{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T06:01:43Z","timestamp":1769580103875,"version":"3.49.0"},"reference-count":202,"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\u2013OGET, Faculty of Engineering, Eduardo Mondlane University","award":["CS-OGET\/2023"],"award-info":[{"award-number":["CS-OGET\/2023"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Energies"],"abstract":"<jats:p>To maximize photovoltaic (PV) production, it is necessary to estimate the amount of solar radiation that is available on Earth\u2019s surface, as it can occasionally vary. This study aimed to systematize the parametric forecast (PF) of solar energy over time, adopting the validation of estimates by machine learning models (MLMs), with highly complex analyses as inclusion criteria and studies not validated in the short or long term as exclusion criteria. A total of 145 scholarly sources were examined, with a value of 0.17 for bias risk. Four components were analyzed: atmospheric, temporal, geographic, and spatial components. These quantify dispersed, absorbed, and reflected solar energy, causing energy to fluctuate when it arrives at the surface of a PV plant. The results revealed strong trends towards the adoption of artificial neural network (ANN), random forest (RF), and simple linear regression (SLR) models for a sample taken from the Nipepe station in Niassa, validated by a PF model with errors of 0.10, 0.11, and 0.15. The included studies\u2019 statistically measured parameters showed high trends of dependence on the variability in transmittances. The synthesis of the results, hence, improved the accuracy of the estimations produced by MLMs, making the model applicable to any reality, with a very low margin of error for the calculated energy. Most studies adopted large time intervals of atmospheric parameters. Applying interpolation models can help extrapolate short scales, as their inference and treatment still require a high investment cost. Due to the need to access the forecasted energy over land, this study was funded by CS\u2013OGET.<\/jats:p>","DOI":"10.3390\/en18061460","type":"journal-article","created":{"date-parts":[[2025,3,17]],"date-time":"2025-03-17T06:36:23Z","timestamp":1742193383000},"page":"1460","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Parametric Forecast of Solar Energy over Time by Applying Machine Learning Techniques: Systematic Review"],"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","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_2","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_3","unstructured":"Iqbal, M. (1983). An Introduction to Solar Radiation, Academic Press."},{"key":"ref_4","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_5","unstructured":"(2023, December 16). Tracking SDG 7\u2014The Energy Progress Report 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_6","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_7","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_8","doi-asserted-by":"crossref","first-page":"138","DOI":"10.1016\/j.solener.2012.10.007","article-title":"Accuracy of the solar irradiance forecasts of the Japan Meteorological Agency mesoscale model for the Kanto region, Japan","volume":"98","author":"Ohtake","year":"2013","journal-title":"Sol. Energy"},{"key":"ref_9","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_10","doi-asserted-by":"crossref","unstructured":"Zhang, P., Ma, X., and She, K. (2019). Forecasting Japan\u2019s Solar Energy Consumption Using a Novel Incomplete Gamma Grey Model. Sustainability, 11.","DOI":"10.3390\/su11215921"},{"key":"ref_11","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_12","doi-asserted-by":"crossref","first-page":"124367","DOI":"10.1016\/j.energy.2022.124367","article-title":"Genetic algorithm selection of the weather research and forecasting model physics to support wind and solar energy integration","volume":"254","author":"Sward","year":"2022","journal-title":"Energy"},{"key":"ref_13","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_14","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_15","unstructured":"Duf, J.A., and Beckman, W.A. (1980). Solar Engineering of Thermal Processes, Wiley."},{"key":"ref_16","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_17","doi-asserted-by":"crossref","first-page":"1185","DOI":"10.1016\/j.solener.2018.09.055","article-title":"A spatio-temporal city-scale assessment of residential photovoltaic power integration scenarios","volume":"174","author":"Litjens","year":"2018","journal-title":"Sol. Energy"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"804","DOI":"10.1016\/j.solener.2015.09.047","article-title":"Baseline and target values for regional and point PV power forecasts: Toward improved solar forecasting","volume":"122","author":"Zhang","year":"2015","journal-title":"Sol. Energy"},{"key":"ref_19","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_20","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_21","doi-asserted-by":"crossref","unstructured":"Jung, Y., Lee, H., Kim, J., Cho, Y., Kim, J., and Lee, Y.G. (2016). Spatio-Temporal Characteristics in the Clearness Index Derived from Global Solar Radiation Observations in Korea. Atmosphere, 7.","DOI":"10.3390\/atmos7040055"},{"key":"ref_22","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_23","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_24","unstructured":"Wenham, S.R., Green, M.A., Watt, M.E., Corkish, R., and Sproul, A. (2011). Applied Photovoltaics, Routledge. [3rd ed.]."},{"key":"ref_25","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_26","unstructured":"(2023, December 15). Energy Access Situation in Mozambique\u2014Energypedia. Available online: https:\/\/energypedia.info\/wiki\/Situa%C3%A7%C3%A3o_de_Acesso_%C3%A0_Energia_em_Mo%C3%A7ambique."