A Systematic Review on the Accessibility of Spatial and Temporal Variability of Solar Energy Availability on a Short Scale Measurement
DOI:
https://doi.org/10.54536/ajenr.v3i1.2430Keywords:
Accessibility, Solar, Spatio-Temporal, Mid-West, Variability, MozambiqueAbstract
The difficult access to energy in rural communities make up 80% of the world’s population, without access to electricity, strongly impels the need to establish the metric for access to this resource, to respond to the demand for inefficiency, fluctuations and instability that reduces output efficiency of a photovoltaic (PV) solar generator plant. The analysis of around 123 bibliographical sources accessed through the ePPI reviewer platform, focusing on the last 20 years, shows the majority to be applied to studies of small resolutions (one second) for hundreds of kilometers. One minute added to ten minutes of annual measurements is applied for thousands of interprovincial kilometers throughout Mid-western Mozambique. The potential is concluded as a source of spatial and temporal accessibility to the availability of solar energy is created.
Downloads
References
Adib, R. (2015). Renewables 2015 Global Status Report. Retrieved April 10, 2023, from https://www.taylorfrancis.com/books/mono/10.4324/9781849776981/applied-photovoltaics-alistair-sproul-richard-corkish-stuart-wenham-muriel-watt-martin-green.
Alharkan, H., Habib, S., & Islam, M. (2023). Solar Power Prediction Using Dual Stream CNN-LSTM Architecture. Sensors, 23(2), Article 2. https://doi.org/10.3390/s23020945
Almorox, J., Voyant, C., Bailek, N., Kuriqi, A., & Arnaldo, J. A. (2021). Total solar irradiance’s effect on the performance of empirical models for estimating global solar radiation: An empirical-based review. Energy, 236, 121486. https://doi.org/10.1016/j.energy.2021.121486
Amillo, A. G., Ntsangwane, L., Huld, T., & Trentmann, J. (2018). Comparison of satellite-retrieved high-resolution solar radiation datasets for South Africa. Journal of Energy in Southern Africa, 29(2). https://doi.org/10.17159/2413-3051/2017/v29i2a3376
Anenberg, S. C., Henze, D. K., Lacey, F., Irfan, A., Kinney, P., Kleiman, G., & Pillarisetti, A. (2017). Air pollution-related health and climate benefits of clean cookstove programs in Mozambique. Environmental Research Letters, 12(2), 025006. https://doi.org/10.1088/1748-9326/aa5557
Stuart R. Wenham, Martin A. Green, Muriel E. W. (2007), Applied Photovoltaics. Retrieved April 30, 2023, from https://www.taylorfrancis.com/books/mono/10.4324/9781849776981/applied-photovoltaics-alistair-sproul-richard-corkish-stuart-wenham-muriel-watt-martin-green
Arias-Castro, E., Kleissl, J., & Lave, M. (2014). A Poisson model for anisotropic solar ramp rate correlations. Solar Energy, 101, 192–202. https://doi.org/10.1016/j.solener.2013.12.028
Aryaputera, A. W., Yang, D., Zhao, L., & Walsh, W. M. (2015). Very short-term irradiance forecasting at unobserved locations using spatio-temporal kriging. Solar Energy, 122, 1266–1278. https://doi.org/10.1016/j.solener.2015.10.023
Assuno, H. F., Escobedo, J. F., & Oliveira, A. P. (2003). Modelling frequency distributions of 5 minute-averaged solar radiation indexes using Beta probability functions. Theoretical and Applied Climatology, 75(3–4), 213–224. https://doi.org/10.1007/s00704-003-0733-9
Ayet, A., & Tandeo, P. (2018). Nowcasting solar irradiance using an analog method and geostationary satellite images. Solar Energy, 164, 301–315. https://doi.org/10.1016/j.solener.2018.02.068
Bailek, N., Bouchouicha, K., Abdel-Hadi, Y. A., El-Shimy, M., Slimani, A., Jamil, B., & Djaafari, A. (2020). Developing a new model for predicting global solar radiation on a horizontal surface located in Southwest Region of Algeria. NRIAG Journal of Astronomy and Geophysics, 9(1), 341–349. https://doi.org/10.1080/20909977.2020.1746892
