{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T15:46:46Z","timestamp":1742917606237,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":23,"publisher":"Springer Singapore","isbn-type":[{"type":"print","value":"9789811651878"},{"type":"electronic","value":"9789811651885"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-981-16-5188-5_7","type":"book-chapter","created":{"date-parts":[[2021,8,19]],"date-time":"2021-08-19T23:04:52Z","timestamp":1629414292000},"page":"83-95","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["An Improved Echo State Network Model for Spatial-Temporal Energy Consumption Prediction in Public Buildings"],"prefix":"10.1007","author":[{"given":"Yuyang","family":"Sun","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ji","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ruiqi","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhou","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,8,20]]},"reference":[{"key":"7_CR1","doi-asserted-by":"crossref","unstructured":"Allouhi, A., EI-Fouih, Y., Kousksou, T., Jamil, A., Zeraouli, Y., Mourad, Y.: Energy consumption and efficiency in buildings: current status and future trends. J. Clean. Prod. 109, 118\u2013130 (2015)","DOI":"10.1016\/j.jclepro.2015.05.139"},{"key":"7_CR2","doi-asserted-by":"publisher","first-page":"371","DOI":"10.1016\/j.egypro.2017.03.155","volume":"110","author":"S Banihashemi","year":"2017","unstructured":"Banihashemi, S., Ding, G., Wang, J.: Developing a hybrid model of prediction and classification algorithms for building energy consumption. Energy Procedia 110, 371\u2013376 (2017)","journal-title":"Energy Procedia"},{"issue":"1","key":"7_CR3","first-page":"37","volume":"5","author":"L Moga","year":"2015","unstructured":"Moga, L., Moga, I.: Building design influence on the energy performance. J. Appl. Eng. Sci. 5(1), 37\u201346 (2015)","journal-title":"J. Appl. Eng. Sci."},{"issue":"11","key":"7_CR4","doi-asserted-by":"publisher","first-page":"3243","DOI":"10.1016\/j.enbuild.2011.08.025","volume":"43","author":"C Chang","year":"2011","unstructured":"Chang, C., Jing, Z., Zhu, N.: Energy saving effect prediction and post evaluation of air-conditioning system in public buildings. Energy Build. 43(11), 3243\u20133249 (2011)","journal-title":"Energy Build."},{"key":"7_CR5","unstructured":"Fong, W.K., Matsumoto, H., Lun, Y.F., et al.: System dynamic model for the prediction of urban energy consumption trends (2007)"},{"key":"7_CR6","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1016\/j.apenergy.2018.03.125","volume":"221","author":"Y Guo","year":"2018","unstructured":"Guo, Y., Wang, J., Chen, H., et al.: Machine learning-based thermal response time ahead energy demand prediction for building heating systems. Appl. Energy 221, 16\u201327 (2018)","journal-title":"Appl. Energy"},{"key":"7_CR7","doi-asserted-by":"publisher","first-page":"403","DOI":"10.1016\/j.buildenv.2016.05.034","volume":"105","author":"M Killian","year":"2016","unstructured":"Killian, M., Koze, M.: Ten questions concerning model predictive control for energy efficient building. Build. Environ. 105, 403\u2013412 (2016)","journal-title":"Build. Environ."},{"key":"7_CR8","doi-asserted-by":"crossref","unstructured":"Amasyali, K., EI-Gohary, N.M.: A review of data-driven building energy consumption prediction studies. Renew. Sustain. Energy Rev. 81, 1192\u20131205 (2018)","DOI":"10.1016\/j.rser.2017.04.095"},{"key":"7_CR9","doi-asserted-by":"publisher","first-page":"109980","DOI":"10.1016\/j.rser.2020.109980","volume":"131","author":"XJ Luo","year":"2020","unstructured":"Luo, X.J., Oyedele, L.O., Ajayi, A.O., et al.: Feature extraction and genetic algorithm enhanced adaptive deep neural network for energy consumption prediction in buildings. Renew. Sustain. Energy Rev. 131, 109980 (2020)","journal-title":"Renew. Sustain. Energy Rev."},{"issue":"11","key":"7_CR10","first-page":"57","volume":"149","author":"LGB Ruiz","year":"2017","unstructured":"Ruiz, L.G.B., Rueda, R., Cuellar, M.P., et al.: Energy consumption forecasting based on elman neural networks with evolutive optimization. Expert Syst. Appl. 149(11), 57\u201368 (2017)","journal-title":"Expert Syst. Appl."},{"key":"7_CR11","doi-asserted-by":"publisher","first-page":"117756","DOI":"10.1016\/j.energy.2020.117756","volume":"203","author":"H Lu","year":"2020","unstructured":"Lu, H., Cheng, F., et al.: Short-term prediction of building energy consumption employing an improved extreme gradient boosting model: a case study of an intake tower. Energy 203, 117756 (2020)","journal-title":"Energy"},{"key":"7_CR12","doi-asserted-by":"publisher","first-page":"121082","DOI":"10.1016\/j.jclepro.2020.121082","volume":"260","author":"A-D Pham","year":"2020","unstructured":"Pham, A.-D., Ngo, N.-T., et al.: Prediction energy consumption in multiple buildings using machine learning for improving energy efficiency and sustainability. J. Clean. Prod. 260, 121082 (2020)","journal-title":"J. Clean. Prod."},{"key":"7_CR13","doi-asserted-by":"publisher","first-page":"122542","DOI":"10.1016\/j.jclepro.2020.122542","volume":"272","author":"Y Liu","year":"2020","unstructured":"Liu, Y., Chen, H., et al.: Energy consumption prediction and diagnosis of public buildings based on support vector machine learning: a case study in China. J. Clean. Prod. 272, 122542 (2020)","journal-title":"J. Clean. Prod."