. Proceedings of the 2015 North American Power Symposium (NAPS), Charlotte, NC, USA.","DOI":"10.1109\/NAPS.2015.7335176"},{"key":"ref_56","unstructured":"(2023, April 30). FUNAE\u2014National Energy Fund of Mozambique, Data on the Solar Radiation Component Extracted from the Energy Atlas. Available online: https:\/\/funae.co.mz\/."},{"key":"ref_57","unstructured":"(2024, August 29). AERONET\u2014Aerosol Robotic Network, Site Information Page, Available online: https:\/\/aeronet.gsfc.nasa.gov\/new_web\/webtool_aod_v3.html."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.solener.2014.05.027","article-title":"An empirical model for ramp analysis of utility-scale solar PV power","volume":"107","author":"Mazumdar","year":"2014","journal-title":"Sol. Energy"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"17-1","DOI":"10.1029\/2000JC000639","article-title":"Effect of wind variability and variable air density on wave modeling","volume":"107","author":"Abdalla","year":"2002","journal-title":"J. Geophys. Res. Ocean."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"563","DOI":"10.1016\/j.solener.2009.09.009","article-title":"Modelling Complex Fenestration Systems using physical and virtual models","volume":"84","author":"Thanachareonkit","year":"2010","journal-title":"Sol. Energy"},{"key":"ref_61","first-page":"10","article-title":"A Statistical Modeling for spatial-temporal variability analysis of solar energy with respect to the climate in the Punjab Region","volume":"7","author":"Amjad","year":"2023","journal-title":"Bahria Univ. Res. J. Earth Sci."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1016\/j.solener.2019.08.044","article-title":"Comparison of statistical post-processing methods for probabilistic NWP forecasts of solar radiation","volume":"191","author":"Bakker","year":"2019","journal-title":"Sol. Energy"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"508","DOI":"10.1016\/j.solener.2017.06.003","article-title":"Statistical properties of clear and dark duration lengths","volume":"153","author":"Brabec","year":"2017","journal-title":"Sol. Energy"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1016\/j.enconman.2017.02.006","article-title":"A novel hybrid model for hourly global solar radiation prediction using random forests technique and firefly algorithm","volume":"138","author":"Ibrahim","year":"2017","journal-title":"Energy Convers. Manag."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"542","DOI":"10.1016\/j.renene.2015.12.069","article-title":"Machine learning for solar irradiance forecasting of photovoltaic system","volume":"90","author":"Li","year":"2016","journal-title":"Renew. Energy"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"487","DOI":"10.1016\/j.rser.2019.02.006","article-title":"Automatic hourly solar forecasting using machine learning models","volume":"105","author":"Yagli","year":"2019","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"1266","DOI":"10.1016\/j.solener.2015.10.023","article-title":"Very short-term irradiance forecasting at unobserved locations using spatio-temporal kriging","volume":"122","author":"Aryaputera","year":"2015","journal-title":"Sol. Energy"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"1766","DOI":"10.1175\/2009JAMC2090.1","article-title":"Holzer-Popp, and S. Dech. Short-Range Direct and Diffuse Irradiance Forecasts for Solar Energy Applications Based on Aerosol Chemical Transport and Numerical Weather Modeling","volume":"48","author":"Breitkreuz","year":"2009","journal-title":"J. Appl. Meteorol. Climatol."},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"4083","DOI":"10.5194\/amt-8-4083-2015","article-title":"Towards a long-term global aerosol optical depth record: Applying a consistent aerosol retrieval algorithm to MODIS and VIIRS-observed reflectance","volume":"8","author":"Levy","year":"2015","journal-title":"Atmospheric Meas. Tech."},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1016\/j.solener.2018.02.068","article-title":"Nowcasting solar irradiance using an analog method and geostationary satellite images","volume":"164","author":"Ayet","year":"2018","journal-title":"Sol. Energy"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"00010","DOI":"10.1051\/e3sconf\/20186100010","article-title":"Nowcasting the Output Power of PV Systems","volume":"61","author":"Paulescu","year":"2018","journal-title":"E3S Web Conf."