Barry, J., Munzke, N., & Thomas, J. (2017). Power fluctuations in solar-storage clusters: Spatial correlation and battery response times. Energy Procedia, 135, 379–390. https://doi.org/10.1016/j.egypro.2017.09.516
Charabi, Y., & Gastli, A. (2012). Spatio-temporal assessment of dust risk maps for solar energy systems using proxy data. Renewable Energy, 44, 23–31. https://doi.org/10.1016/j.renene.2011.12.005
Chen, W.-H., Cheng, L.-S., Chang, Z.-P., Zhou, H.-T., Yao, Q.-F., Peng, Z.-M., Fu, L.-Q., & Chen, Z.-X. (2022). Interval Prediction of Photovoltaic Power Using Improved NARX Network and Density Peak Clustering Based on Kernel Mahalanobis Distance. Complexity, 2022, 1–22. https://doi.org/10.1155/2022/8169510
Ciampi, M., Leccese, F., & Tuoni, G. (2015). Energy efficiency in buildings: A parameter for the thermal qualification of opaque building envelope.
Come Zebra, E. I., Mahumane, G., Canu, F. A., & Cardoso, A. (2021). Assessing the Greenhouse Gas Impact of a Renewable Energy Feed-in Tariff Policy in Mozambique: Towards NDC Ambition and Recommendations to Effectively Measure, Report, and Verify Its Implementation. Sustainability, 13(10), 5376. https://doi.org/10.3390/su13105376
Dambreville, R., Blanc, P., Chanussot, J., & Boldo, D. (2014). Very short term forecasting of the Global Horizontal Irradiance using a spatio-temporal autoregressive model. Renewable Energy, 72, 291–300. https://doi.org/10.1016/j.renene.2014.07.012
Dantas, P. V. S. (2015). Análise e dimensionamento de um sistema fotovoltaico com diferentes tecnologias no estágio de potência.
de Souza, A., Ihaddadene, R., Fernandes, W., & Abreu, M. C. (2012). Modeling of the Global Solar Radiation Series as a Function of Probability Distribution.
Di Fonzo, T., & Girolimetto, D. (2023). Spatio-temporal reconciliation of solar forecasts. Solar Energy, 251, 13–29. https://doi.org/10.1016/j.solener.2023.01.003
Duffie, J. A., & Beckman, W. A. (1991). Solar engineering of thermal processes. Wiley.
Gueymard, C. A., & Wilcox, S. M. (2011). Assessment of spatial and temporal variability in the US solar resource from radiometric measurements and predictions from models using ground-based or satellite data. Solar Energy, 85(5), 1068–1084. https://doi.org/10.1016/j.solener.2011.02.030
Gueymard, C., & Ruiz-Arias, J. A. (2015). Performance of Separation Models to Predict Direct Irradiance at High Frequency: Validation over Arid Areas. Proceedings of the EuroSun 2014 Conference, 1–10. https://doi.org/10.18086/eurosun.2014.08.06
Habte, A., Sengupta, M., Gueymard, C., Golnas, A., & Xie, Y. (2020). Long-term spatial and temporal solar resource variability over America using the NSRDB version 3 (1998–2017). Renewable and Sustainable Energy Reviews, 134, 110285. https://doi.org/10.1016/j.rser.2020.110285
Haegel, N. M., Margolis, R., Buonassisi, T., Feldman, D., Froitzheim, A., Garabedian, R., Green, M., Glunz, S., Henning, H.-M., Holder, B., Kaizuka, I., Kroposki, B., Matsubara, K., Niki, S., Sakurai, K., Schindler, R. A., Tumas, W., Weber, E. R., Wilson, G., … Kurtz, S. (2017). Terawatt-scale photovoltaics: Trajectories and challenges. Science, 356(6334), 141–143. https://doi.org/10.1126/science.aal1288
Hassan, M. A., Bailek, N., Bouchouicha, K., Ibrahim, A., Jamil, B., Kuriqi, A., Nwokolo, S. C., & El-kenawy, E.-S. M. (2022). Evaluation of energy extraction of PV systems affected by environmental factors under real outdoor conditions. Theoretical and Applied Climatology, 150(1–2), 715–729. https://doi.org/10.1007/s00704-022-04166-6
Hoff, T. E., & Perez, R. (2011). PV Power Output Variability: Calculation of Correlation Coefficients Using Satellite Insolation Data.
