{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,7]],"date-time":"2026-02-07T09:08:43Z","timestamp":1770455323034,"version":"3.49.0"},"reference-count":49,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2020,11,12]],"date-time":"2020-11-12T00:00:00Z","timestamp":1605139200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100010193","name":"Korea Electric Power Corporation","doi-asserted-by":"publisher","award":["R19XO01-04"],"award-info":[{"award-number":["R19XO01-04"]}],"id":[{"id":"10.13039\/501100010193","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Precise and accurate prediction of solar photovoltaic (PV) generation plays a major role in developing plans for the supply and demand of power grid systems. Most previous studies on the prediction of solar PV generation employed only weather data composed of numerical text data. The numerical text weather data can reflect temporal factors, however, they cannot consider the movement features related to the wind direction of the spatial characteristics, which include the amount of both clouds and particulate matter (PM) among other weather features. This study aims developing a hybrid spatio-temporal prediction model by combining general weather data and data extracted from satellite images having spatial characteristics. A model for hourly prediction of solar PV generation is proposed using data collected from a solar PV power plant in Incheon, South Korea. To evaluate the performance of the prediction model, we compared and performed ARIMAX analysis, which is a traditional statistical time-series analysis method, and SVR, ANN, and DNN, which are based on machine learning algorithms. The models that reflect the temporal and spatial characteristics exhibited better performance than those using only the general weather numerical data or the satellite image data.<\/jats:p>","DOI":"10.3390\/rs12223706","type":"journal-article","created":{"date-parts":[[2020,11,12]],"date-time":"2020-11-12T10:00:32Z","timestamp":1605175232000},"page":"3706","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["A Hybrid Spatio-Temporal Prediction Model for Solar Photovoltaic Generation Using Numerical Weather Data and Satellite Images"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6257-5526","authenticated-orcid":false,"given":"Bowoo","family":"Kim","sequence":"first","affiliation":[{"name":"Department of Convergence &amp; Fusion System Engineering, Kyungpook National University, Sangju 37224, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7201-0521","authenticated-orcid":false,"given":"Dongjun","family":"Suh","sequence":"additional","affiliation":[{"name":"Department of Convergence &amp; Fusion System Engineering, Kyungpook National University, Sangju 37224, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,11,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1038\/479267b","article-title":"Climate change","volume":"479","author":"Cullen","year":"2011","journal-title":"Nature"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"740","DOI":"10.1017\/S0020782900004253","article-title":"Paris Agreement","volume":"55","author":"Horowitz","year":"2016","journal-title":"Int. Leg. Mater."},{"key":"ref_3","unstructured":"Ministry of Trade, Industry and Energy (2017). Renewable Energy 3020 Plan. 3020 Plan."},{"key":"ref_4","unstructured":"Korea Ministry of Trade, Industry and Energy (2017, December 17). Renewable Energy Statistics 2013, Available online: http:\/\/www.motie.go.kr."},{"key":"ref_5","unstructured":"Choi, H., Zhao, W., Ciobotaru, M., and Agelidis, V.G. (2012, January 25\u201328). Large-scale PV system based on the multiphase isolated DC\/DC converter. Proceedings of the 2012 3rd IEEE International Symposium on Power Electronics for Distributed Generation Systems (PEDG), Aalborg, Denmark."},{"key":"ref_6","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 anda derived analytic model","volume":"19","author":"Javier","year":"2011","journal-title":"Prog. Photovolt."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"387","DOI":"10.5394\/KINPR.2011.35.5.387","article-title":"Predict Solar Radiation According to Weather Report","volume":"35","author":"Won","year":"2011","journal-title":"J. Korean Navig. Port Res."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"944","DOI":"10.1109\/SURV.2011.101911.00087","article-title":"Smart Grid\u2014The New and Improved Power Grid: A Survey","volume":"14","author":"Fang","year":"2011","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_9","first-page":"935","article-title":"Hourly Solar Irradiance Prediction Based on Support Vector Machine and Its Error Analysis","volume":"32","author":"Bae","year":"2016","journal-title":"IEEE Trans. Power Syst."