{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,24]],"date-time":"2025-12-24T12:20:44Z","timestamp":1766578844292,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":27,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819998920"},{"type":"electronic","value":"9789819998937"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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":[[2024]]},"DOI":"10.1007\/978-981-99-9893-7_6","type":"book-chapter","created":{"date-parts":[[2024,1,22]],"date-time":"2024-01-22T18:03:08Z","timestamp":1705946588000},"page":"72-87","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["VMD-AC-LSTM: An Accurate Prediction Method for Solar Irradiance"],"prefix":"10.1007","author":[{"given":"Jianwei","family":"Wang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ke","family":"Yan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xiang","family":"Ma","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,1,23]]},"reference":[{"key":"6_CR1","doi-asserted-by":"publisher","first-page":"399","DOI":"10.1016\/j.rser.2017.07.046","volume":"81","author":"Z Guo","year":"2018","unstructured":"Guo, Z., Zhou, K., Zhang, C., et al.: Residential electricity consumption behavior: Influencing factors, related theories and intervention strategies. Renew. Sustain. Energy Rev. 81, 399\u2013412 (2018)","journal-title":"Renew. Sustain. Energy Rev."},{"key":"6_CR2","doi-asserted-by":"publisher","first-page":"531","DOI":"10.1016\/j.rser.2017.05.251","volume":"80","author":"MM Bah","year":"2017","unstructured":"Bah, M.M., Azam, M.: Investigating the relationship between electricity consumption and economic growth: evidence from South Africa. Renew. Sustain. Energy Rev. 80, 531\u2013537 (2017)","journal-title":"Renew. Sustain. Energy Rev."},{"key":"6_CR3","doi-asserted-by":"publisher","DOI":"10.1016\/j.enconman.2022.115374","volume":"256","author":"YJ Chae","year":"2022","unstructured":"Chae, Y.J., Lee, J.I.: Thermodynamic analysis of compressed and liquid carbon dioxide energy storage system integrated with steam cycle for flexible operation of thermal power plant. Energy Convers. Manage. 256, 115374 (2022)","journal-title":"Energy Convers. Manage."},{"key":"6_CR4","doi-asserted-by":"publisher","first-page":"397","DOI":"10.1007\/s10640-016-0025-3","volume":"68","author":"S Carley","year":"2017","unstructured":"Carley, S., Baldwin, E., MacLean, L.M., et al.: Global expansion of renewable energy generation: an analysis of policy instruments. Environ. Resource Econ. 68, 397\u2013440 (2017)","journal-title":"Environ. Resource Econ."},{"issue":"3","key":"6_CR5","doi-asserted-by":"publisher","first-page":"1458","DOI":"10.1109\/TSTE.2018.2789937","volume":"9","author":"E Scolari","year":"2018","unstructured":"Scolari, E., Reyes-Chamorro, L., Sossan, F., et al.: A comprehensive assessment of the short-term uncertainty of grid-connected PV systems. IEEE Trans. Sustainable Energy 9(3), 1458\u20131467 (2018)","journal-title":"IEEE Trans. Sustainable Energy"},{"key":"6_CR6","doi-asserted-by":"crossref","unstructured":"Wang, W., Chen, H., Lou, B., et al: Data-driven intelligent maintenance planning of smart meter reparations for large-scale smart electric power grid. In: 2018 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computing, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation, pp. 1929\u20131935. IEEE (2018)","DOI":"10.1109\/SmartWorld.2018.00323"},{"key":"6_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2022.123403","volume":"246","author":"X Huang","year":"2022","unstructured":"Huang, X., Li, Q., Tai, Y., et al.: Time series forecasting for hourly photovoltaic power using conditional generative adversarial network and Bi-LSTM. Energy 246, 123403 (2022)","journal-title":"Energy"},{"issue":"3","key":"6_CR8","doi-asserted-by":"publisher","first-page":"366","DOI":"10.3390\/app4030366","volume":"4","author":"A Yona","year":"2014","unstructured":"Yona, A., Senjyu, T., Funabashi, T., et al.: Optimizing re-planning operation for smart house applying solar radiation forecasting. Appl. Sci. 4(3), 366\u2013379 (2014)","journal-title":"Appl. Sci."},{"key":"6_CR9","doi-asserted-by":"crossref","unstructured":"Pi, M., Jin, N., Ma, X., et al.: Short-term solar irradiation prediction model based on WCNN_ALSTM. In: 2021 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress, pp. 405\u2013412. IEEE (2021)","DOI":"10.1109\/DASC-PICom-CBDCom-CyberSciTech52372.2021.00075"},{"key":"6_CR10","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2021.117449","volume":"301","author":"L Zhang","year":"2021","unstructured":"Zhang, L., Wang, J., Niu, X., et al.: Ensemble wind speed forecasting with multi-objective Archimedes optimization algorithm and sub-model selection. Appl. Energy 301, 117449 (2021)","journal-title":"Appl. Energy"},{"key":"6_CR11","doi-asserted-by":"publisher","DOI":"10.1016\/j.aei.2021.101442","volume":"51","author":"N Jin","year":"2022","unstructured":"Jin, N., Yang, F., Mo, Y., et al.: Highly accurate energy consumption forecasting model based on parallel LSTM neural networks. Adv. Eng. Inform. 51, 101442 (2022)","journal-title":"Adv. Eng. Inform."},{"key":"6_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2019.05.028","volume":"181","author":"Y Li","year":"2019","unstructured":"Li, Y., Zhu, Z., Kong, D., et al.: EA-LSTM: evolutionary attention-based LSTM for time series prediction. Knowl.-Based Syst.Based Syst. 181, 104785 (2019)","journal-title":"Knowl.-Based Syst.Based Syst."