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"100355","DOI":"10.1016\/j.csite.2018.11.006","article-title":"Analysis and forecasting of weather conditions in Oman for renewable energy applications","volume":"13","author":"Yousif","year":"2019","journal-title":"Case Stud. Therm. Eng."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1016\/j.energy.2018.01.177","article-title":"Hourly day-ahead solar irradiance prediction using weather forecasts by LSTM","volume":"148","author":"Qing","year":"2018","journal-title":"Energy"},{"key":"ref_29","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_30","unstructured":"Kariniotakis, G. (2017). 4\u2014Mathematical methods for optimized solar forecasting. Renewable Energy Forecasting, Woodhead Publishing."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"044045","DOI":"10.1088\/1748-9326\/abe06d","article-title":"Short-term solar irradiance forecasting using convolutional neural networks and cloud imagery","volume":"16","author":"Choi","year":"2021","journal-title":"Environ. Res. Lett."},{"key":"ref_32","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_33","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_34","doi-asserted-by":"crossref","unstructured":"Lauret, P., Lorenz, E., and David, M. (2016). Solar Forecasting in a Challenging Insular Context. Atmosphere, 7.","DOI":"10.3390\/atmos7020018"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"119043","DOI":"10.1016\/j.renene.2023.119043","article-title":"A regional solar forecasting approach using generative adversarial networks with solar irradiance maps","volume":"216","author":"Wen","year":"2023","journal-title":"Renew. Energy"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1766","DOI":"10.1175\/2009JAMC2090.1","article-title":"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_37","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1016\/j.energy.2016.06.139","article-title":"Short term solar radiation forecasting: Island versus continental sites","volume":"113","author":"Boland","year":"2016","journal-title":"Energy"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.pecs.2018.10.003","article-title":"A current perspective on the accuracy of incoming solar energy forecasting","volume":"70","author":"Blaga","year":"2019","journal-title":"Prog. Energy Combust. Sci."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1016\/j.energy.2019.04.127","article-title":"A hybrid spatio-temporal forecasting of solar generating resources for grid integration","volume":"177","author":"Nam","year":"2019","journal-title":"Energy"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Kumler, A., Xie, Y., and Zhang, Y. (2018). A New Approach for Short-Term Solar Radiation Forecasting Using the Estimation of Cloud Fraction and Cloud Albedo, NREL.","DOI":"10.2172\/1476449"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Camacho, D., Braubach, L., Venticinque, S., and Badica, C. (2015). A Study of Machine Learning Techniques for Daily Solar Energy Forecasting Using Numerical Weather Models. Intelligent Distributed Computing VIII, Springer International Publishing.","DOI":"10.1007\/978-3-319-10422-5"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"2741","DOI":"10.1007\/s11277-020-07173-w","article-title":"A Solar Energy Forecast Model Using Neural Networks: Application for Prediction of Power for Wireless Sensor Networks in Precision Agriculture","volume":"112","author":"Dhillon","year":"2020","journal-title":"Wirel. Pers. Commun."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.compenvurbsys.2015.03.002","article-title":"Spatio-temporal modeling of roof-top photovoltaic panels for improved technical potential assessment and electricity peak load offsetting at the municipal scale","volume":"52","author":"Zink","year":"2015","journal-title":"Comput. Environ. Urban Syst."},{"key":"ref_44","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_45","doi-asserted-by":"crossref","unstructured":"McCandless, T., and Jim\u00e9nez, P.A. (2020). Examining the Potential of a Random Forest Derived Cloud Mask from GOES-R Satellites to Improve Solar Irradiance Forecasting. Energies, 13.","DOI":"10.3390\/en13071671"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"01163","DOI":"10.1051\/e3sconf\/202130901163","article-title":"Analysis of Solar Power Generation Forecasting Using Machine Learning Techniques","volume":"309","author":"Anuradha","year":"2021","journal-title":"E3S Web Conf."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1016\/j.renene.2014.12.071","article-title":"On the role of lagged exogenous variables and spatio\u2013temporal correlations in improving the accuracy of solar forecasting methods","volume":"78","author":"Zagouras","year":"2015","journal-title":"Renew. Energy"},{"key":"ref_48","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_49","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_50","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_51","doi-asserted-by":"crossref","first-page":"3356","DOI":"10.1109\/TGRS.2006.877952","article-title":"Scatterometer Backscatter Uncertainty Due to Wind Variability","volume":"44","author":"Portabella","year":"2006","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"11","DOI":"10.5194\/asr-12-11-2015","article-title":"Wind variability in a coastal area (Alfacs Bay, Ebro River delta)","volume":"12","author":"Cerralbo","year":"2015","journal-title":"Adv. Sci. Res."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"100057","DOI":"10.1016\/j.seja.2024.100057","article-title":"Very short-term probabilistic and scenario-based forecasting of solar irradiance using Markov-chain mixture distribution modeling","volume":"4","author":"Munkhammar","year":"2024","journal-title":"Sol. Energy Adv."