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1064","DOI":"10.1109\/TIA.2012.2190816","article-title":"Forecasting Power Output of Photovoltaic Systems Based on Weather Classification and Support Vector Machines","volume":"48","author":"Shi","year":"2012","journal-title":"IEEE Trans. Ind. Appl."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"917","DOI":"10.1109\/TSTE.2014.2313600","article-title":"A Weather-Based Hybrid Method for 1-Day Ahead Hourly Forecasting of PV Power Output","volume":"5","author":"Yang","year":"2014","journal-title":"IEEE Trans. Sustain. Energy"},{"key":"ref_12","first-page":"1","article-title":"Deep Learning Based Prediction Method of Long-term Photovoltaic Power Generation Using Meteorological and Seasonal Information","volume":"24","author":"Lee","year":"2019","journal-title":"Soc. e-Bus. Stud."},{"key":"ref_13","first-page":"175","article-title":"Forecasting of 24_hours Ahead Photovoltaic Power Output Using Support Vector Regression","volume":"14","author":"Lee","year":"2016","journal-title":"J. Korean Inst. Inf. Technol."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1255","DOI":"10.1109\/TSTE.2016.2535466","article-title":"Solar Power Prediction Based on Satellite Images and Support Vector Machine","volume":"7","author":"Jang","year":"2016","journal-title":"IEEE Trans. Sustain. Energy"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Jang, H.S., Bae, K.Y., Park, H.-S., and Sung, D.K. (2015, January 2\u20135). Effect of aggregation for multi-site photovoltaic (PV) farms. Proceedings of the 2015 IEEE International Conference on Smart Grid Communications (SmartGridComm), Miami, FL, USA.","DOI":"10.1109\/SmartGridComm.2015.7436370"},{"key":"ref_16","first-page":"411","article-title":"Short-Term Forecasting of Solar Radiation","volume":"67","author":"Hammer","year":"2000","journal-title":"1999 ISES Sol. World Congr."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Peng, Z., Yoo, S., Yu, D., and Huang, D. (2013, January 21\u201324). Solar irradiance forecast system based on geostationary satellite. Proceedings of the 2013 IEEE International Conference on Smart Grid Commun. SmartGridComm, Vancouver, BC, Canada.","DOI":"10.1109\/SmartGridComm.2013.6688042"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Kim, I.-J., and Lee, S.-K. (2019). A Study on the Design of Testable CAM using MTA Code. Trans. Korean Inst. Electr. Eng., 106\u2013111.","DOI":"10.5370\/KIEEP.2019.68.2.106"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"268","DOI":"10.1109\/TSTE.2018.2832634","article-title":"A Solar Time Based Analog Ensemble Method for Regional Solar Power Forecasting","volume":"10","author":"Zhang","year":"2018","journal-title":"IEEE Trans. Sustain. Energy"},{"key":"ref_20","first-page":"327","article-title":"An Analysis of the Causes of Fine Dust in Korea Considering Spatial Correlation","volume":"28","author":"Kang","year":"2019","journal-title":"Environ. Resour. Econ. Rev."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"3043","DOI":"10.1039\/C8EE01100A","article-title":"Urban haze and photovoltaics","volume":"11","author":"Peters","year":"2018","journal-title":"Energy Environ. Sci."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"735","DOI":"10.1016\/j.rser.2014.08.068","article-title":"Effect of dust pollutant type on photovoltaic performance","volume":"41","author":"Darwish","year":"2015","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Maghami, M.R., Hizam, H., Gomes, C., Hajighorbani, S., and Rezaei, N. (2015). Evaluation of the 2013 Southeast Asian Haze on Solar Generation Performance. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0135118"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"698","DOI":"10.1016\/j.rser.2012.12.065","article-title":"A comprehensive review of the impact of dust on the use of solar energy: History, investigations, results, literature, and mitigation approaches","volume":"22","author":"Sarver","year":"2013","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1021\/acs.estlett.7b00197","article-title":"Large Reductions in Solar Energy Production Due to Dust and Particulate Air Pollution","volume":"4","author":"Bergin","year":"2017","journal-title":"Environ. Sci. Technol. Lett."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"585","DOI":"10.1016\/0960-1481(93)90064-N","article-title":"Degradation of photovoltaic cell performance due to dust deposition on to its surface","volume":"3","author":"Hussein","year":"1993","journal-title":"Renew. Energy"},{"key":"ref_27","unstructured":"Korea Meteorolgical Administration (2020, October 22). Available online: https:\/\/data.kma.go.kr\/."