{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,10]],"date-time":"2026-01-10T03:55:26Z","timestamp":1768017326266,"version":"3.49.0"},"publisher-location":"Singapore","reference-count":26,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819642069","type":"print"},{"value":"9789819642076","type":"electronic"}],"license":[{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"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":[[2025]]},"DOI":"10.1007\/978-981-96-4207-6_55","type":"book-chapter","created":{"date-parts":[[2025,4,2]],"date-time":"2025-04-02T18:34:57Z","timestamp":1743618897000},"page":"613-625","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Multiscale Global-Local Transformer for\u00a0Long-Sequence PV Power Generation Forecasting"],"prefix":"10.1007","author":[{"given":"Tian-Yang","family":"Deng","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wen-Li","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Feng","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Qiang","family":"Hua","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chun-Ru","family":"Dong","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Boon Han","family":"Lim","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,4,1]]},"reference":[{"key":"55_CR1","doi-asserted-by":"crossref","first-page":"894","DOI":"10.1016\/j.rser.2017.09.094","volume":"82","author":"E Kabir","year":"2018","unstructured":"Kabir, E., Kumar, P., Kumar, S., Adelodun, A.A., Kim, K.H.: Solar energy: potential and future prospects. Renew. Sustain. Energy Rev. 82, 894\u2013900 (2018)","journal-title":"Renew. Sustain. Energy Rev."},{"issue":"1","key":"55_CR2","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1016\/j.rser.2009.07.015","volume":"14","author":"MA Eltawil","year":"2010","unstructured":"Eltawil, M.A., Zhao, Z.: Grid-connected photovoltaic power systems: technical and potential problems-a review. Renew. Sustain. Energy Rev. 14(1), 112\u2013129 (2010)","journal-title":"Renew. Sustain. Energy Rev."},{"key":"55_CR3","doi-asserted-by":"publisher","unstructured":"Ahmed, R., Sreeram, V., Mishra, Y., Arif, M.: A review and evaluation of the state-of-the-art in PV solar power forecasting: techniques and optimization. Renew. Sustain. Energy Rev. 109792 (2020). https:\/\/doi.org\/10.1016\/j.rser.2020.109792","DOI":"10.1016\/j.rser.2020.109792"},{"key":"55_CR4","doi-asserted-by":"crossref","unstructured":"Lorenz, E., Scheidsteger, T., Hurka, J., Heinemann, D., Kurz, C.: Regional PV power prediction for improved grid integration. Progress Photovoltaics (2010)","DOI":"10.1002\/pip.1033"},{"key":"55_CR5","volume":"198","author":"W Gu","year":"2019","unstructured":"Gu, W., Ma, T., Song, A., Li, M., Shen, L.: Mathematical modelling and performance evaluation of a hybrid photovoltaic-thermoelectric system. Energy Convers. Manag. 198, 111800 (2019)","journal-title":"Energy Convers. Manag."},{"key":"55_CR6","doi-asserted-by":"crossref","DOI":"10.1016\/j.apenergy.2020.116239","volume":"283","author":"MJ Mayer","year":"2021","unstructured":"Mayer, M.J., Gr\u00f3f, G.: Extensive comparison of physical models for photovoltaic power forecasting. Appl. Energy 283, 116239 (2021)","journal-title":"Appl. Energy"},{"key":"55_CR7","doi-asserted-by":"publisher","unstructured":"Crisosto, C., Hofmann, M., Mubarak, R., Seckmeyer, G.: One-hour prediction of the global solar irradiance from all-sky images using artificial neural networks. Energies 2906 (2018). https:\/\/doi.org\/10.3390\/en11112906","DOI":"10.3390\/en11112906"},{"key":"55_CR8","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.jastp.2013.10.017","volume":"107","author":"J Vindel","year":"2014","unstructured":"Vindel, J., Polo, J.: Markov processes and Zipf\u2019s law in daily solar irradiation at earth\u2019s surface. J. Atmos. Solar Terr. Phys. 107, 42\u201347 (2014)","journal-title":"J. Atmos. Solar Terr. Phys."},{"key":"55_CR9","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/j.solener.2016.03.064","volume":"133","author":"M David","year":"2016","unstructured":"David, M., Ramahatana, F., Trombe, P.J., Lauret, P.: Probabilistic forecasting of the solar irradiance with recursive ARMA and GARCH models. Sol. Energy 133, 55\u201372 (2016)","journal-title":"Sol. Energy"},{"key":"55_CR10","doi-asserted-by":"publisher","unstructured":"Sharadga, H., Hajimirza, S., Balog, R.S.: Time series forecasting of solar power generation for large-scale photovoltaic plants. Renew. Energy 797\u2013807 (2020). https:\/\/doi.org\/10.1016\/j.renene.2019.12.131","DOI":"10.1016\/j.renene.2019.12.131"},{"key":"55_CR11","doi-asserted-by":"crossref","unstructured":"Li, Z., Zang, C., Zeng, P., Yu, H., Li, H.: Day-ahead hourly photovoltaic generation forecasting using extreme learning machine. In: 2015 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER), pp. 779\u2013783. IEEE (2015)","DOI":"10.1109\/CYBER.2015.7288041"},{"key":"55_CR12","doi-asserted-by":"crossref","first-page":"446","DOI":"10.1016\/j.solener.2014.12.014","volume":"112","author":"P Lauret","year":"2015","unstructured":"Lauret, P., Voyant, C., Soubdhan, T., David, M., Poggi, P.: A benchmarking of machine learning techniques for solar radiation forecasting in an insular context. Sol. Energy 112, 446\u2013457 (2015)","journal-title":"Sol. Energy"},{"key":"55_CR13","doi-asserted-by":"crossref","first-page":"409","DOI":"10.1016\/j.enconman.2017.10.008","volume":"153","author":"H Wang","year":"2017","unstructured":"Wang, H., et al.: Deterministic and probabilistic forecasting of photovoltaic power based on deep convolutional neural network. Energy Convers. Manag. 