},{"key":"ref_54","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_55","doi-asserted-by":"crossref","first-page":"1782","DOI":"10.1016\/j.solener.2010.07.003","article-title":"Quantifying PV power Output Variability","volume":"84","author":"Hoff","year":"2010","journal-title":"Sol. Energy"},{"key":"ref_56","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_57","doi-asserted-by":"crossref","first-page":"985","DOI":"10.1016\/j.renene.2019.05.075","article-title":"Short-term forecasting of solar irradiance","volume":"143","author":"Paulescu","year":"2019","journal-title":"Renew. Energy"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"6758","DOI":"10.3390\/s140406758","article-title":"Spatial Estimation of Sub-Hour Global Horizontal Irradiance Based on Official Observations and Remote Sensors","volume":"14","year":"2014","journal-title":"Sensors"},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"1019","DOI":"10.1016\/j.rser.2015.04.174","article-title":"Modeling and analysis of the spatiotemporal variations of photosynthetically active radiation in China during 1961\u20132012","volume":"49","author":"Wang","year":"2015","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1186\/s13643-021-01626-4","article-title":"The PRISMA 2020 statement: An updated guideline for reporting systematic reviews","volume":"10","author":"Page","year":"2021","journal-title":"Syst. Rev."},{"key":"ref_61","unstructured":"(2023, April 30). FUNAE (National Energy Fund of Mozambique) [Online]. Available online: https:\/\/funae.co.mz\/."},{"key":"ref_62","unstructured":"(2023, May 05). INAM (National Meteorology Institute of Mozambique) [Online], Available online: https:\/\/www.inam.gov.mz\/index.php\/pt\/."},{"key":"ref_63","unstructured":"(2023, February 12). AERONET (Aerosol Robotic Network) [Online], Available online: https:\/\/aeronet.gsfc.nasa.gov\/."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.solener.2016.12.055","article-title":"Cloud cover effect of clear-sky index distributions and differences between human and automatic cloud observations","volume":"144","author":"Smith","year":"2017","journal-title":"Sol. Energy"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"352","DOI":"10.32479\/ijeep.16187","article-title":"Analysis of Existing and Forecasting for Coal and Solar Energy Consumption on Climate Change in Asia Pacific: New Evidence for Sustainable Development Goals","volume":"14","author":"Kurniadi","year":"2024","journal-title":"Int. J. Energy Econ. Policy"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"100059","DOI":"10.1016\/j.seja.2024.100059","article-title":"Enhancing the reliability of probabilistic PV power forecasts using conformal prediction","volume":"4","author":"Renkema","year":"2024","journal-title":"Sol. Energy Adv."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"1020","DOI":"10.1016\/j.renene.2019.06.128","article-title":"Wind speed variability and wind power potential over Turkey: Case studies for \u00c7anakkale and \u0130stanbul","volume":"145","author":"Arslan","year":"2020","journal-title":"Renew. Energy"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"e1824","DOI":"10.1002\/met.1824","article-title":"Temporal and spatial variability analysis of the solar radiation in a region affected by the intertropical convergence zone","volume":"27","author":"Vindel","year":"2020","journal-title":"Meteorol. Appl."},{"key":"ref_69","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_70","doi-asserted-by":"crossref","first-page":"1207","DOI":"10.1016\/j.energy.2018.10.053","article-title":"Sustainable energy storage for solar home systems in rural Sub-Saharan Africa\u2014A comparative examination of lifecycle aspects of battery technologies for circular economy, with emphasis on the South African context","volume":"166","author":"Charles","year":"2019","journal-title":"Energy"},{"key":"ref_71","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_72","doi-asserted-by":"crossref","unstructured":"Lichtenwoehrer, P., Abart-Heriszt, L., Kretschmer, F., Suppan, F., Stoeglehner, G., and Neugebauer, G. (2021). Evaluating Spatial Interdependencies of Sector Coupling Using Spatiotemporal Modelling. Energies, 14.","DOI":"10.3390\/en14051256"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"1371","DOI":"10.1016\/j.solener.2015.10.053","article-title":"A novel clustering approach for short-term solar radiation forecasting","volume":"122","author":"Ghayekhloo","year":"2015","journal-title":"Sol. Energy"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"1402","DOI":"10.1016\/j.egypro.2012.05.156","article-title":"Linear Regression Model in Estimating Solar Radiation in Perlis","volume":"18","author":"Ibrahim","year":"2012","journal-title":"Energy Procedia"},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Twidell, J., and Weir, T. (2015). Renewable Energy Resources, Routledge. [3rd ed.].","DOI":"10.4324\/9781315766416"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"2411","DOI":"10.5194\/amt-7-2411-2014","article-title":"Retrieval of aerosol optical depth over land surfaces from AVHRR data","volume":"7","author":"Mei","year":"2014","journal-title":"Atmos. Meas. Techol."},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Zhang, W., Xu, H., and Zheng, F. (2018). Aerosol Optical Depth Retrieval over East Asia Using Himawari-8\/AHI Data. Remote Sens., 10.","DOI":"10.3390\/rs10010137"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"5484","DOI":"10.1029\/2017JD028116","article-title":"Retrieval of Aerosol Optical Depth From INSAT-3D Imager Over Asian Landmass and Adjoining Ocean: Retrieval Uncertainty and Validation","volume":"123","author":"Mishra","year":"2018","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_79","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":"Atmos. Meas. Technol."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"11977","DOI":"10.5194\/acp-11-11977-2011","article-title":"A multi-angle aerosol optical depth retrieval algorithm for geostationary satellite data over the United States","volume":"11","author":"Zhang","year":"2011","journal-title":"Atmos. Chem. Phys."