},{"key":"ref_28","unstructured":"Air Korea (2020, October 22). Available online: https:\/\/www.airkorea.or.kr\/."},{"key":"ref_29","unstructured":"(2020, October 22). Open Data Portal, Available online: https:\/\/www.data.go.kr\/."},{"key":"ref_30","unstructured":"National Meteorological Satellite Center (2020, October 22). Available online: https:\/\/nmsc.kma.go.kr\/."},{"key":"ref_31","unstructured":"National Meteorological Satellite Center (2012). AMV (AMV: Atmospheric Motion Vector) Algorithm Theoretical Basis Document."},{"key":"ref_32","unstructured":"National Meteorological Satellite Center (2012). COT Algorithm Theoretical Basis Document."},{"key":"ref_33","unstructured":"National Meteorological Satellite Center (2012). AOD Algorithm Theoretical Basis Document."},{"key":"ref_34","unstructured":"National Meteorological Satellite Center (2012). INS Algorithm Theoretical Basis Document."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"197","DOI":"10.1016\/j.solener.2016.05.051","article-title":"Comparing support vector regression for PV power forecasting to a physical modeling approach using measurement, numerical weather prediction, and cloud motion data","volume":"135","author":"Wolff","year":"2016","journal-title":"Sol. Energy"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Li, L., Gong, J., and Zhou, J. (2014). Spatial Interpolation of Fine Particulate Matter Concentrations Using the Shortest Wind-Field Path Distance. PLoS ONE, 9.","DOI":"10.1371\/journal.pone.0096111"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Newsham, G.R., and Birt, B.J. (2010, January 3\u20135). Building-level occupancy data to improve ARIMA-based electricity use forecasts. Proceedings of the 2nd ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Building, Zurich, Switzerland.","DOI":"10.1145\/1878431.1878435"},{"key":"ref_38","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","year":"2019","journal-title":"Korean Sol. Energy Soc."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"104","DOI":"10.1016\/j.energy.2013.03.005","article-title":"Tools for PV (photovoltaic) plant operators: Nowcasting of passing clouds","volume":"54","author":"Paulescu","year":"2013","journal-title":"Energy"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Alsharif, M.H., Younes, M.K., and Kim, J. (2019). Time Series ARIMA Model for Prediction of Daily and Monthly Average Global Solar Radiation: The Case Study of Seoul, South Korea. Symmetry, 11.","DOI":"10.3390\/sym11020240"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Liu, W., Liu, C., Lin, Y., Ma, L., Xiong, F., and Li, J. (2018). Ultra-Short-Term Forecast of Photovoltaic Output Power under Fog and Haze Weather. Energies, 11.","DOI":"10.3390\/en11030528"},{"key":"ref_43","unstructured":"Kim, K., and Jin, H. (2018, January 11\u201313). Photovoltaic Power Forecasting and Analysis of Forecasting Error for Model Learning Periods Using SVR. Proceedings of the Korean Institute of Electrical Engineers, Pyeongchang, Korea."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1016\/S0092-8240(05)80006-0","article-title":"A logical calculus of the ideas immanent in nervous activity","volume":"52","author":"McCulloch","year":"1990","journal-title":"Bull. Math. Biol."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2014\/469701","article-title":"Modelling and Prediction of Photovoltaic Power Output Using Artificial Neural Networks","volume":"2014","author":"Saberian","year":"2014","journal-title":"Int. J. Photoenergy"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"1623","DOI":"10.1109\/TNN.2006.880582","article-title":"Estimating the Number of Hidden Neurons in a Feedforward Network Using the Singular Value Decomposition","volume":"17","author":"Teoh","year":"2006","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"11","DOI":"10.1016\/j.neucom.2016.12.038","article-title":"A survey of deep neural network architectures and their applications","volume":"234","author":"Liu","year":"2017","journal-title":"Neurocomputing"},{"key":"ref_48","unstructured":"Oh, I.-S. (2017). Machine Learning, HANBIT Academy, Inc."},{"key":"ref_49","unstructured":"ANSI\/ASHRAE (2002). ASHRAE Guideline 14-2002 Measurement of Energy and Demand Savings. ASHRAE, 8400, 170."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/22\/3706\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:32:19Z","timestamp":1760178739000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/22\/3706"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,11,12]]},"references-count":49,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2020,11]]}},"alternative-id":["rs12223706"],"URL":"https:\/\/doi.org\/10.3390\/rs12223706","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,11,12]]}}}