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"343","DOI":"10.1016\/j.rser.2018.04.116","article-title":"Solar irradiation nowcasting by stochastic persistence: A new parsimonious, simple and efficient forecasting tool","volume":"92","author":"Voyant","year":"2018","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"117239","DOI":"10.1016\/j.energy.2020.117239","article-title":"A comparative study of several machine learning based non-linear regression methods in estimating solar radiation: Case studies of the USA and Turkey regions","volume":"197","author":"Alizamir","year":"2020","journal-title":"Energy"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"13370","DOI":"10.1002\/2014JD021550","article-title":"Improvement of aerosol optical depth retrieval using visibility data in China during the past 50 years","volume":"119","author":"Wu","year":"2014","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"645","DOI":"10.1016\/j.solener.2015.03.030","article-title":"Cloud motion and stability estimation for intra-hour solar forecasting","volume":"115","author":"Chow","year":"2015","journal-title":"Sol. Energy"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"113596","DOI":"10.1016\/j.apenergy.2019.113596","article-title":"Ensemble spatiotemporal forecasting of solar irradiation using variational Bayesian convolutional gate recurrent unit network","volume":"253","author":"Liu","year":"2019","journal-title":"Appl. Energy"},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Puga-Gil, D., Astray, G., Barreiro, E., G\u00e1lvez, J.F., and Mejuto, J.C. (2022). Global Solar Irradiation Modelling and Prediction Using Machine Learning Models for Their Potential Use in Renewable Energy Applications. Mathematics, 10.","DOI":"10.3390\/math10244746"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1016\/j.solener.2012.10.012","article-title":"Forecasting solar radiation on an hourly time scale using a Coupled AutoRegressive and Dynamical System (CARDS) model","volume":"87","author":"Huang","year":"2013","journal-title":"Sol. Energy"},{"key":"ref_79","doi-asserted-by":"crossref","unstructured":"Kosmopoulos, P.G., Kazadzis, S., El-Askary, H., Taylor, M., Gkikas, A., Proestakis, E., Kontoes, C., and El-Khayat, M.M. (2018). Earth-Observation-Based Estimation and Forecasting of Particulate Matter Impact on Solar Energy in Egypt. Remote Sens., 10.","DOI":"10.3390\/rs10121870"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"825","DOI":"10.1016\/j.rser.2015.04.077","article-title":"Solar radiation forecasting with multiple parameters neural networks","volume":"49","author":"Kashyap","year":"2015","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_81","doi-asserted-by":"crossref","unstructured":"Notton, G., Voyant, C., Fouilloy, A., Duchaud, J.L., and Nivet, M.L. (2019). Some Applications of ANN to Solar Radiation Estimation and Forecasting for Energy Applications. Appl. Sci., 9.","DOI":"10.3390\/app9010209"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"3435","DOI":"10.1016\/j.energy.2006.04.001","article-title":"Study of forecasting solar irradiance using neural networks with preprocessing sample data by wavelet analysis","volume":"31","author":"Cao","year":"2006","journal-title":"Energy"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"761","DOI":"10.1007\/s13762-013-0179-2","article-title":"Predicting solar radiation fluxes for solar energy system applications","volume":"10","author":"Saffaripour","year":"2013","journal-title":"Int. J. Environ. Sci. Technol."},{"key":"ref_84","first-page":"25","article-title":"Design and Analysis of a Passive Lighting Device for a Sustainable Office Environment in Hot-Arid Climate Conditions","volume":"13","author":"Daich","year":"2022","journal-title":"Int. J. Sustain. Constr. Eng. Technol."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"65","DOI":"10.3189\/S0260305500011277","article-title":"A meteorological estimation of relevant parameters for snow models","volume":"18","author":"Durand","year":"1993","journal-title":"Ann. Glaciol."},{"key":"ref_86","first-page":"9037","article-title":"Cloud speed sensor","volume":"6","author":"Fung","year":"2013","journal-title":"Atmos. Meas. Tech. Discussions"},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"547","DOI":"10.1038\/modpathol.3800322","article-title":"Tumor classification by tissue microarray profiling: Random Forest clustering applied to renal cell carcinoma","volume":"18","author":"Shi","year":"2005","journal-title":"Mod. Pathol."