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"2017","DOI":"10.1016\/j.solener.2012.04.004","article-title":"Assessment of forecasting techniques for solar power production with no exogenous inputs","volume":"86","author":"Pedro","year":"2012","journal-title":"Sol. Energy"},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"3293","DOI":"10.5194\/amt-9-3293-2016","article-title":"A surface reflectance scheme for retrieving aerosol optical depth over urban surfaces in MODIS Dark Target retrieval algorithm","volume":"9","author":"Gupta","year":"2016","journal-title":"Atmos. Meas. Technol."},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Sun, L., Wei, J., Bilal, M., Tian, X., Jia, C., Guo, Y., and Mi, X. (2016). Aerosol Optical Depth Retrieval over Bright Areas Using Landsat 8 OLI Images. Remote Sens., 8.","DOI":"10.3390\/rs8010023"},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"135860","DOI":"10.1016\/j.jclepro.2023.135860","article-title":"How solar radiation forecasting impacts the utilization of solar energy: A critical review","volume":"388","author":"Krishnan","year":"2023","journal-title":"J. Clean. Prod."},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Tian, X., Liu, S., Sun, L., and Liu, Q. (2018). Retrieval of Aerosol Optical Depth in the Arid or Semiarid Region of Northern Xinjiang, China. Remote Sens., 10.","DOI":"10.3390\/rs10020197"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"581","DOI":"10.1016\/0741-983X(88)90050-1","article-title":"A simple method for predicting global solar radiation on a horizontal surface","volume":"5","author":"Gopinathan","year":"1988","journal-title":"Sol. Wind Technol."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1016\/j.renene.2015.11.024","article-title":"How significant is the stability of the radiative regime when the best operation of solar DHW systems is evaluated?","volume":"88","author":"Badescu","year":"2016","journal-title":"Renew. Energy"},{"key":"ref_88","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":"Volume 52","author":"Kokossis","year":"2023","journal-title":"Computer Aided Chemical Engineering"},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.solener.2014.09.010","article-title":"Uncertainty of daylight illuminance on vertical building fa\u00e7ades when determined from sky scanner data: A numerical study","volume":"110","author":"Kocifaj","year":"2014","journal-title":"Sol. Energy"},{"key":"ref_90","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_91","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_92","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_93","doi-asserted-by":"crossref","unstructured":"Jogunuri, S., Ft, J., Stonier, A.A., Peter, G., Jayaraj, J., and Ganji, V. (2024). Random Forest machine learning algorithm based seasonal multi-step ahead short-term solar photovoltaic power output forecasting. IET Renewable Power Generation, Wiley.","DOI":"10.1049\/rpg2.12921"},{"key":"ref_94","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_95","doi-asserted-by":"crossref","first-page":"125939","DOI":"10.1016\/j.energy.2022.125939","article-title":"Understanding multi-scale spatiotemporal energy consumption data: A visual analysis approach","volume":"263","author":"Wu","year":"2023","journal-title":"Energy"},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"304","DOI":"10.1016\/j.procs.2017.09.045","article-title":"Solar Irradiance Forecasting Using Deep Neural Networks","volume":"114","author":"Alzahrani","year":"2017","journal-title":"Procedia Comput. Sci."},{"key":"ref_97","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_98","doi-asserted-by":"crossref","first-page":"99","DOI":"10.3390\/solar4010005","article-title":"A Review of Solar Forecasting Techniques and the Role of Artificial Intelligence","volume":"4","author":"Barhmi","year":"2024","journal-title":"Solar"},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"1367","DOI":"10.1016\/j.renene.2021.02.017","article-title":"Modeling and predicting the electricity production in hydropower using conjunction of wavelet transform, long short-term memory and random forest models","volume":"170","author":"Zolfaghari","year":"2021","journal-title":"Renew. Energy"},{"key":"ref_100","unstructured":"Nielsen, M.A. (2015). Neural Networks and Deep Learning, Determination Press. Available online: http:\/\/neuralnetworksanddeeplearning.com."},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v102.i07","article-title":"NeuralSens: Sensitivity Analysis of Neural Networks","volume":"102","author":"Pizarroso","year":"2022","journal-title":"J. Stat. Softw."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v046.i07","article-title":"Neural Networks in R Using the Stuttgart Neural Network Simulator: RSNNS","volume":"46","author":"Bergmeir","year":"2012","journal-title":"J. Stat. Softw."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v085.i11","article-title":"NeuralNetTools: Visualization and Analysis Tools for Neural Networks","volume":"85","author":"Beck","year":"2018","journal-title":"J. Stat. Softw."},{"key":"ref_104","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_105","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1016\/j.apenergy.2014.05.055","article-title":"Artificial neural network based daily local forecasting for global solar radiation","volume":"130","author":"Amrouche","year":"2014","journal-title":"Appl. Energy"},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1016\/j.renene.2016.01.013","article-title":"Multilayer Perceptron approach for estimating 5-min and hourly horizontal global irradiation from exogenous meteorological data in locations without solar measurements","volume":"90","author":"Dahmani","year":"2016","journal-title":"Renew. Energy"},{"key":"ref_107","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_108","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/0038-092X(80)90021-3","article-title":"Estimation and prediction of global solar radiation over Greece","volume":"24","author":"Flocas","year":"1980","journal-title":"Sol. Energy"},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1016\/j.solener.2019.11.079","article-title":"A copula-based Bayesian method for probabilistic solar power forecasting","volume":"196","author":"Panamtash","year":"2020","journal-title":"Sol. Energy"},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.enconman.2016.04.051","article-title":"Assessing the potential of random forest method for estimating solar radiation using air pollution index","volume":"119","author":"Sun","year":"2016","journal-title":"Energy Convers. Manag."