{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T14:42:42Z","timestamp":1743086562468,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":23,"publisher":"Springer Singapore","isbn-type":[{"type":"print","value":"9789811600098"},{"type":"electronic","value":"9789811600104"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/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":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-981-16-0010-4_11","type":"book-chapter","created":{"date-parts":[[2021,2,9]],"date-time":"2021-02-09T00:19:49Z","timestamp":1612829989000},"page":"115-127","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Analysing and Forecasting Electricity Demand and Price Using Deep Learning Model During the COVID-19\u00a0Pandemic"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2248-2472","authenticated-orcid":false,"given":"Israt","family":"Fatema","sequence":"first","affiliation":[]},{"given":"Xiaoying","family":"Kong","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0845-6718","authenticated-orcid":false,"given":"Gengfa","family":"Fang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,2,7]]},"reference":[{"key":"11_CR1","unstructured":"Adhikari, R., Agrawal, R.K.: An introductory study on time series modeling and forecasting. arXiv preprint arXiv:1302.6613 (2013)"},{"key":"11_CR2","first-page":"3104","volume":"27","author":"I Sutskever","year":"2014","unstructured":"Sutskever, I., Vinyals, O., Le, Q.V.: Sequence to sequence learning with neural networks. Adv. Neural Inf. Process. Syst. 27, 3104\u20133112 (2014)","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"7553","key":"11_CR3","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y LeCun","year":"2015","unstructured":"LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436\u2013444 (2015)","journal-title":"Nature"},{"key":"11_CR4","unstructured":"Mikolov, T., Joulin, A., Chopra, S., Mathieu, M., Ranzato, M.A.: Learning longer memory in recurrent neural networks. arXiv preprint arXiv:1412.7753 (2014)"},{"key":"11_CR5","doi-asserted-by":"crossref","unstructured":"Graves, A., Mohamed, A.-R., Hinton, G.: Speech recognition with deep RNN networks. In: IEEE Conference on Acoustics, Speech and Signal Processing, pp. 6645\u20136649 (2013)","DOI":"10.1109\/ICASSP.2013.6638947"},{"key":"11_CR6","doi-asserted-by":"crossref","unstructured":"Nelson, D.M., Pereira, A.C., de Oliveira, R.A.: Stock market's price movement prediction with LSTM neural networks. In: IEEE International Joint Conference on Neural Networks (IJCNN), pp. 1419\u20131426 (2017)","DOI":"10.1109\/IJCNN.2017.7966019"},{"issue":"7","key":"11_CR7","doi-asserted-by":"publisher","first-page":"1387","DOI":"10.3390\/w11071387","volume":"1","author":"X-H Le","year":"2019","unstructured":"Le, X.-H., Ho, H.V., Lee, G., Jung, S.: Application of long short-term memory (LSTM) neural network for flood forecasting. Water 1(7), 1387 (2019)","journal-title":"Water"},{"issue":"5","key":"11_CR8","doi-asserted-by":"publisher","first-page":"1255","DOI":"10.3390\/en11051255","volume":"11","author":"U Ugurlu","year":"2018","unstructured":"Ugurlu, U., Oksuz, I., Tas, O.: Electricity price forecasting using recurrent neural networks. Energies 11(5), 1255 (2018)","journal-title":"Energies"},{"key":"11_CR9","unstructured":"Lipton, Z.C., Berkowitz, J., Elkan, C.: A critical review of recurrent neural networks for sequence learning. arXiv preprint arXiv:1506.00019 (2015)"},{"issue":"8","key":"11_CR10","doi-asserted-by":"publisher","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735\u20131780 (1997)","journal-title":"Neural Comput."},{"issue":"7","key":"11_CR11","doi-asserted-by":"publisher","first-page":"1636","DOI":"10.3390\/en11071636","volume":"11","author":"S Bouktif","year":"2018","unstructured":"Bouktif, S., Fiaz, A., Ouni, A., Serhani, M.A.: Optimal deep learning LSTM model for electric load forecasting using feature selection and genetic algorithm: comparison with machine learning approaches. Energies 11(7), 1636 (2018)","journal-title":"Energies"},{"issue":"10","key":"11_CR12","doi-asserted-by":"publisher","first-page":"1525","DOI":"10.3390\/en10101525","volume":"10","author":"C Li","year":"2017","unstructured":"Li, C., Ding, Z., Zhao, D., Yi, J., Zhang, G.: Building energy consumption prediction: an extreme deep learning approach. Energies 10(10), 1525 (2017)","journal-title":"Energies"},{"key":"11_CR13","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1016\/j.jpdc.2017.06.007","volume":"117","author":"C Tong","year":"2018","unstructured":"Tong, C., Li, J., Lang, C., Kong, F., Niu, J., Rodrigues, J.J.: An efficient deep model for day-ahead electricity load forecasting with stacked denoising auto-encoders. J. Parallel Distrib. Comput. 