Hoff, T. E., & Perez, R. (2010). Quantifying PV power Output Variability. Solar Energy, 84(10), 1782–1793. https://doi.org/10.1016/j.solener.2010.07.003
Hummon, M., Ibanez, E., Brinkman, G., & Lew, D. (2012). Sub-hour solar data for power system modeling from static spatial variability analysis: Preprint (NREL/CP-6A20-56204). National Renewable Energy Laboratory. https://www.osti.gov/biblio/1059579
Iban˜ez, M., Beckman, W. A., & Klein, S. A. (2002). Frequency Distributions for Hourly and Daily Clearness Indices. Journal of Solar Energy Engineering, 124(1), 28–33. https://doi.org/10.1115/1.1445443
International Finance Corporation. (2015). A project developer’s guide to utility-scale solar photovoltaic power plants. Washington, DC: Author. Retrieved December 11, 2023, from https://cdn.who.int/media/docs/default-source/air-pollution-documents/air-quality-and-hf?sfvrsn=669
Iqbal, M. (1983). An introduction to solar radiation. Academic Press.
Jain, A., Yamujala, S., Gaur, A., Das, P., Bhakar, R., & Mathur, J. (2023). Power sector decarbonization planning considering renewable resource variability and system operational constraints. Applied Energy, 331, 120404. https://doi.org/10.1016/j.apenergy.2022.120404
Jerez, S., Tobin, I., Turco, M., Jiménez-Guerrero, P., Vautard, R., & Montávez, J. P. (2019). Future changes, or lack thereof, in the temporal variability of the combined wind-plus-solar power production in Europe. Renewable Energy, 139, 251–260. https://doi.org/10.1016/j.renene.2019.02.060
Keeratimahat, K. (2020). Characterising short-term variability, uncertainty and controllability of utility photovoltaics and their implications for integrating high renewables penetrations.
Keeratimahat, K., Bruce, A. G., & MacGill, I. (2017). Short term variability of utility-scale PV in the Australian National Electricity Market.
Klein, S. A. (1977). Calculation of monthly average insolation on tilted surfaces. Solar Energy, 19(4), 325–329. https://doi.org/10.1016/0038-092X(77)90001-9
Klima, K., & Apt, J. (2015). Geographic smoothing of solar PV: Results from Gujarat. Environmental Research Letters, 10(10), 104001. https://doi.org/10.1088/1748-9326/10/10/104001
Kong, X., Du, X., Xu, Z., & Xue, G. (2023). Predicting solar radiation for space heating with thermal storage system based on temporal convolutional network-attention model. Applied Thermal Engineering, 219, 119574. https://doi.org/10.1016/j.applthermaleng.2022.119574
Koudouris, G., Dimitriadis, P., Iliopoulou, T., Mamassis, N., & Koutsoyiannis, D. (2018). A stochastic model for the hourly solar radiation process for application in renewable resources management. Advances in Geosciences, 45, 139–145. https://doi.org/10.5194/adgeo-45-139-2018
Kreuwel, F. P. M., Knap, W. H., Visser, L. R., Van Sark, W. G. J. H. M., Vilà-Guerau De Arellano, J., & Van Heerwaarden, C. C. (2020). Analysis of high frequency photovoltaic solar energy fluctuations. Solar Energy, 206, 381–389. https://doi.org/10.1016/j.solener.2020.05.093