},{"key":"ref_111","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_112","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1016\/j.solener.2013.02.018","article-title":"A high-resolution, cloud-assimilating numerical weather prediction model for solar irradiance forecasting","volume":"92","author":"Mathiesen","year":"2013","journal-title":"Sol. Energy"},{"key":"ref_113","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_114","doi-asserted-by":"crossref","first-page":"406","DOI":"10.1016\/j.enconman.2015.02.052","article-title":"A hybrid method for forecasting the energy output of photovoltaic systems","volume":"95","author":"Ramsami","year":"2015","journal-title":"Energy Convers. Manag."},{"key":"ref_115","doi-asserted-by":"crossref","unstructured":"Gupta, S., Katta, A.R., Baldaniya, Y., and Kumar, R. (2020, January 30\u201331). Hybrid Random Forest and Particle Swarm Optimization Algorithm for Solar Radiation Prediction. Proceedings of the 2020 IEEE 5th International Conference on Computing Communication and Automation (ICCCA), Greater Noida, India.","DOI":"10.1109\/ICCCA49541.2020.9250715"},{"key":"ref_116","doi-asserted-by":"crossref","unstructured":"Travieso-Gonz\u00e1lez, C.M., Cabrera-Quintero, F., Pi\u00f1\u00e1n-Roescher, A., and Celada-Bernal, S. (2024). A Review and Evaluation of the State of Art in Image-Based Solar Energy Forecasting: The Methodology and Technology Used. Appl. Sci., 14.","DOI":"10.3390\/app14135605"},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"112224","DOI":"10.1016\/j.rser.2022.112224","article-title":"A review of behind-the-meter solar forecasting","volume":"160","author":"Erdener","year":"2022","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"e4366","DOI":"10.1002\/dac.4366","article-title":"A review on solar forecasting and power management approaches for energy-harvesting wireless sensor networks","volume":"33","author":"Sharma","year":"2020","journal-title":"Int. J. Commun. Syst."},{"key":"ref_119","unstructured":"Zhang, Y., Shen, Y., Xia, X., and Shi, G. (2020). Validation of GFS day-ahead solar irradiance forecasts in China. arXiv."},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v097.i04","article-title":"The R Package forestinventory: Design-Based Global and Small Area Estimations for Multiphase Forest Inventories","volume":"97","author":"Hill","year":"2021","journal-title":"J. Stat. Softw."},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"1","DOI":"10.18637\/jss.v054.i08","article-title":"A Fortran 90 Program for the Generalized Order-Restricted Information Criterion","volume":"54","author":"Kuiper","year":"2013","journal-title":"J. Stat. Softw."},{"key":"ref_122","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_123","doi-asserted-by":"crossref","first-page":"391","DOI":"10.1016\/j.solener.2018.12.075","article-title":"Reconciling solar forecasts: Sequential reconciliation","volume":"179","author":"Yagli","year":"2019","journal-title":"Sol. Energy"},{"key":"ref_124","doi-asserted-by":"crossref","first-page":"8044","DOI":"10.1016\/j.egypro.2014.11.841","article-title":"CO2 Geological Storage and Utilization for a Carbon Neutral \u201cPower-to-gas-to-power\u201d Cycle to Even Out Fluctuations of Renewable Energy Provision","volume":"63","author":"Nakaten","year":"2014","journal-title":"Energy Procedia"},{"key":"ref_125","doi-asserted-by":"crossref","unstructured":"Zwane, N., Tazvinga, H., Botai, C., Murambadoro, M., Botai, J., de Wit, J., Mabasa, B., Daniel, S., and Mabhaudhi, T. (2022). A Bibliometric Analysis of Solar Energy Forecasting Studies in Africa. Energies, 15.","DOI":"10.3390\/en15155520"},{"key":"ref_126","unstructured":"Yang, C., and Xie, L. (2012, January 9\u201311). A novel ARX-based multi-scale spatio-temporal solar power forecast model. Proceedings of the 2012 North American Power Symposium (NAPS), Champaign, IL, USA."},{"key":"ref_127","doi-asserted-by":"crossref","first-page":"111758","DOI":"10.1016\/j.rser.2021.111758","article-title":"A review of very short-term wind and solar power forecasting","volume":"153","author":"Tawn","year":"2022","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_128","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.jneumeth.2013.08.024","article-title":"A comparison of random forest regression and multiple linear regression for prediction in neuroscience","volume":"220","author":"Smith","year":"2013","journal-title":"J. Neurosci. Methods"},{"key":"ref_129","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_130","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_131","doi-asserted-by":"crossref","first-page":"227","DOI":"10.1016\/j.solener.2012.12.004","article-title":"Electrical power fluctuations in a network of DC\/AC inverters in a large PV plant: Relationship between correlation, distance and time scale","volume":"88","author":"Marcos","year":"2013","journal-title":"Sol. Energy"},{"key":"ref_132","doi-asserted-by":"crossref","first-page":"104001","DOI":"10.1088\/1748-9326\/10\/10\/104001","article-title":"Geographic smoothing of solar PV: Results from Gujarat","volume":"10","author":"Klima","year":"2015","journal-title":"Environ. Res. Lett."},{"key":"ref_133","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1561\/2700000006","article-title":"Spatial and Temporal Variability of Solar Energy","volume":"1","author":"Perez","year":"2016","journal-title":"Found. Trends\u00ae Renew. Energy"},{"key":"ref_134","doi-asserted-by":"crossref","first-page":"218","DOI":"10.1002\/pip.1016","article-title":"Power output fluctuations in large scale pv plants: One year observations with one second resolution and a derived analytic model: Power Output Fluctuations in Large Scale PV plants","volume":"19","author":"Marcos","year":"2011","journal-title":"Prog. Photovolt. Res. Appl."