{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,2]],"date-time":"2025-10-02T00:15:33Z","timestamp":1759364133222,"version":"build-2065373602"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer Nature Switzerland","isbn-type":[{"value":"9783032027276","type":"print"},{"value":"9783032027283","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T00:00:00Z","timestamp":1759276800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T00:00:00Z","timestamp":1759276800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026]]},"DOI":"10.1007\/978-3-032-02728-3_16","type":"book-chapter","created":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T00:25:50Z","timestamp":1759278350000},"page":"191-204","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Improving PV Power Prediction Based on\u00a0GRU and\u00a0Meteorological Factors"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9993-7095","authenticated-orcid":false,"given":"Myriam","family":"Cumbajin","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9849-5712","authenticated-orcid":false,"given":"Ruxandra","family":"Stoean","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3954-3646","authenticated-orcid":false,"given":"Jos\u00e9","family":"Aguado","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9256-0870","authenticated-orcid":false,"given":"Gonzalo","family":"Joya","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,10,1]]},"reference":[{"key":"16_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.chaos.2021.110861","volume":"146","author":"K ArunKumar","year":"2021","unstructured":"ArunKumar, K., Kalaga, D.V., Kumar, C.M.S., Kawaji, M., Brenza, T.M.: Forecasting of Covid-19 using deep layer recurrent neural networks (RNNs) with gated recurrent units (GRUs) and long short-term memory (LSTM) cells. Chaos Solitons Fractals 146, 110861 (2021)","journal-title":"Chaos Solitons Fractals"},{"issue":"2","key":"16_CR2","first-page":"49","volume":"2","author":"BT Chicho","year":"2021","unstructured":"Chicho, B.T., Sallow, A.B.: A comprehensive survey of deep learning models based on Keras framework. J. Soft Comput. Data Min. 2(2), 49\u201362 (2021)","journal-title":"J. Soft Comput. Data Min."},{"key":"16_CR3","series-title":"Lecture Notes in Networks and Systems","doi-asserted-by":"publisher","first-page":"26","DOI":"10.1007\/978-3-030-94262-5_3","volume-title":"Sustainability, Energy and City","author":"M Cumbajin","year":"2022","unstructured":"Cumbajin, M., Stoean, R., Aguado, J., Joya, G.: Hybrid deep learning architecture approach for\u00a0photovoltaic power plant output prediction. In: Chauvin, M.I.A., Botto-Tobar, M., D\u00edaz Cadena, A., Montes Le\u00f3n, S. (eds.) CSECity 2021. LNNS, vol. 379, pp. 26\u201337. Springer, Cham (2022). https:\/\/doi.org\/10.1007\/978-3-030-94262-5_3"},{"key":"16_CR4","doi-asserted-by":"crossref","unstructured":"Fairuzabadi, M., Kusrini, K., Utami, E., Setyanto, A.: Advancements and challenges in gated recurrent units (GRU) for text classification: a systematic literature review. In: 2024 7th International Conference of Computer and Informatics Engineering (IC2IE), pp.\u00a01\u20137. IEEE (2024)","DOI":"10.1109\/IC2IE63342.2024.10748229"},{"key":"16_CR5","doi-asserted-by":"publisher","first-page":"786","DOI":"10.1016\/j.egyr.2023.05.208","volume":"9","author":"X Guo","year":"2023","unstructured":"Guo, X., Zhan, Y., Zheng, D., Li, L., Qi, Q.: Research on short-term forecasting method of photovoltaic power generation based on clustering so-GRU method. Energy Rep. 9, 786\u2013793 (2023)","journal-title":"Energy Rep."