{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,7]],"date-time":"2026-01-07T08:11:29Z","timestamp":1767773489693,"version":"build-2065373602"},"reference-count":49,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2021,7,2]],"date-time":"2021-07-02T00:00:00Z","timestamp":1625184000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100007053","name":"Korea Institute of Energy Technology Evaluation and Planning","doi-asserted-by":"publisher","award":["20194010000040"],"award-info":[{"award-number":["20194010000040"]}],"id":[{"id":"10.13039\/501100007053","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010193","name":"Korea Electric Power Corporation","doi-asserted-by":"publisher","award":["R21XO01-36"],"award-info":[{"award-number":["R21XO01-36"]}],"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>Currently, the world is actively responding to climate change problems. There is significant research interest in renewable energy generation, with focused attention on solar photovoltaic (PV) generation. Therefore, this study developed an accurate and precise solar PV generation prediction model for several solar PV power plants in various regions of South Korea to establish stable supply-and-demand power grid systems. To reflect the spatial and temporal characteristics of solar PV generation, data extracted from satellite images and numerical text data were combined and used. Experiments were conducted on solar PV power plants in Incheon, Busan, and Yeongam, and various machine learning algorithms were applied, including the SARIMAX, which is a traditional statistical time-series analysis method. Furthermore, for developing a precise solar PV generation prediction model, the SARIMAX-LSTM model was applied using a stacking ensemble technique that created one prediction model by combining the advantages of several prediction models. Consequently, an advanced multisite hybrid spatio-temporal solar PV generation prediction model with superior performance was proposed using information that could not be learned in the existing single-site solar PV generation prediction model.<\/jats:p>","DOI":"10.3390\/rs13132605","type":"journal-article","created":{"date-parts":[[2021,7,2]],"date-time":"2021-07-02T10:06:34Z","timestamp":1625220394000},"page":"2605","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":35,"title":["A Novel Hybrid Spatio-Temporal Forecasting of Multisite Solar Photovoltaic Generation"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6257-5526","authenticated-orcid":false,"given":"Bowoo","family":"Kim","sequence":"first","affiliation":[{"name":"Department of Convergence & Fusion System Engineering, Kyungpook Nation University, Sangju 37224, Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7201-0521","authenticated-orcid":false,"given":"Dongjun","family":"Suh","sequence":"additional","affiliation":[{"name":"Department of Convergence & Fusion System Engineering, Kyungpook Nation University, Sangju 37224, Korea"}]},{"given":"Marc-Oliver","family":"Otto","sequence":"additional","affiliation":[{"name":"Department of Mathematics, Natural and Economic Science, Ulm University of Applied Science, Prittwitzstr, 10, 89075 Ulm, Germany"}]},{"given":"Jeung-Soo","family":"Huh","sequence":"additional","affiliation":[{"name":"Department of Convergence & Fusion System Engineering, Kyungpook Nation University, Sangju 37224, Korea"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"797","DOI":"10.1016\/j.enpol.2012.10.046","article-title":"Depletion of fossil fuels and anthropogenic climate change\u2014A review","volume":"52","author":"Tang","year":"2013","journal-title":"Energy Policy"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"267","DOI":"10.1038\/479267b","article-title":"Climate change","volume":"479","author":"Horowitz","year":"2011","journal-title":"Nature"},{"key":"ref_3","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_4","unstructured":"IRENA (2017). Energy and Renewable Energy 3020 Plan, IEA."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1126\/science.aal1288","article-title":"Terawatt-scale photovoltaics: Trajectories and challenges","volume":"356","author":"Haegel","year":"2017","journal-title":"Science"},{"key":"ref_6","unstructured":"(2021, May 09). Renewable Energy Statistics. Korea Ministry of Trade, Industry and Energy, Available online: http:\/\/www.motie.go.kr."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"443","DOI":"10.1016\/j.rser.2012.09.028","article-title":"Progress in solar PV technology: Research and achievement","volume":"20","author":"Tyagi","year":"2013","journal-title":"Renew. Sustain. Energy Rev."