},{"key":"7_CR14","doi-asserted-by":"publisher","first-page":"109675.1","DOI":"10.1016\/j.enbuild.2019.109675","volume":"208","author":"T Liu","year":"2020","unstructured":"Liu, T., Tan, Z., Xu, C., et al.: Study on deep reinforcement learning techniques for building energy consumption forecasting. Energy Build. 208, 109675.1-109675.14 (2020)","journal-title":"Energy Build."},{"key":"7_CR15","doi-asserted-by":"publisher","first-page":"110225","DOI":"10.1016\/j.enbuild.2020.110225","volume":"224","author":"S Brandi","year":"2020","unstructured":"Brandi, S., Piscitelli, M.S., Martellacci, M., et al.: Deep reinforcement learning to optimise indoor temperature control and heating energy consumption in buildings. Energy Build. 224, 110225 (2020)","journal-title":"Energy Build."},{"issue":"5667","key":"7_CR16","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1126\/science.1091277","volume":"304","author":"H Jaeger","year":"2004","unstructured":"Jaeger, H., Haas, H.: Harnessing nonlinearity: predicting chaotic systems and saving energy in wireless communication. Science 304(5667), 78\u201380 (2004)","journal-title":"Science"},{"key":"7_CR17","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ins.2018.09.057","volume":"475","author":"L Sun","year":"2019","unstructured":"Sun, L., Jin, B., Yang, H., et al.: Unsupervised EEG feature extraction based on echo state network. Inf. Sci. 475, 1\u201317 (2019)","journal-title":"Inf. Sci."},{"key":"7_CR18","doi-asserted-by":"publisher","first-page":"53","DOI":"10.1016\/j.artmed.2018.02.002","volume":"86","author":"SE Lacy","year":"2018","unstructured":"Lacy, S.E., Smith, S.L., Lones, M.A.: Using echo state networks for classification: a case study in parkinsons disease diagnosis. Artif. Intell. Med. 86, 53\u201359 (2018)","journal-title":"Artif. Intell. Med."},{"key":"7_CR19","doi-asserted-by":"publisher","first-page":"211","DOI":"10.1016\/j.asoc.2017.01.049","volume":"55","author":"N Chouikhi","year":"2017","unstructured":"Chouikhi, N., Ammar, B., Rokbani, N., et al.: PSO-based analysis of echo state network parameters for time series forecasting. Appl. Soft Comput. 55, 211\u2013225 (2017)","journal-title":"Appl. Soft Comput."},{"key":"7_CR20","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1016\/j.enconman.2016.02.022","volume":"114","author":"W Sun","year":"2016","unstructured":"Sun, W., Liu, M.: Wind speed forecasting using FEEMD echo state networks with RELM in Hebei, China. Energy Convers. Manag. 114, 197\u2013208 (2016)","journal-title":"Energy Convers. Manag."},{"key":"7_CR21","doi-asserted-by":"publisher","first-page":"121151","DOI":"10.1016\/j.jclepro.2020.121231","volume":"261","author":"L Qian","year":"2020","unstructured":"Qian, L., Zhou, W., Hz, C.: Spatio-temporal modeling with enhanced flexibility and robustness of solar irradiance prediction: a chain-structure echo state network approach. J. Clean. Prod. 261, 121151 (2020)","journal-title":"J. Clean. Prod."},{"issue":"3","key":"7_CR22","doi-asserted-by":"publisher","first-page":"335","DOI":"10.1016\/j.neunet.2007.04.016","volume":"20","author":"H Jaeger","year":"2007","unstructured":"Jaeger, H., Lukosevicius, M., Popovici, D., et al.: Optimization and applications of echo state networks with leaky-integrator neurons. Neural Netw. 20(3), 335\u2013352 (2007)","journal-title":"Neural Netw."},{"key":"7_CR23","doi-asserted-by":"publisher","first-page":"439","DOI":"10.1016\/j.egypro.2017.07.400","volume":"122","author":"C Miller","year":"2017","unstructured":"Miller, C., Meggers, F.: The building data genome project: an open, public data set from non-residential building electrical meters. Energy Procedia 122, 439\u2013444 (2017)","journal-title":"Energy Procedia"}],"container-title":["Communications in Computer and Information Science","Neural Computing for Advanced Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-16-5188-5_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,8,19]],"date-time":"2021-08-19T23:09:24Z","timestamp":1629414564000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-16-5188-5_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9789811651878","9789811651885"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-981-16-5188-5_7","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"20 August 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"NCAA","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Neural Computing for Advanced Applications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Guangzhou","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":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 August 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 August 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ncaa2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/dl2link.com\/ncaa2021\/","order":11,"name":"conference_url","label":"Conference URL","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":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"144","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":"54","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":"0","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":"38% - 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":"3.07","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":"3.62","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)"}}]}}