Kühnert, J., Lorenz, E., & Heinemann, D. (2013). Satellite-Based Irradiance and Power Forecasting for the German Energy Market. In Solar Energy Forecasting and Resource Assessment (pp. 267–297). Elsevier. https://doi.org/10.1016/B978-0-12-397177-7.00011-5
Kumar, D. (2019). Hyper-temporal variability analysis of solar insolation with respect to local seasons. Remote Sensing Applications: Society and Environment, 15, 100241. https://doi.org/10.1016/j.rsase.2019.100241
Kumar, D. (2021). Spatial variability analysis of the solar energy resources for future urban energy applications using Meteosat satellite-derived datasets. Remote Sensing Applications: Society and Environment, 22, 100481. https://doi.org/10.1016/j.rsase.2021.100481
Lan, H., Yin, H., Hong, Y.-Y., Wen, S., Yu, D. C., & Cheng, P. (2018). Day-ahead spatio-temporal forecasting of solar irradiation along a navigation route. Applied Energy, 211, 15–27. https://doi.org/10.1016/j.apenergy.2017.11.014
Lave, M., & Kleissl, J. (2013). Cloud speed impact on solar variability scaling – Application to the wavelet variability model. Solar Energy, 91, 11–21. https://doi.org/10.1016/j.solener.2013.01.023
Lave, M., Kleissl, J., & Arias-Castro, E. (2012). High-frequency irradiance fluctuations and geographic smoothing. Solar Energy, 86(8), 2190–2199. https://doi.org/10.1016/j.solener.2011.06.031
Lefèvre, M., Oumbe, A., Blanc, P., Espinar, B., Gschwind, B., Qu, Z., Wald, L., Schroedter-Homscheidt, M., Hoyer-Klick, C., Arola, A., Benedetti, A., Kaiser, J. W., & Morcrette, J.-J. (2013). McClear: A new model estimating downwelling solar radiation at ground level in clear-sky conditions. Atmospheric Measurement Techniques, 6(9), 2403–2418. https://doi.org/10.5194/amt-6-2403-2013
Litjens, G. B. M. A., Kausika, B. B., Worrell, E., & van Sark, W. G. J. H. M. (2018). A spatio-temporal city-scale assessment of residential photovoltaic power integration scenarios. Solar Energy, 174, 1185–1197. https://doi.org/10.1016/j.solener.2018.09.055
Liu, Y., Xiao, L., Wang, H., Dai, S., & Qi, Z. (2013). Analysis on the hourly spatiotemporal complementarities between China’s solar and wind energy resources spreading in a wide area. Science China Technological Sciences, 56(3), 683–692. https://doi.org/10.1007/s11431-012-5105-1
Lohmann, G. M. (2018). Irradiance Variability Quantification and Small-Scale Averaging in Space and Time: A Short Review.
Lohmann, G. M., & Monahan, A. H. (2017). Effects of temporal averaging on short-term irradiance variability under mixed sky conditions [Preprint]. Atmospheric Measurement Techniques. https://doi.org/10.5194/amt-2017-309
Lohmann, G. M., & Monahan, A. H. (2018). Effects of temporal averaging on short-term irradiance variability under mixed sky conditions. Atmospheric Measurement Techniques, 11(5), 3131–3144. https://doi.org/10.5194/amt-11-3131-2018
Lohmann, G. M., Monahan, A. H., & Heinemann, D. (2016). Local short-term variability in solar irradiance.
Lorenzo, A. T. (2017). Short-term irradiance forecasting using an irradiance monitoring network, satellite imagery, and data assimilation.