},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"102067","DOI":"10.1016\/j.est.2020.102067","article-title":"On the assessment of specific heat capacity of nanofluids for solar energy applications: Application of Gaussian process regression (GPR) approach","volume":"33","author":"Jamei","year":"2021","journal-title":"J. Energy Storage"},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Byrne, J., Taminiau, J., Kim, K.N., Lee, J., and Seo, J. (2019). Multivariate Analysis of Solar City Economics. Advances in Energy Systems, John Wiley & Sons, Ltd.","DOI":"10.1002\/9781119508311.ch29"},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1016\/j.solener.2017.04.066","article-title":"Multi-site solar power forecasting using gradient boosted regression trees","volume":"150","author":"Persson","year":"2017","journal-title":"Sol. Energy"},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"121645","DOI":"10.1016\/j.apenergy.2023.121645","article-title":"Forecasting solar energy production in Spain: A comparison of univariate and multivariate models at the national level","volume":"350","author":"Riquelme","year":"2023","journal-title":"Appl. Energy"},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"714","DOI":"10.1016\/j.solener.2008.02.003","article-title":"Hourly solar radiation forecasting using optimal coefficient 2-D linear filters and feed-forward neural networks","volume":"82","author":"Gerek","year":"2008","journal-title":"Sol. Energy"},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"1134","DOI":"10.1049\/rpg2.12389","article-title":"Execution of synthetic Bayesian model average for solar energy forecasting","volume":"16","author":"Abedinia","year":"2022","journal-title":"IET Renew. Power Gener."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"61","DOI":"10.17159\/2413-3051\/2017\/v28i2a1640","article-title":"Solar resource classification in South Africa using a new index","volume":"28","author":"Zhandire","year":"2017","journal-title":"J. Energy S. Afr."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"3103","DOI":"10.1016\/B978-0-443-15274-0.50495-9","article-title":"Big data analysis of solar energy fluctuation characteristics and integration of wind-photovoltaic to hydrogen system","volume":"52","author":"Chen","year":"2023","journal-title":"Comput. Aided Chem. Eng."},{"key":"ref_96","doi-asserted-by":"crossref","unstructured":"Woollen, E., Ryan, C.M., Baumert, S., Vollmer, F., Grundy, I., Fisher, J., Fernando, J., Luz, A., Ribeiro, N., and Lisboa, S.N. (2016). Charcoal production in the Mopane woodlands of Mozambique: What are the trade-offs with other ecosystem services?. Philos. Trans. R. Soc. B Biol. Sci., 371.","DOI":"10.1098\/rstb.2015.0315"},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"2479","DOI":"10.5194\/hess-14-2479-2010","article-title":"Topographic effects on solar radiation distribution in mountainous watersheds and their influence on reference evapotranspiration estimates at watershed scale","volume":"14","author":"Aguilar","year":"2010","journal-title":"Hydrol. Earth Syst. Sci."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"859","DOI":"10.1002\/er.4440140808","article-title":"Solar energy at various depths below a water surface","volume":"14","author":"Jamal","year":"1990","journal-title":"Int. J. Energy Res."},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1016\/j.solener.2019.06.020","article-title":"Solar irradiance modelling using an offline coupling procedure for the Weather Research and Forecasting (WRF) model","volume":"188","author":"Pereira","year":"2019","journal-title":"Sol. Energy"},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1016\/j.solener.2017.01.058","article-title":"Assessing the value of simulated regional weather variability in solar forecasting using numerical weather prediction","volume":"144","author":"Huang","year":"2017","journal-title":"Sol. Energy"},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"1697","DOI":"10.5194\/acp-5-1697-2005","article-title":"Intercomparison of satellite retrieved aerosol optical depth over ocean during the period September 1997 to December 2000","volume":"5","author":"Myhre","year":"2005","journal-title":"Atmos. Chem. Phys."