},{"key":"ref_135","unstructured":"Mills, A. (2019). Understanding Variability and Uncertainty of Photovoltaics for Integration with the Electric Power System, Lawrence Berkeley National Laboratory. Available online: https:\/\/escholarship.org\/uc\/item\/58z9s527."},{"key":"ref_136","doi-asserted-by":"crossref","first-page":"192","DOI":"10.1016\/j.solener.2013.12.028","article-title":"A Poisson model for anisotropic solar ramp rate correlations","volume":"101","author":"Kleissl","year":"2014","journal-title":"Sol. Energy"},{"key":"ref_137","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1016\/j.esd.2014.04.005","article-title":"Solar energy from Negev desert, Israel: Assessment of power fluctuations for future PV fleet","volume":"21","author":"Malachi","year":"2014","journal-title":"Energy Sustain. Dev."},{"key":"ref_138","doi-asserted-by":"crossref","first-page":"04021005","DOI":"10.1061\/(ASCE)IS.1943-555X.0000602","article-title":"Using Machine Learning to Examine Impact of Type of Performance Indicator on Flexible Pavement Deterioration Modeling","volume":"27","year":"2021","journal-title":"J. Infrastruct. Syst."},{"key":"ref_139","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\/Cr\u00e9teil, France.","DOI":"10.1109\/BioSMART54244.2021.9677566"},{"key":"ref_140","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1016\/j.solener.2016.04.011","article-title":"Nonparametric short-term probabilistic forecasting for solar radiation","volume":"133","author":"Grantham","year":"2016","journal-title":"Sol. Energy"},{"key":"ref_141","doi-asserted-by":"crossref","first-page":"e02692","DOI":"10.1016\/j.heliyon.2019.e02692","article-title":"Solar radiation forecasting using MARS, CART, M5, and random forest model: A case study for India","volume":"5","author":"Srivastava","year":"2019","journal-title":"Heliyon"},{"key":"ref_142","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.solener.2017.01.008","article-title":"Strong short-term non-linearity of solar irradiance fluctuations","volume":"144","author":"Madanchi","year":"2017","journal-title":"Sol. Energy"},{"key":"ref_143","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/j.solener.2006.03.001","article-title":"Fluctuations in instantaneous clearness index: Analysis and statistics","volume":"81","author":"Woyte","year":"2007","journal-title":"Sol. Energy"},{"key":"ref_144","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_145","doi-asserted-by":"crossref","first-page":"117061","DOI":"10.1016\/j.apenergy.2021.117061","article-title":"Long short term memory\u2013convolutional neural network based deep hybrid approach for solar irradiance forecasting","volume":"295","author":"Kumari","year":"2021","journal-title":"Appl. Energy"},{"key":"ref_146","doi-asserted-by":"crossref","first-page":"126617","DOI":"10.1016\/j.energy.2023.126617","article-title":"Long-term forecast of electrical energy consumption with considerations for solar and wind energy sources","volume":"268","author":"Kamani","year":"2023","journal-title":"Energy"},{"key":"ref_147","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1016\/0960-1481(92)90044-4","article-title":"Estimates of monthly average daily global solar radiation in Malaysia","volume":"2","author":"Sopian","year":"1992","journal-title":"Renew. Energy"},{"key":"ref_148","doi-asserted-by":"crossref","first-page":"101332","DOI":"10.1016\/j.esd.2023.101332","article-title":"Analysis of the socio-economic benefits of on-grid hybrid solar energy system on Bugala island in Uganda","volume":"77","author":"Kayima","year":"2023","journal-title":"Energy Sustain. Dev."},{"key":"ref_149","doi-asserted-by":"crossref","first-page":"495","DOI":"10.1016\/j.energy.2019.04.194","article-title":"Possibilities for wider investment in solar energy implementation","volume":"180","year":"2019","journal-title":"Energy"},{"key":"ref_150","first-page":"207","article-title":"Hybrid One-Step Block Fourth Derivative Method for the Direct Solution of Third Order Initial Value Problems of Ordinary Differential Equations","volume":"119","author":"Alkasassbeh","year":"2018","journal-title":"Int. J. Pure Appl. Math."},{"key":"ref_151","doi-asserted-by":"crossref","first-page":"110992","DOI":"10.1016\/j.rser.2021.110992","article-title":"A survey on deep learning methods for power load and renewable energy forecasting in smart microgrids","volume":"144","author":"Aslam","year":"2021","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_152","unstructured":"Dincer, I., Colpan, C.O., and Kizilkan, O. (2018). Chapter 1.8\u2014Comparison of ANN, Regression Analysis, and ANFIS Models in Estimation of Global Solar Radiation for Different Climatological Locations. Exergetic, Energetic and Environmental Dimensions, Academic Press."},{"key":"ref_153","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1016\/j.solener.2022.01.059","article-title":"Ramp-rate limiting strategies to alleviate the impact of PV power ramping on voltage fluctuations using energy storage systems","volume":"234","author":"Kumar","year":"2022","journal-title":"Sol. Energy"},{"key":"ref_154","doi-asserted-by":"crossref","first-page":"568","DOI":"10.1016\/j.apenergy.2016.01.130","article-title":"A wavelet-coupled support vector machine model for forecasting global incident solar radiation using limited meteorological dataset","volume":"168","author":"Deo","year":"2016","journal-title":"Appl. Energy"},{"key":"ref_155","doi-asserted-by":"crossref","first-page":"8625","DOI":"10.1007\/s00521-022-08160-x","article-title":"AI-based solar energy forecasting for smart grid integration","volume":"35","author":"Said","year":"2023","journal-title":"Neural Comput. Appl."