Lozano, I. L., Sánchez-Hernández, G., Guerrero-Rascado, J. L., Alados, I., & Foyo-Moreno, I. (2022). Analysis of cloud effects on long-term global and diffuse photosynthetically active radiation at a Mediterranean site. Atmospheric Research, 268, 106010. https://doi.org/10.1016/j.atmosres.2021.106010
Lucaciu, S., Blaga, R., Stefu, N., & Paulescu, M. (2016). Quantification of the Solar Radiative Regime Variability Based on the Clearness Index. Annals of West University of Timisoara - Physics, 59(1), 13–17. https://doi.org/10.1515/awutp-2016-0003
Marcos, J., Marroyo, L., Lorenzo, E., Alvira, D., & Izco, E. (2011). 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. Progress in Photovoltaics: Research and Applications, 19(2), 218–227. https://doi.org/10.1002/pip.1016
Mazumdar, B. M., Saquib, Mohd., & Das, A. K. (2014). An empirical model for ramp analysis of utility-scale solar PV power. Solar Energy, 107, 44–49. https://doi.org/10.1016/j.solener.2014.05.027
Miller, S. D., Rogers, M. A., Haynes, J. M., Sengupta, M., & Heidinger, A. K. (2018). Short-term solar irradiance forecasting via satellite/model coupling. Solar Energy, 168, 102–117. https://doi.org/10.1016/j.solener.2017.11.049
Mills, A. (2011). Understanding Variability and Uncertainty of Photovoltaics for Integration with the Electric Power System
Mol, W. B., van Stratum, B. J. H., Knap, W. H., & van Heerwaarden, C. C. (2023). Reconciling Observations of Solar Irradiance Variability With Cloud Size Distributions. Journal of Geophysical Research: Atmospheres, 128(5), e2022JD037894. https://doi.org/10.1029/2022JD037894
Monjoly, S., André, M., Calif, R., & Soubdhan, T. (2019). Forecast Horizon and Solar Variability Influences on the Performances of Multiscale Hybrid Forecast Model. Energies, 12(12), 2264. https://doi.org/10.3390/en12122264
Mucomole, F. V., Silva, C. A. S., & Magaia, L. L. (2023). Temporal Variability of Solar Energy Availability in the Conditions of the Southern Region of Mozambique. American Journal of Energy and Natural Resources, 2(1), Article 1. https://doi.org/10.54536/ajenr.v2i1.1311
Nam, S., & Hur, J. (2019). A hybrid spatio-temporal forecasting of solar generating resources for grid integration. Energy, 177, 503–510. https://doi.org/10.1016/j.energy.2019.04.127
Neggers, R. A. J., Jonker, H. J. J., & Siebesma, A. P. (2003). Size Statistics of Cumulus Cloud Populations in Large-Eddy Simulations. Journal of the Atmospheric Sciences, 60(8), 1060–1074. https://doi.org/10.1175/1520-0469(2003)60<1060:SSOCCP>2.0.CO;2
Nwokolo, S. C., Obiwulu, A. U., & Ogbulezie, J. C. (2023). Machine learning and analytical model hybridization to assess the impact of climate change on solar PV energy production. Physics and Chemistry of the Earth, Parts A/B/C, 130, 103389. https://doi.org/10.1016/j.pce.2023.103389
Nwokolo, S. C., Obiwulu, A. U., Ogbulezie, J. C., & Amadi, S. O. (2022). Hybridization of statistical machine learning and numerical models for improving beam, diffuse and global solar radiation prediction. Cleaner Engineering and Technology, 9, 100529. https://doi.org/10.1016/j.clet.2022.100529