},{"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":"2012","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_9","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_10","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_11","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_12","doi-asserted-by":"crossref","first-page":"1307","DOI":"10.1016\/j.rser.2016.01.044","article-title":"Power loss due to soiling on solar panel: A review","volume":"59","author":"Maghami","year":"2016","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_13","first-page":"241","article-title":"Neural network based estimation of maximum power generation from PV module using environmental information","volume":"17","author":"Hiyama","year":"1997","journal-title":"IEEE Power Eng. Rev."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"660","DOI":"10.1016\/j.enbuild.2012.08.011","article-title":"Short-term prediction of photovoltaic energy generation by intelligent approach","volume":"55","author":"Chow","year":"2012","journal-title":"Energy Build."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"700","DOI":"10.1016\/j.apenergy.2018.06.112","article-title":"Prediction of short-term PV power output and uncertainty analysis","volume":"228","author":"Liu","year":"2018","journal-title":"Appl. Energy"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"832","DOI":"10.1109\/JPHOTOV.2019.2898521","article-title":"Prediction model for PV performance with correlation analysis of environmental variables","volume":"9","author":"Kim","year":"2019","journal-title":"IEEE J. Photovoltaics"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Monfared, M., Fazeli, M., Lewis, R., and Searle, J. (2020, January 12\u201313). Day-ahead prediction of pv generation using weather forecast data: A case study in the UK. Proceedings of the 2nd Intetnational Conference on Electrical, Communication and Computer Engineering (ICECCE), Istanbul, Turkey.","DOI":"10.1109\/ICECCE49384.2020.9179454"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Dev, S., Savoy, F.M., Lee, Y.H., and Winkler, S. (2016, January 22\u201325). Short-term prediction of localized cloud motion using ground-based sky imagers. Proceedings of the 2016 IEEE Region 10 Conference (TENCON), Singapore.","DOI":"10.1109\/TENCON.2016.7848499"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1016\/j.renene.2016.12.023","article-title":"Cloud tracking using clusters of feature points for accurate solar irradiance nowcasting","volume":"104","author":"Cheng","year":"2017","journal-title":"Renew. Energy"},{"key":"ref_20","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_21","doi-asserted-by":"crossref","first-page":"2881","DOI":"10.1016\/j.solener.2011.08.025","article-title":"Intra-hour forecasting with a total sky imager at the UC San Diego solar energy testbed","volume":"85","author":"Chow","year":"2011","journal-title":"Sol. Energy"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1007\/s11063-018-09969-1","article-title":"Machine learning nowcasting of PV energy using satellite data","volume":"52","author":"Catalina","year":"2020","journal-title":"Neural Process. Lett."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Kim, B., and Suh, D. (2020). A Hybrid spatio-temporal prediction model for solar photovoltaic generation using numerical weather data and satellite images. Remote Sens., 12.","DOI":"10.3390\/rs12223706"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Khandakar, A., Chowdhury, M.E.H., Kazi, M.-K., Benhmed, K., Touati, F., Al-Hitmi, M., and Gonzales, A.J.S.P. (2019). Machine learning based photovoltaics (PV) power prediction using different environmental parameters of Qatar. Energies, 12.","DOI":"10.3390\/en12142782"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Preda, S., Oprea, S.-V., B\u00e2ra, A., and Belciu, A. (2018). PV Forecasting using support vector machine learning in a big data analytics context. Symmetry, 10.","DOI":"10.3390\/sym10120748"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1016\/j.energy.2018.08.207","article-title":"Tree-based ensemble methods for predicting PV power generation and their comparison with support vector regression","volume":"164","author":"Ahmad","year":"2018","journal-title":"Energy"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Vagropoulos, S.I., Chouliaras, G.I., Kardakos, E.G., Simoglou, C.K., and Bakirtzis, A.G. (2016, January 4\u20138). Comparison of SARIMAX, SARIMA, modified SARIMA and ANN-based models for short-term PV generation forecasting. Proceedings of the 2016 IEEE International Energy Conference (ENERGYCON), Leuven, Belgium.","DOI":"10.1109\/ENERGYCON.2016.7514029"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Gensler, A., Henze, J., Sick, B., and Raabe, N. (2017, January 9\u201312). Deep Learning for Solar Power Forecasting\u2014An Approach Using AutoEncoder and LSTM Neural Networks. Proceedings of the 2016 IEEE International Conference on Systems, Man and Cybernetics (SMC 2016), Budapest, Hungary.","DOI":"10.1109\/SMC.2016.7844673"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1281","DOI":"10.1049\/iet-rpg.2016.1036","article-title":"Takagi\u2013Sugeno fuzzy model-based approach considering multiple weather factors for the photovoltaic power short-term forecasting","volume":"11","author":"Liu","year":"2017","journal-title":"IET Renew. Power Gener."