},{"key":"16_CR6","doi-asserted-by":"publisher","first-page":"105939","DOI":"10.1109\/ACCESS.2021.3099169","volume":"9","author":"P Jia","year":"2021","unstructured":"Jia, P., Zhang, H., Liu, X., Gong, X.: Short-term photovoltaic power forecasting based on VMD and ISSA-GRU. IEEE Access 9, 105939\u2013105950 (2021)","journal-title":"IEEE Access"},{"key":"16_CR7","doi-asserted-by":"publisher","first-page":"673","DOI":"10.1007\/978-981-15-0291-0_92","volume-title":"APAC 2019","author":"X-H Le","year":"2020","unstructured":"Le, X.-H., Ho, H.V., Lee, G.: Application of gated recurrent unit (GRU) network for forecasting river water levels affected by tides. In: APAC 2019, pp. 673\u2013680. Springer, Singapore (2020). https:\/\/doi.org\/10.1007\/978-981-15-0291-0_92"},{"issue":"22","key":"16_CR8","doi-asserted-by":"publisher","first-page":"6121","DOI":"10.3390\/en13226121","volume":"13","author":"X Li","year":"2020","unstructured":"Li, X., Ma, X., Xiao, F., Wang, F., Zhang, S.: Application of gated recurrent unit (GRU) neural network for smart batch production prediction. Energies 13(22), 6121 (2020)","journal-title":"Energies"},{"key":"16_CR9","doi-asserted-by":"publisher","unstructured":"Mahjoub, S., Chrifi-Alaoui, L., Marhic, B., Delahoche, L.: Predicting energy consumption using LSTM, multi-layer GRU and drop-GRU neural networks. Sensors 22(11) (2022). https:\/\/doi.org\/10.3390\/s22114062","DOI":"10.3390\/s22114062"},{"key":"16_CR10","unstructured":"Pfenninger, S., Staffell, I., Jansen, M.: Renewables. ninja-a model for the global output of weather-dependent renewable energy sources. In: EMS Annual Meeting Abstracts, vol.\u00a015 (2018)"},{"key":"16_CR11","doi-asserted-by":"crossref","unstructured":"Rajamani, S.K., Iyer, R.S.: Machine learning-based mobile applications using python and scikit-learn. In: Designing and Developing Innovative Mobile Applications, pp. 282\u2013306. IGI Global (2023)","DOI":"10.4018\/978-1-6684-8582-8.ch016"},{"issue":"5","key":"16_CR12","doi-asserted-by":"publisher","first-page":"1256","DOI":"10.3390\/en17051256","volume":"17","author":"A Raudys","year":"2024","unstructured":"Raudys, A., Gaidukevi\u010dius, J.: Forecasting solar energy generation and household energy usage for efficient utilisation. Energies 17(5), 1256 (2024)","journal-title":"Energies"},{"issue":"3","key":"16_CR13","doi-asserted-by":"publisher","first-page":"6303","DOI":"10.1080\/15567036.2022.2097751","volume":"44","author":"NM Sabri","year":"2022","unstructured":"Sabri, N.M., El Hassouni, M.: Accurate photovoltaic power prediction models based on deep convolutional neural networks and gated recurrent units. Energy Sour. Part A: Recov. Utilization Environ. Effects 44(3), 6303\u20136320 (2022)","journal-title":"Energy Sour. Part A: Recov. Utilization Environ. Effects"},{"key":"16_CR14","doi-asserted-by":"publisher","first-page":"895","DOI":"10.1016\/j.procs.2018.04.298","volume":"131","author":"G Shen","year":"2018","unstructured":"Shen, G., Tan, Q., Zhang, H., Zeng, P., Xu, J.: Deep learning with gated recurrent unit networks for financial sequence predictions. Procedia Comput. Sci. 131, 895\u2013903 (2018)","journal-title":"Procedia Comput. Sci."},{"key":"16_CR15","doi-asserted-by":"crossref","unstructured":"Souhe, F.G.Y., Mbey, C.F., Kakeu, V.J.F., Meyo, A.E., Boum, A.T.: Optimized forecasting of photovoltaic power generation using hybrid deep learning model based on GRU and SVM. Electr. Eng. 1\u201320 (2024)","DOI":"10.1007\/s00202-024-02492-8"},{"issue":"9","key":"16_CR16","doi-asserted-by":"publisher","DOI":"10.1016\/j.heliyon.2024.e30002","volume":"10","author":"CM Travieso-Gonz\u00e1lez","year":"2024","unstructured":"Travieso-Gonz\u00e1lez, C.M., Celada-Bernal, S., Lomoschitz, A., Cabrera-Quintero, F.: Analysis of variables to determine their influence on renewable energy forecasting using