},{"key":"ref_156","doi-asserted-by":"crossref","first-page":"2471","DOI":"10.1016\/S0196-8904(03)00004-9","article-title":"Estimation of horizontal diffuse solar radiation in Egypt","volume":"44","author":"Trabea","year":"2003","journal-title":"Energy Convers. Manag."},{"key":"ref_157","doi-asserted-by":"crossref","first-page":"495","DOI":"10.1016\/j.solener.2004.12.005","article-title":"Analysis of short-term solar radiation data","volume":"79","author":"Vijayakumar","year":"2005","journal-title":"Sol. Energy"},{"key":"ref_158","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/0196-8904(88)90050-7","article-title":"Measurement of solar energy radiation at Nsukka and the determination of the regression coefficients","volume":"28","author":"Awachie","year":"1988","journal-title":"Energy Convers. Manag."},{"key":"ref_159","doi-asserted-by":"crossref","first-page":"845","DOI":"10.5194\/wes-3-845-2018","article-title":"Assessing variability of wind speed: Comparison and validation of 27 methodologies","volume":"3","author":"Lee","year":"2018","journal-title":"Wind Energy Sci."},{"key":"ref_160","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_161","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.apenergy.2015.08.011","article-title":"An analog ensemble for short-term probabilistic solar power forecast","volume":"157","author":"Alessandrini","year":"2015","journal-title":"Appl. Energy"},{"key":"ref_162","doi-asserted-by":"crossref","first-page":"753","DOI":"10.1016\/j.solener.2017.10.036","article-title":"A virtual sky imager testbed for solar energy forecasting","volume":"158","author":"Kurtz","year":"2017","journal-title":"Sol. Energy"},{"key":"ref_163","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_164","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_165","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1016\/S0927-0248(00)00325-1","article-title":"Operation control of photovoltaic\/diesel hybrid generating system considering fluctuation of solar radiation","volume":"67","author":"Park","year":"2001","journal-title":"Sol. Energy Mater. Sol. Cells"},{"key":"ref_166","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1109\/MPE.2014.2379971","article-title":"Twilight of the Grids: The Impact of Distributed Solar on Germany?s Energy Transition","volume":"13","author":"Stetz","year":"2015","journal-title":"IEEE Power Energy Mag."},{"key":"ref_167","unstructured":"Suri, M., Huld, T., Dunlop, E., Albuisson, M., Lef\u00e8vre, M., and Wald, L. (2007, January 3\u20137). Uncertainties in solar electricity yield prediction from fluctuation of solar radiation. Proceedings of the 22nd European Photovoltaic Solar Energy Conference, Milan, Italy."},{"key":"ref_168","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/j.renene.2019.02.060","article-title":"Future changes, or lack thereof, in the temporal variability of the combined wind-plus-solar power production in Europe","volume":"139","author":"Jerez","year":"2019","journal-title":"Renew. Energy"},{"key":"ref_169","doi-asserted-by":"crossref","first-page":"289","DOI":"10.1016\/j.energy.2015.02.100","article-title":"Short-term solar irradiation forecasting based on Dynamic Harmonic Regression","volume":"84","author":"Trapero","year":"2015","journal-title":"Energy"},{"key":"ref_170","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/0038-092X(93)90086-4","article-title":"The frequency distribution of daily global irradiation at Kumasi","volume":"50","author":"Akuffo","year":"1993","journal-title":"Sol. Energy"},{"key":"ref_171","doi-asserted-by":"crossref","unstructured":"Tiba, C., Ramalho, R.D., de Souza, J.L., and da Silva, M.A.D.A. (2016). Variabilidade da Irradia\u00e7\u00e3o Solar em Escala de Minuto no Estado de Alagoas. An. Congr. Bras. De Energ. Sol. CBENS, 1\u20137.","DOI":"10.59627\/cbens.2016.1454"},{"key":"ref_172","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1186\/s42162-020-00109-5","article-title":"Topological considerations on peer-to-peer energy exchange and distributed energy generation in the smart grid","volume":"3","author":"Sha","year":"2020","journal-title":"Energy Inform."},{"key":"ref_173","doi-asserted-by":"crossref","first-page":"139","DOI":"10.5194\/adgeo-45-139-2018","article-title":"A stochastic model for the hourly solar radiation process for application in renewable resources management","volume":"45","author":"Koudouris","year":"2018","journal-title":"Adv. Geosci."},{"key":"ref_174","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1080\/00038628.1996.9697353","article-title":"Regression Analysis of Solar Radiation and Sunshine Duration","volume":"39","author":"Lam","year":"1996","journal-title":"Archit. Sci. Rev."},{"key":"ref_175","doi-asserted-by":"crossref","unstructured":"Thaker, J., and H\u00f6ller, R. (2022). A Comparative Study of Time Series Forecasting of Solar Energy Based on Irradiance Classification. Energies, 15.","DOI":"10.3390\/en15082837"},{"key":"ref_176","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_177","doi-asserted-by":"crossref","first-page":"117153","DOI":"10.1016\/j.applthermaleng.2021.117153","article-title":"Design and prediction method of dual working medium solar energy drying system","volume":"195","author":"Hao","year":"2021","journal-title":"Appl. Therm. Eng."},{"key":"ref_178","doi-asserted-by":"crossref","first-page":"895","DOI":"10.1016\/j.renene.2019.09.083","article-title":"Description of short circuit current of outdoor photovoltaic modules by multiple regression analysis under various solar irradiance levels","volume":"147","author":"Nakayama","year":"2020","journal-title":"Renew. Energy"},{"key":"ref_179","first-page":"586","article-title":"Effect of some factors on water distillation by solar energy","volume":"27","author":"Younis","year":"2010","journal-title":"Misr J. Agric. Eng."