},{"key":"ref_102","unstructured":"Penner, J.E., Zhang, S.Y., Chin, M., Chuang, C.C., Feichter, J., Feng, Y., Geogdzhayev, I.V., Ginoux, P., Herzog, M., and Higurashi, A. (2024, September 04). A Comparison of Model- and Satellite-Derived Aerosol Optical Depth and Reflectivity. Available online: https:\/\/journals.ametsoc.org\/view\/journals\/atsc\/59\/3\/1520-0469_2002_059_0441_acomas_2.0.co_2.xml."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"8801","DOI":"10.1002\/2016JD026355","article-title":"The impact of aerosol vertical distribution on aerosol optical depth retrieval using CALIPSO and MODIS data: Case study over dust and smoke regions","volume":"122","author":"Wu","year":"2017","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"D8","DOI":"10.1029\/2003JD004375","article-title":"On the sources of bias in aerosol optical depth retrieval in the UV range","volume":"109","author":"Arola","year":"2004","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"D24","DOI":"10.1029\/2003JD004044","article-title":"Aerosol radiative forcing and the accuracy of satellite aerosol optical depth retrieval","volume":"108","author":"Chylek","year":"2003","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"117362","DOI":"10.1016\/j.atmosenv.2020.117362","article-title":"Simplified and Fast Atmospheric Radiative Transfer model for satellite-based aerosol optical depth retrieval","volume":"224","author":"Yan","year":"2020","journal-title":"Atmos. Environ."},{"key":"ref_107","unstructured":"Kleissl, J. (2024, September 21). Current State of the Art in Solar Forecasting. Available online: https:\/\/escholarship.org\/uc\/item\/4fx8983f."},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"747","DOI":"10.1016\/j.renene.2022.04.065","article-title":"Benchmarks for solar radiation time series forecasting","volume":"191","author":"Voyant","year":"2022","journal-title":"Renew. Energy"},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"807","DOI":"10.1016\/j.solener.2010.02.006","article-title":"A 24-h forecast of solar irradiance using artificial neural network: Application for performance prediction of a grid-connected PV plant at Trieste, Italy","volume":"84","author":"Mellit","year":"2010","journal-title":"Sol. Energy"},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1016\/j.solener.2018.02.011","article-title":"Operational photovoltaics power forecasting using seasonal time series ensemble","volume":"166","author":"Yang","year":"2018","journal-title":"Sol. Energy"},{"key":"ref_111","doi-asserted-by":"crossref","unstructured":"Didavi, A.B.K., Agbokpanzo, R.G., and Agbomahena, M. (2021, January 8\u201310). Comparative study of Decision Tree, Random Forest and XGBoost performance in forecasting the power output of a photovoltaic system. Proceedings of the 2021 4th International Conference on Bio-Engineering for Smart Technologies (BioSMART), Paris\/Creteil, France.","DOI":"10.1109\/BioSMART54244.2021.9677566"},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.enbuild.2018.04.008","article-title":"Random Forest based hourly building energy prediction","volume":"171","author":"Wang","year":"2018","journal-title":"Energy Build."},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"115940","DOI":"10.1016\/j.energy.2019.115940","article-title":"Random forest solar power forecast based on classification optimization","volume":"187","author":"Liu","year":"2019","journal-title":"Energy"},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random Forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v102.i06","article-title":"The R Package stagedtrees for Structural Learning of Stratified Staged Trees","volume":"102","author":"Carli","year":"2022","journal-title":"J. Stat. Softw."},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"255","DOI":"10.1016\/j.solener.2019.03.068","article-title":"An ultra-fast way of searching weather analogs for renewable energy forecasting","volume":"185","author":"Yang","year":"2019","journal-title":"Sol. Energy"},{"key":"ref_117","doi-asserted-by":"crossref","unstructured":"Fahrmeir, L., Kneib, T., Lang, S., and Marx, B. (2013). Regression: Models, Methods and Applications, Springer.","DOI":"10.1007\/978-3-642-34333-9"},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"128566","DOI":"10.1016\/j.jclepro.2021.128566","article-title":"Deep learning models for solar irradiance forecasting: A comprehensive review","volume":"318","author":"Kumari","year":"2021","journal-title":"J. Clean. Prod."