153, 409\u2013422 (2017)","journal-title":"Energy Convers. Manag."},{"key":"55_CR14","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1016\/j.energy.2018.01.177","volume":"148","author":"X Qing","year":"2018","unstructured":"Qing, X., Niu, Y.: Hourly day-ahead solar irradiance prediction using weather forecasts by LSTM. Energy 148, 461\u2013468 (2018)","journal-title":"Energy"},{"key":"55_CR15","doi-asserted-by":"crossref","first-page":"145651","DOI":"10.1109\/ACCESS.2019.2946057","volume":"7","author":"Y Yu","year":"2019","unstructured":"Yu, Y., Cao, J., Zhu, J.: An LSTM short-term solar irradiance forecasting under complicated weather conditions. IEEE Access 7, 145651\u2013145666 (2019)","journal-title":"IEEE Access"},{"key":"55_CR16","doi-asserted-by":"publisher","first-page":"42","DOI":"10.1016\/j.enconman.2016.06.076","volume":"124","author":"VJ Chin","year":"2016","unstructured":"Chin, V.J., Salam, Z., Ishaque, K.: An accurate modelling of the two-diode model of PV module using a hybrid solution based on differential evolution. Energy Convers. Manag. 124, 42\u201350 (2016). https:\/\/doi.org\/10.1016\/j.enconman.2016.06.076","journal-title":"Energy Convers. Manag."},{"key":"55_CR17","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1016\/j.renene.2015.12.030","volume":"89","author":"T McCandless","year":"2016","unstructured":"McCandless, T., Haupt, S., Young, G.S.: A regime-dependent artificial neural network technique for short-range solar irradiance forecasting. Renew. Energy 89, 351\u2013359 (2016)","journal-title":"Renew. Energy"},{"issue":"3","key":"55_CR18","doi-asserted-by":"crossref","first-page":"1064","DOI":"10.1109\/TIA.2012.2190816","volume":"48","author":"J Shi","year":"2012","unstructured":"Shi, J., Lee, W.J., Liu, Y., Yang, Y., Wang, P.: Forecasting power output of photovoltaic systems based on weather classification and support vector machines. IEEE Trans. Ind. Appl. 48(3), 1064\u20131069 (2012)","journal-title":"IEEE Trans. Ind. Appl."},{"key":"55_CR19","doi-asserted-by":"crossref","DOI":"10.1016\/j.energy.2020.119647","volume":"219","author":"A Rafati","year":"2021","unstructured":"Rafati, A., Joorabian, M., Mashhour, E., Shaker, H.R.: High dimensional very short-term solar power forecasting based on a data-driven heuristic method. Energy 219, 119647 (2021)","journal-title":"Energy"},{"key":"55_CR20","doi-asserted-by":"crossref","first-page":"74822","DOI":"10.1109\/ACCESS.2019.2921238","volume":"7","author":"CJ Huang","year":"2019","unstructured":"Huang, C.J., Kuo, P.H.: Multiple-input deep convolutional neural network model for short-term photovoltaic power forecasting. IEEE Access 7, 74822\u201374834 (2019)","journal-title":"IEEE Access"},{"key":"55_CR21","volume":"189","author":"K Wang","year":"2019","unstructured":"Wang, K., Qi, X., Liu, H.: Photovoltaic power forecasting based LSTM-convolutional network. Energy 189, 116225 (2019)","journal-title":"Energy"},{"key":"55_CR22","doi-asserted-by":"publisher","unstructured":"Zhou, H., et al.: Informer: beyond efficient transformer for long sequence time-series forecasting. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 11106\u201311115 (2022). https:\/\/doi.org\/10.1609\/aaai.v35i12.17325","DOI":"10.1609\/aaai.v35i12.17325"},{"key":"55_CR23","unstructured":"Wu, H., Xu, J., Wang, J., Long, M.: Autoformer: decomposition transformers with auto-correlation for long-term series forecasting. Cornell University - arXiv (2021)"},{"key":"55_CR24","unstructured":"Nie, Y., Nguyen, N.H., Sinthong, P., Kalagnanam, J.: A time series is worth 64 words: long-term forecasting with transformers. arXiv preprint arXiv:2211.14730 (2022)"},{"key":"55_CR25","volume":"78","author":"T Peng","year":"2023","unstructured":"Peng, T., et al.: An intelligent hybrid approach for photovoltaic power forecasting using enhanced chaos game optimization algorithm and locality sensitive hashing based informer model. J. Build. Eng. 78, 107635 (2023)","journal-title":"J. Build. Eng."},{"key":"55_CR26","unstructured":"Li, Z., Rao, Z., Pan, L., Xu, Z.: MTS-mixers: multivariate time series forecasting via factorized temporal and channel mixing. arXiv preprint arXiv:2302.04501 (2023)"}],"container-title":["Lecture Notes in Computer Science","Parallel and Distributed Computing, Applications and Technologies"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-96-4207-6_55","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,4,2]],"date-time":"2025-04-02T18:35:58Z","timestamp":1743618958000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-96-4207-6_55"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"ISBN":["9789819642069","9789819642076"],"references-count":26,"URL":"https:\/\/doi.org\/10.1007\/978-981-96-4207-6_55","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025]]},"assertion":[{"value":"1 April 2025","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Disclosure of Interests"}},{"value":"PDCAT","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Parallel and Distributed Computing: Applications and Technologies","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Hong Kong","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2024","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"14 December 2024","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 December 2024","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"25","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pdcat2024","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/hpcc.siat.ac.cn\/meeting\/pdcat2024\/index.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}