},{"key":"ref_180","doi-asserted-by":"crossref","first-page":"1111","DOI":"10.1016\/S0196-8904(02)00099-7","article-title":"Mathematical modeling of thin layer drying of pistachio by using solar energy","volume":"44","author":"Midilli","year":"2003","journal-title":"Energy Convers. Manag."},{"key":"ref_181","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1016\/j.egypro.2018.07.049","article-title":"First solar power plant in Latvia. Analysis of operational data","volume":"147","author":"Lauka","year":"2018","journal-title":"Energy Procedia"},{"key":"ref_182","doi-asserted-by":"crossref","first-page":"118988","DOI":"10.1016\/j.energy.2020.118988","article-title":"Analysis of factors influencing actual absorption of solar energy by building walls","volume":"215","author":"Li","year":"2021","journal-title":"Energy"},{"key":"ref_183","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.reseneeco.2015.07.003","article-title":"Consumers\u2019 willingness to pay for renewable energy: A meta-regression analysis","volume":"42","author":"Ma","year":"2015","journal-title":"Resour. Energy Econ."},{"key":"ref_184","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_185","doi-asserted-by":"crossref","first-page":"1263","DOI":"10.1016\/j.envsoft.2004.09.001","article-title":"Principal component and multiple regression analysis in modelling of ground-level ozone and factors affecting its concentrations","volume":"20","author":"Bakheit","year":"2005","journal-title":"Environ. Model. Softw."},{"key":"ref_186","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_187","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_188","doi-asserted-by":"crossref","unstructured":"Verma, T., Tiwana, A.P.S., Reddy, C.C., Arora, V., and Devanand, P. (2016, January 25\u201327). Data Analysis to Generate Models Based on Neural Network and Regression for Solar Power Generation Forecasting. Proceedings of the 2016 7th International Conference on Intelligent Systems, Modelling and Simulation (ISMS), Bangkok, Thailand.","DOI":"10.1109\/ISMS.2016.65"},{"key":"ref_189","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_190","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/S0038-092X(00)00053-0","article-title":"Atmospheric transparency, atmospheric turbidity and climatic parameters","volume":"69","author":"Rapti","year":"2000","journal-title":"Sol. Energy"},{"key":"ref_191","doi-asserted-by":"crossref","first-page":"62","DOI":"10.54287\/gujsa.1085005","article-title":"An Approach on Developing a Dynamic Wind-Solar Map for Tracking Electricity Production Potential and Energy Harvest","volume":"9","year":"2022","journal-title":"Gazi Univ. J. Sci. Part Eng. Innov."},{"key":"ref_192","unstructured":"Gomes, C.C.C., Torres, I.C., and Tiba, C. (2020). Taxas de rampas de irradi\u00e2ncia e pot\u00eancia. CIES2020-XVII Congresso Ib\u00e9rico e XIII Congresso Ibero-Americano de Energia Solar, LNEG\u2014Laborat\u00f3rio Nacional de Energia e Geologia."},{"key":"ref_193","doi-asserted-by":"crossref","first-page":"1471","DOI":"10.1016\/j.solmat.2010.12.014","article-title":"What limits the efficiency of chalcopyrite solar cells?","volume":"95","author":"Siebentritt","year":"2011","journal-title":"Sol. Energy Mater. Sol. Cells"},{"key":"ref_194","doi-asserted-by":"crossref","first-page":"507","DOI":"10.1109\/TEC.2014.2304951","article-title":"A Novel Approach for Ramp-Rate Control of Solar PV Using Energy Storage to Mitigate Output Fluctuations Caused by Cloud Passing","volume":"29","author":"Alam","year":"2014","journal-title":"IEEE Trans. Energy Convers."},{"key":"ref_195","doi-asserted-by":"crossref","first-page":"171","DOI":"10.5547\/01956574.42.1.thof","article-title":"Locational (In)Efficiency of Renewable Energy Feed-In into the Electricity Grid: A Spatial Regression Analysis","volume":"42","author":"Hofer","year":"2021","journal-title":"Energy J."},{"key":"ref_196","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1016\/j.renene.2015.03.035","article-title":"HARmonic\u2013LINear (HarLin) model for solar irradiation estimation","volume":"81","year":"2015","journal-title":"Renew. Energy"},{"key":"ref_197","doi-asserted-by":"crossref","first-page":"2558","DOI":"10.1049\/iet-rpg.2019.0223","article-title":"Voltage fluctuation mitigation: Fast allocation and daily local control of DSTATCOMs to increase solar energy harvest","volume":"13","author":"Mishra","year":"2019","journal-title":"IET Renew. Power Gener."},{"key":"ref_198","doi-asserted-by":"crossref","first-page":"3356","DOI":"10.1016\/j.solmat.2006.02.034","article-title":"An evaluation method of the fluctuation characteristics of photovoltaic systems by using frequency analysis","volume":"90","author":"Kawasaki","year":"2006","journal-title":"Sol. Energy Mater. Sol. Cells"},{"key":"ref_199","first-page":"54","article-title":"A Systematic Literature Review on big data for solar photovoltaic electricity generation forecasting","volume":"31","year":"2019","journal-title":"Sustain. Energy Technol. Assess."},{"key":"ref_200","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_201","doi-asserted-by":"crossref","first-page":"10111","DOI":"10.1002\/jgrd.50806","article-title":"Retrieval of aerosol optical depth under thin cirrus from MODIS: Application to an ocean algorithm","volume":"118","author":"Lee","year":"2013","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_202","unstructured":"UEM-Eduardo Mondlane University (2023, December 09). Department of Physics-Solar Energy Data Source [Online]. 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\/1460\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T16:54:48Z","timestamp":1760028888000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1996-1073\/18\/6\/1460"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,17]]},"references-count":202,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2025,3]]}},"alternative-id":["en18061460"],"URL":"https:\/\/doi.org\/10.3390\/en18061460","relation":{},"ISSN":["1996-1073"],"issn-type":[{"value":"1996-1073","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,17]]}}}