},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v091.i09","article-title":"Fuzzy Forests: Extending Random Forest Feature Selection for Correlated, High-Dimensional Data","volume":"91","author":"Conn","year":"2019","journal-title":"J. Stat. Softw."},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1016\/j.jclepro.2018.09.016","article-title":"Determinants of household adoption of solar energy technology in rural Ethiopia","volume":"204","author":"Guta","year":"2018","journal-title":"J. Clean. Prod."},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.solener.2014.08.023","article-title":"A comparative study between fuzzy linear regression and support vector regression for global solar radiation prediction in Iran","volume":"109","author":"Ramedani","year":"2014","journal-title":"Sol. Energy"},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v100.i11","article-title":"BayesSUR: An R Package for High-Dimensional Multivariate Bayesian Variable and Covariance Selection in Linear Regression","volume":"100","author":"Zhao","year":"2021","journal-title":"J. Stat. Softw."},{"key":"ref_123","doi-asserted-by":"crossref","unstructured":"Chodakowska, E., Nazarko, J., Nazarko, \u0141., Rabayah, H.S., Abendeh, R.M., and Alawneh, R. (2023). ARIMA Models in Solar Radiation Forecasting in Different Geographic Locations. Energies, 16.","DOI":"10.3390\/en16135029"},{"key":"ref_124","unstructured":"Haddad, M., Nicod, J., Mainassara, Y.B., Rabehasaina, L., Al Masry, Z., and P\u00e9ra, M. (2019, January 25\u201327). Wind and Solar Forecasting for Renewable Energy System using SARIMA-based Model. Proceedings of the International Conference on Time Series and Forecasting, Gran Canaria, Spain. Available online: https:\/\/hal.science\/hal-02867736."},{"key":"ref_125","doi-asserted-by":"crossref","unstructured":"Atique, S., Noureen, S., Roy, V., Subburaj, V., Bayne, S., and Macfie, J. (2019, January 7\u20139). Forecasting of total daily solar energy generation using ARIMA: A case study. Proceedings of the 2019 IEEE 9th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, NY, USA.","DOI":"10.1109\/CCWC.2019.8666481"},{"key":"ref_126","doi-asserted-by":"crossref","first-page":"59","DOI":"10.7836\/kses.2019.39.3.059","article-title":"Solar Power Generation Forecast Model Using Seasonal ARIMA","volume":"39","author":"Lee","year":"2019","journal-title":"J. Korean Sol. Energy Soc."},{"key":"ref_127","doi-asserted-by":"crossref","unstructured":"Konstantinou, M., Peratikou, S., and Charalambides, A.G. (2021). Solar Photovoltaic Forecasting of Power Output Using LSTM Networks. Atmosphere, 12.","DOI":"10.3390\/atmos12010124"},{"key":"ref_128","unstructured":"Wilson, P., and Tanaka, O.K. (2024, February 06). Statistics, Basic Concepts \u2014Wilson Pereira\/Oswaldo K. Tanaka, 2018. Available online: https:\/\/www.estantevirtual.com.br\/livros\/wilson-pereira-oswaldo-k-tanaka\/estatistica-conceitos-basicos\/189548989."},{"key":"ref_129","doi-asserted-by":"crossref","unstructured":"Hauser, A., Oesch, D., Foppa, N., and Wunderle, S. (2005). NOAA AVHRR derived aerosol optical depth over land. J. Geophys. Res. Atmos., 110.","DOI":"10.1029\/2004JD005439"},{"key":"ref_130","unstructured":"(2024, December 09). INAM\u2014Mozambique\u2019s National Institute of Meteorology, Weather and Solar Data, Available online: https:\/\/www.inam.gov.mz\/index.php\/pt\/."},{"key":"ref_131","unstructured":"(2024, December 09). Eduardo Mondlane University\u2014Undergraduate, Postgraduate, Extension and Innovation (Department of Physics). Available online: https:\/\/uem.mz\/index.php\/en\/home-english\/."}],"container-title":["Energies"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1996-1073\/18\/6\/1469\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T16:55:00Z","timestamp":1760028900000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1996-1073\/18\/6\/1469"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,17]]},"references-count":131,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2025,3]]}},"alternative-id":["en18061469"],"URL":"https:\/\/doi.org\/10.3390\/en18061469","relation":{},"ISSN":["1996-1073"],"issn-type":[{"value":"1996-1073","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,17]]}}}