Obiwulu, A. U., Erusiafe, N., Olopade, M. A., & Nwokolo, S. C. (2022). Modeling and estimation of the optimal tilt angle, maximum incident solar radiation, and global radiation index of the photovoltaic system. Heliyon, 8(6), e09598. https://doi.org/10.1016/j.heliyon.2022.e09598
Ohtake, H., Shimose, K., Fonseca, J. G. D. S., Takashima, T., Oozeki, T., & Yamada, Y. (2013). Accuracy of the solar irradiance forecasts of the Japan Meteorological Agency mesoscale model for the Kanto region, Japan. Solar Energy, 98, 138–152. https://doi.org/10.1016/j.solener.2012.10.007
Perez, M. J. R., & Fthenakis, V. M. (2015). On the spatial decorrelation of stochastic solar resource variability at long timescales. Solar Energy, 117, 46–58. https://doi.org/10.1016/j.solener.2015.04.020
Perez, R., David, M., Hoff, T. E., Jamaly, M., Kivalov, S., Kleissl, J., Lauret, P., & Perez, M. (2016). Spatial and Temporal Variability of Solar Energy. Foundations and Trends® in Renewable Energy, 1(1), 1–44. https://doi.org/10.1561/2700000006
Perez, R., Kivalov, S., Schlemmer, J., Hemker, K., & Hoff, T. E. (2012). Short-term irradiance variability: Preliminary estimation of station pair correlation as a function of distance. Solar Energy, 86(8), 2170–2176. https://doi.org/10.1016/j.solener.2012.02.027
Perez, R., Lauret, P., Perez, M., David, M., Hoff, T. E., & Kivalov, S. (2018). Solar Resource Variability. In R. Perez (Ed.), Wind Field and Solar Radiation Characterization and Forecasting: A Numerical Approach for Complex Terrain (pp. 149–170). Springer International Publishing. https://doi.org/10.1007/978-3-319-76876-2_7
Perez, R., Rábago, K. R., Trahan, M., Rawlings, L., Norris, B., Hoff, T., Putnam, M., & Perez, M. (2016). Achieving very high PV penetration – The need for an effective electricity remuneration framework and a central role for grid operators. Energy Policy, 96, 27–35. https://doi.org/10.1016/j.enpol.2016.05.016
Perpiñán, O., & Lorenzo, E. (2011). Analysis and synthesis of the variability of irradiance and PV power time series with the wavelet transform. Solar Energy, 85(1), 188–197. https://doi.org/10.1016/j.solener.2010.08.013
Rapti, A. S. (2000). Atmospheric transparency, atmospheric turbidity and climatic parameters. Solar Energy, 69(2), 99–111. https://doi.org/10.1016/S0038-092X(00)00053-0
Roversi, K., & Rampinelli, G. A. (2020). Análise do fator de dimensionamento do inversor em um sistema fotovoltaico conectado à rede.
Salmanoğlu, F., & Çeti̇N, N. S. (2022). An Approach on Developing a Dynamic Wind-Solar Map for Tracking Electricity Production Potential and Energy Harvest. Gazi University Journal of Science Part A: Engineering and Innovation, 62–78. https://doi.org/10.54287/gujsa.1085005
International Energy Agency (IEA), International Renewable Energy Agency (IRENA), United Nations Statistics Division (UNSD), World Bank, & World Health Organization (WHO). (2023). Tracking SDG 7: The energy progress report. World Bank. 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
Sengupta, M., Kurtz, S., Dobos, A., Wilbert, S., Lorenz, E., Renné, D., Myers, D., Wilcox, S., Blanc, P., & Perez, R. (2015). Best Practices Handbook for the Collection and Use of Solar Resource Data for Solar Energy Applications. IEA Solar Heating and Cooling Programme. https://doi.org/10.18777/ieashc-task46-2015-0001