ensemble methods. Heliyon 10(9), e30002 (2024). https:\/\/doi.org\/10.1016\/j.heliyon.2024.e30002","journal-title":"Heliyon"},{"issue":"8","key":"16_CR17","doi-asserted-by":"publisher","first-page":"2163","DOI":"10.3390\/en11082163","volume":"11","author":"Y Wang","year":"2018","unstructured":"Wang, Y., Liao, W., Chang, Y.: Gated recurrent unit network-based short-term photovoltaic forecasting. Energies 11(8), 2163 (2018)","journal-title":"Energies"},{"issue":"21","key":"16_CR18","doi-asserted-by":"publisher","first-page":"4055","DOI":"10.3390\/en12214055","volume":"12","author":"J Wojtkiewicz","year":"2019","unstructured":"Wojtkiewicz, J., Hosseini, M., Gottumukkala, R., Chambers, T.: Hour-ahead solar irradiance forecasting using multivariate gated recurrent units. Energies 12(21), 4055 (2019)","journal-title":"Energies"},{"key":"16_CR19","doi-asserted-by":"publisher","first-page":"7050","DOI":"10.1109\/TII.2021.3056867","volume":"17","author":"M Xia","year":"2021","unstructured":"Xia, M., Shao, H., Ma, X., de Silva, C.: A stacked GRU-RNN-based approach for predicting renewable energy and electricity load for smart grid operation. IEEE Trans. Ind. Inform. 17, 7050\u20137059 (2021). https:\/\/doi.org\/10.1109\/TII.2021.3056867","journal-title":"IEEE Trans. Ind. Inform."},{"issue":"10","key":"16_CR20","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0285410","volume":"18","author":"A Zameer","year":"2023","unstructured":"Zameer, A., Jaffar, F., Shahid, F., Muneeb, M., Khan, R., Nasir, R.: Short-term solar energy forecasting: integrated computational intelligence of LSTMs and GRU. PLoS ONE 18(10), e0285410 (2023)","journal-title":"PLoS ONE"},{"issue":"7","key":"16_CR21","doi-asserted-by":"publisher","first-page":"3142","DOI":"10.1002\/ese3.1811","volume":"12","author":"J Zhang","year":"2024","unstructured":"Zhang, J., Liao, Z., Shu, J., Yue, J., Liu, Z., Tao, R.: Interval prediction of short-term photovoltaic power based on an improved GRU model. Energy Sci. Eng. 12(7), 3142\u20133156 (2024)","journal-title":"Energy Sci. Eng."},{"key":"16_CR22","doi-asserted-by":"publisher","unstructured":"Zulqarnain, M., Ishak, S.A., Ghazali, R., Nawi, N.M., Aamir, M., Hassim, Y.M.M.: An improved deep learning approach based on variant two-state gated recurrent unit and word embeddings for sentiment classification. Int. J. Adv. Comput. Sci. Appl. 11(1) (2020). https:\/\/doi.org\/10.14569\/IJACSA.2020.0110174","DOI":"10.14569\/IJACSA.2020.0110174"}],"container-title":["Lecture Notes in Computer Science","Advances in Computational Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-032-02728-3_16","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T00:25:53Z","timestamp":1759278353000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-032-02728-3_16"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10,1]]},"ISBN":["9783032027276","9783032027283"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-032-02728-3_16","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,10,1]]},"assertion":[{"value":"1 October 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IWANN","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Work-Conference on Artificial Neural Networks","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"A Coru\u00f1a","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Spain","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2025","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 June 2025","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 June 2025","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iwann2025","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/iwann.uma.es\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}