},{"issue":"5","key":"6_CR13","doi-asserted-by":"publisher","first-page":"2799","DOI":"10.3390\/s23052799","volume":"23","author":"Q Li","year":"2023","unstructured":"Li, Q., Zhang, D., Yan, K.: A solar irradiance forecasting framework based on the CEE-WGAN-LSTM model. Sensors 23(5), 2799 (2023)","journal-title":"Sensors"},{"issue":"1","key":"6_CR14","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1007\/s12145-021-00723-1","volume":"15","author":"P Singla","year":"2022","unstructured":"Singla, P., Duhan, M., Saroha, S.: An ensemble method to forecast 24-h ahead solar irradiance using wavelet decomposition and BiLSTM deep learning network. Earth Sci. Inf. 15(1), 291\u2013306 (2022)","journal-title":"Earth Sci. Inf."},{"key":"6_CR15","doi-asserted-by":"publisher","first-page":"127","DOI":"10.1016\/j.physa.2018.11.061","volume":"519","author":"J Cao","year":"2019","unstructured":"Cao, J., Li, Z., Li, J.: Financial time series forecasting model based on CEEMDAN and LSTM. Physica A 519, 127\u2013139 (2019)","journal-title":"Physica A"},{"issue":"6","key":"6_CR16","first-page":"1","volume":"55","author":"K Benidis","year":"2022","unstructured":"Benidis, K., Rangapuram, S.S., Flunkert, V., et al.: Deep learning for time series forecasting: tutorial and literature survey. ACM Comput. Surv.Comput. Surv. 55(6), 1\u201336 (2022)","journal-title":"ACM Comput. Surv.Comput. Surv."},{"key":"6_CR17","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2019.112896","volume":"140","author":"K Bandara","year":"2020","unstructured":"Bandara, K., Bergmeir, C., Smyl, S.: Forecasting across time series databases using recurrent neural networks on groups of similar series: a clustering approach. Expert Syst. Appl. 140, 112896 (2020)","journal-title":"Expert Syst. Appl."},{"key":"6_CR18","doi-asserted-by":"publisher","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":"6_CR19","doi-asserted-by":"publisher","DOI":"10.1016\/j.bdr.2022.100360","volume":"31","author":"L Cascone","year":"2023","unstructured":"Cascone, L., Sadiq, S., Ullah, S., et al.: Predicting household electric power consumption using multi-step time series with convolutional LSTM. Big Data Research 31, 100360 (2023)","journal-title":"Big Data Research"},{"key":"6_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2022.109796","volume":"256","author":"J Guo","year":"2022","unstructured":"Guo, J., Wang, W., Tang, Y., et al.: A CNN-Bi_LSTM parallel network approach for train travel time prediction. Knowl.-Based Syst. 256, 109796 (2022)","journal-title":"Knowl.-Based Syst."},{"key":"6_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.buildenv.2022.108822","volume":"213","author":"Y Zeng","year":"2022","unstructured":"Zeng, Y., Chen, J., Jin, N., et al.: Air quality forecasting with hybrid LSTM and extended stationary wavelet transform. Build. Environ. 213, 108822 (2022)","journal-title":"Build. Environ."},{"key":"6_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2022\/2372748","volume":"2022","author":"M Pi","year":"2022","unstructured":"Pi, M., Jin, N., Chen, D., et al.: Short-term solar irradiance prediction based on multichannel LSTM neural networks using edge-based IoT system. Wirel. Commun. Mob. Comput. 2022, 1\u201311 (2022)","journal-title":"Wirel. Commun. Mob. Comput."},{"issue":"3","key":"6_CR23","doi-asserted-by":"publisher","first-page":"531","DOI":"10.1109\/TSP.2013.2288675","volume":"62","author":"K Dragomiretskiy","year":"2013","unstructured":"Dragomiretskiy, K., Zosso, D.: Variational mode decomposition. IEEE Trans. Signal Process. 62(3), 531\u2013544 (2013)","journal-title":"IEEE Trans. Signal Process."},{"key":"6_CR24","unstructured":"Vaswani, A., Shazeer, N., Parmar, N., et al.: Attention is all you need. Advances in neural information processing systems, 30 (2017)"},{"key":"6_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2023.126781","volume":"269","author":"R Li","year":"2023","unstructured":"Li, R., Zeng, D., Li, T., et al.: Real-time prediction of SO2 emission concentration under wide range of variable loads by convolution-LSTM VE-transformer. Energy 269, 126781 (2023)","journal-title":"Energy"},{"key":"6_CR26","doi-asserted-by":"crossref","unstructured":"Markova, M.: Convolutional neural networks for forex time series forecasting. In: AIP Conference Proceedings. AIP Publishing 2459(1) (2022)","DOI":"10.1063\/5.0083533"},{"key":"6_CR27","doi-asserted-by":"publisher","first-page":"240","DOI":"10.1016\/j.neunet.2022.10.009","volume":"157","author":"H Wang","year":"2023","unstructured":"Wang, H., Zhang, Y., Liang, J., et al.: DAFA-BiLSTM: deep autoregression feature augmented bidirectional LSTM network for time series prediction. Neural Netw. 157, 240\u2013256 (2023)","journal-title":"Neural Netw."}],"container-title":["Lecture Notes in Computer Science","Green, Pervasive, and Cloud Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-9893-7_6","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,22]],"date-time":"2024-01-22T18:03:40Z","timestamp":1705946620000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-9893-7_6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9789819998920","9789819998937"],"references-count":27,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-9893-7_6","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"23 January 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"GPC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Green, Pervasive, and Cloud Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Harbin","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":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 September 2023","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":"gpc2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Easy Chair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"111","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"38","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"34% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"4","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}