117, 267\u2013273 (2018)","journal-title":"J. Parallel Distrib. Comput."},{"key":"11_CR14","doi-asserted-by":"crossref","unstructured":"Marino, D.L., Amarasinghe, K., Manic, M.: Building energy load forecasting using deep neural networks. In: IECON 2016\u201342nd Annual Conference of the IEEE Industrial Electronics Society, pp. 7046\u20137051 (2016)","DOI":"10.1109\/IECON.2016.7793413"},{"key":"11_CR15","doi-asserted-by":"publisher","first-page":"36411","DOI":"10.1109\/ACCESS.2020.2975738","volume":"8","author":"L Sehovac","year":"2020","unstructured":"Sehovac, L., Grolinger, K.: Deep learning for load forecasting: sequence to sequence recurrent neural networks with attention. IEEE Access 8, 36411\u201336426 (2020)","journal-title":"IEEE Access"},{"issue":"16","key":"11_CR16","doi-asserted-by":"publisher","first-page":"3199","DOI":"10.3390\/en12163199","volume":"12","author":"G Gong","year":"2019","unstructured":"Gong, G., An, X., Mahato, N.K., Sun, S., Chen, S., Wen, Y.: Research on short-term load prediction based on Seq2seq model. Energies 12(16), 3199 (2019)","journal-title":"Energies"},{"issue":"4","key":"11_CR17","doi-asserted-by":"publisher","first-page":"1280","DOI":"10.3390\/su10041280","volume":"10","author":"P-H Kuo","year":"2018","unstructured":"Kuo, P.-H., Huang, C.-J.: An electricity price forecasting model by hybrid structured deep neural networks. Sustainability 10(4), 1280 (2018)","journal-title":"Sustainability"},{"issue":"1","key":"11_CR18","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: a simple way to prevent neural networks from overfitting. J. Mach. Learn. Res. 15(1), 1929\u20131958 (2014)","journal-title":"J. Mach. Learn. Res."},{"key":"11_CR19","unstructured":"Aggregated Demand and Price Data. https:\/\/aemo.com.au\/, Aaccessed 28 Sept 2020"},{"issue":"1","key":"11_CR20","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1111\/j.1540-5915.1999.tb01606.x","volume":"30","author":"MY Hu","year":"1999","unstructured":"Hu, M.Y., Zhang, G., Jiang, C.X., Patuwo, B.E.: A cross-validation analysis of neural network out-of-sample performance in exchange rate forecasting. Decis. Sci. 30(1), 197\u2013216 (1999)","journal-title":"Decis. Sci."},{"key":"11_CR21","volume-title":"Learning Scikit-Learn: Machine Learning in Python","author":"R Garreta","year":"2013","unstructured":"Garreta, R., Moncecchi, G.: Learning Scikit-Learn: Machine Learning in Python. Packt Publishing Ltd., Birmingham (2013)"},{"key":"11_CR22","unstructured":"National Electricity Market. https:\/\/opennem.org.au\/energy\/nem\/, Accessed 26 Sept 2020"},{"issue":"4","key":"11_CR23","doi-asserted-by":"publisher","first-page":"1591","DOI":"10.1109\/TSG.2015.2493205","volume":"8","author":"B Stephen","year":"2015","unstructured":"Stephen, B., Tang, X., Harvey, P.R., Galloway, S., Jennett, K.I.: Incorporating practice theory in sub-profile models for short term aggregated residential load forecasting. IEEE Trans. Smart Grid. 8(4), 1591\u20131598 (2015)","journal-title":"IEEE Trans. Smart Grid."}],"container-title":["Communications in Computer and Information Science","Parallel Architectures, Algorithms and Programming"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-16-0010-4_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,4,25]],"date-time":"2021-04-25T02:23:51Z","timestamp":1619317431000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-16-0010-4_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9789811600098","9789811600104"],"references-count":23,"URL":"https:\/\/doi.org\/10.1007\/978-981-16-0010-4_11","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":"7 February 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PAAP","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Symposium on Parallel Architectures, Algorithms and Programming","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shenzhen","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":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 December 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20 December 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"11","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"paap2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"OCS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"75","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":"37","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":"49% - 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":"6","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}