},{"key":"ref_30","unstructured":"National Meteorogical Satellite Center (2021, May 09). Available online: https:\/\/nmsc.kma.go.kr\/."},{"key":"ref_31","unstructured":"N.M.S. Center (2012). Atmospheric Motion Vector Algorithm Theoretical Basis."},{"key":"ref_32","unstructured":"N.M.S. Center (2012). COT Algorithm Theoretical Basis Document."},{"key":"ref_33","unstructured":"N.M.S. Center (2012). AOD Algorithm Theoretical Basis Document."},{"key":"ref_34","unstructured":"N.M.S. Center (2012). INS Algorithm Theoretical Basis Document."},{"key":"ref_35","unstructured":"Korea Meteorolgical Administration (2021, May 09). Available online: https:\/\/data.kma.go.kr\/."},{"key":"ref_36","unstructured":"Air Korea (2021, May 09). Available online: https:\/\/www.airkorea.or.kr\/."},{"key":"ref_37","unstructured":"Open Data Portal (2021, May 09). Available online: https:\/\/www.data.go.kr\/."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Newsham, G.R., and Birt, B.J. (2010, January 2). Building-level occupancy data to improve ARIMA-based electricity use forecasts. Proceedings of the 2nd ACM Workshop Embedded Sensing Systems Energy-Efficiency in Building, Zurich, Switzerland.","DOI":"10.1145\/1878431.1878435"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Sheng, F., and Jia, L. (2020, January 12\u201314). Short-term load forecasting based on SARIMAX-LSTM. Proceedings of the 5th International Conference on Power Renewable Energy (ICPRE), Shanghai, China.","DOI":"10.1109\/ICPRE51194.2020.9233117"},{"key":"ref_40","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_41","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1016\/S1364-0321(01)00006-5","article-title":"Artificial neural networks in renewable energy systems applications: A review","volume":"5","author":"Kalogirou","year":"2000","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_42","first-page":"160","article-title":"Supervised sequence labelling with recurrent neural neural networks","volume":"1999","author":"Biehl","year":"2005","journal-title":"Neural Netw."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"2222","DOI":"10.1109\/TNNLS.2016.2582924","article-title":"LSTM: A search space odyssey","volume":"28","author":"Greff","year":"2017","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_44","first-page":"69","article-title":"Random forest based approach for concept drift handling","volume":"661","author":"Zhukov","year":"2017","journal-title":"Commun. Comput. Inf. Sci."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1545","DOI":"10.1162\/neco.1997.9.7.1545","article-title":"Shape quantization and recognition with randomized trees","volume":"9","author":"Amit","year":"1997","journal-title":"Neural Comput."},{"key":"ref_46","first-page":"5","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Random For."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"488","DOI":"10.3744\/SNAK.2019.56.6.488","article-title":"A study on the work-time estimation for block erections using stacking ensemble learning","volume":"56","author":"Kwon","year":"2019","journal-title":"J. Soc. Nav. Archit. Korea"},{"key":"ref_48","first-page":"1","article-title":"A new ensemble machine learning technique with multiple stacking","volume":"25","author":"Lee","year":"2020","journal-title":"J. Soc. E-Bus. Stud."},{"key":"ref_49","unstructured":"ANSI\/ASHRAE (2002). ASHRAE Guideline 14-2002 Measurement of Energy and Demand Savings, Available online: http:\/\/www.eeperformance.org\/uploads\/8\/6\/5\/0\/8650231\/ashrae_guideline_14-2002_measurement_of_energy_and_demand_saving.pdf."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/13\/2605\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T06:25:25Z","timestamp":1760163925000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/13\/2605"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,2]]},"references-count":49,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2021,7]]}},"alternative-id":["rs13132605"],"URL":"https:\/\/doi.org\/10.3390\/rs13132605","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2021,7,2]]}}}