Sha, A., & Aiello, M. (2018). Topological Considerations on Decentralised Energy Exchange in the Smart Grid. Procedia Computer Science, 130, 720–727. https://doi.org/10.1016/j.procs.2018.04.126
Sha, A., & Aiello, M. (2020). Topological considerations on peer-to-peer energy exchange and distributed energy generation in the smart grid. Energy Informatics, 3(1), 8. https://doi.org/10.1186/s42162-020-00109-5
Shakirov, V. (2019). An analysis of wind and solar power variability to assess its implications for power grid. EPJ Web of Conferences, 217, 01019. https://doi.org/10.1051/epjconf/201921701019
Shen, P., Zhao, S., Ma, Y., & Liu, S. (2023). Urbanization-induced Earth’s surface energy alteration and warming: A global spatiotemporal analysis. Remote Sensing of Environment, 284, 113361. https://doi.org/10.1016/j.rse.2022.113361
Energypedia. (2023). Situação de acesso à energia em Moçambique. Retrieved December 7, 2023, from https://energypedia.info/wiki/Situa%C3%A7%C3%A3o_de_Acesso_%C3%A0_Energia_em_Mo%C3%A7ambique
Sørensen, M. L., Nystrup, P., Bjerregård, M. B., Møller, J. K., Bacher, P., & Madsen, H. (2023). Recent developments in multivariate wind and solar power forecasting. WIREs Energy and Environment, 12(2), e465. https://doi.org/10.1002/wene.465
Stetz, T., Von Appen, J., Niedermeyer, F., Scheibner, G., Sikora, R., & Braun, M. (2015). Twilight of the Grids: The Impact of Distributed Solar on Germany?s Energy Transition. IEEE Power and Energy Magazine, 13(2), 50–61. https://doi.org/10.1109/MPE.2014.2379971
Suri, M., Huld, T., Dunlop, E., Albuisson, M., Lefèvre, M., & Wald, L. (2007). Uncertainties in solar electricity yield prediction from fluctuation of solar radiation.
Tapia, M., Heinemann, D., Ballari, D., & Zondervan, E. (2022). Spatio-temporal characterization of long-term solar resource using spatial functional data analysis: Understanding the variability and complementarity of global horizontal irradiance in Ecuador. Renewable Energy, 189, 1176–1193. https://doi.org/10.1016/j.renene.2022.03.049
Tovar, J., Olmo, F. J., Batlles, F. J., & Alados-Arboledas, L. (2001). Dependence of one-minute global irradiance probability density distributions on hourly irradiation. Energy, 26(7), 659–668. https://doi.org/10.1016/S0360-5442(01)00024-X
Uti, M. N., Md Din, A. H., Yusof, N., & Yaakob, O. (2023). A spatial-temporal clustering for low ocean renewable energy resources using K-means clustering. Renewable Energy, 219, 119549. https://doi.org/10.1016/j.renene.2023.119549
Van Haaren, R., Morjaria, M., & Fthenakis, V. (2014). Empirical assessment of short-term variability from utility-scale solar PV plants: Assessment of variability from utility-scale solar PV plants. Progress in Photovoltaics: Research and Applications, 22(5), 548–559. https://doi.org/10.1002/pip.2302
Verbois, H., Saint-Drenan, Y.-M., Libois, Q., Michel, Y., Cassas, M., Dubus, L., & Blanc, P. (2023). Improvement of satellite-derived surface solar irradiance estimations using spatio-temporal extrapolation with statistical learning. Solar Energy, 258, 175–193. https://doi.org/10.1016/j.solener.2023.04.037
Vijayakumar, G. (2004). Assessment of Solar Radiation Data Used In Analyses of Solar Energy Systems.
Vindel, J. M., Valenzuela, R. X., Navarro, A. A., & Polo, J. (2020). Temporal and spatial variability analysis of the solar radiation in a region affected by the intertropical convergence zone. Meteorological Applications, 27(1), e1824. https://doi.org/10.1002/met.1824
Wilcox, S., Blvd, C., Gueymard, C. A., & Box, P. O. (2016). Spatial and temporal variability of the solar resource in the United States
Wilson, P., & Tanaka, O. K. (2018). Estatística: Conceitos básicos [Basic concepts]. Retrieved April 10, 2023, from https://www.estantevirtual.com.br/livros/wilson-pereira-oswaldo-k-tanaka/estatistica-conceitos-basicos/189548989
Wu, J., Niu, Z., Li, X., Huang, L., Nielsen, P. S., & Liu, X. (2023). Understanding multi-scale spatiotemporal energy consumption data: A visual analysis approach. Energy, 263, 125939. https://doi.org/10.1016/j.energy.2022.125939
Xia, Y., Xu, Q., Fang, J., & Li, F. (2023). Non-iterative decentralized peer-to-peer market clearing in multi-microgrid systems via model substitution and network reduction. IEEE Transactions on Power Systems. Advance online publication. https://doi.org/10.1109/TPWRS.2023.3301447
Xu, Y., Dong, Z., & Wu, Y. (2023). The spatiotemporal effects of environmental regulation on green innovation: Evidence from Chinese cities. Science of The Total Environment, 876, 162790. https://doi.org/10.1016/j.scitotenv.2023.162790
Yan, M., Shahidehpour, M., Paaso, A., Zhang, L., Alabdulwahab, A., & Abusorrah, A. (2020). Distribution network-constrained optimization of peer-to-peer transactive energy trading among multi-microgrids. IEEE Transactions on Smart Grid. Advance online publication. https://doi.org/10.1109/TSG.2020.3032889
Yang, C., & Xie, L. (2012). A novel ARX-based multi-scale spatio-temporal solar power forecast model. 2012 North American Power Symposium (NAPS), 1–6. https://doi.org/10.1109/NAPS.2012.6336383
Yang, D., Dong, Z., Lim, L. H. I., & Liu, L. (2017). Analyzing big time series data in solar engineering using features and PCA. Solar Energy, 153, 317–328. https://doi.org/10.1016/j.solener.2017.05.072
Yu, B., Liu, H., Wu, J., & Lin, W.-M. (2009). Investigating impacts of urban morphology on spatio-temporal variations of solar radiation with airborne LIDAR data and a solar flux model: A case study of downtown Houston. International Journal of Remote Sensing, 30(17), 4359–4385. https://doi.org/10.1080/01431160802555846
Yu, L., Zhang, M., Wang, L., Lu, Y., & Li, J. (2021). Effects of aerosols and water vapour on spatial-temporal variations of the clear-sky surface solar radiation in China. Atmospheric Research, 248, 105162. https://doi.org/10.1016/j.atmosres.2020.105162
Zervos, A., & Lins, C. (2016). Renewables 2016 Global Status Report. REN21.
Zhang, S., & Yan, Y. (2022). Thermal performance of latent heat energy storage system with/without enhancement under solar fluctuation for Organic Rankine power cycle. Energy Conversion and Management, 270, 116276. https://doi.org/10.1016/j.enconman.2022.116276
Zhang, Y., Shen, Y., Xia, X., & Shi, G. (2018). Validation of GFS day-ahead solar irradiance forecasts in China.
Zheng, J., Du, J., Wang, B., Klemeš, J. J., Liao, Q., & Liang, Y. (2023). A hybrid framework for forecasting power generation of multiple renewable energy sources. Renewable and Sustainable Energy Reviews, 172, 113046. https://doi.org/10.1016/j.rser.2022.113046
Zhou, X., Huang, Z., Scheuer, B., Lu, W., Zhou, G., & Liu, Y. (2023). High-resolution spatial assessment of the zero energy potential of buildings with photovoltaic systems at the city level. Sustainable Cities and Society, 93, 104526. https://doi.org/10.1016/j.scs.2023.104526
Zhou, Y., Meng, X., Belle, J. H., Zhang, H., Kennedy, C., Al-Hamdan, M. Z., Wang, J., & Liu, Y. (2019). Compilation and spatio-temporal analysis of publicly available total solar and UV irradiance data in the contiguous United States. Environmental Pollution, 253, 130–140. https://doi.org/10.1016/j.envpol.2019.06.074
Zhu, W., Wu, B., Yan, N., Ma, Z., Wang, L., Liu, W., Xing, Q., & Xu, J. (2019). Estimating Sunshine Duration Using Hourly Total Cloud Amount Data from a Geostationary Meteorological Satellite. Atmosphere, 11(1), 26. https://doi.org/10.3390/atmos11010026
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Fernando V. Mucomole, Carlos A. S. Silva, Lourenço L. Magaia
This work is licensed under a Creative Commons Attribution 4.0 International License.