{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,27]],"date-time":"2025-10-27T20:54:41Z","timestamp":1761598481016},"reference-count":29,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"7","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEICE Trans. Commun."],"published-print":{"date-parts":[[2023,7,1]]},"DOI":"10.1587\/transcom.2022ebt0003","type":"journal-article","created":{"date-parts":[[2022,12,21]],"date-time":"2022-12-21T22:10:18Z","timestamp":1671660618000},"page":"547-556","source":"Crossref","is-referenced-by-count":1,"title":["Toward Predictive Modeling of Solar Power Generation for Multiple Power Plants"],"prefix":"10.23919","volume":"E106.B","author":[{"given":"Kundjanasith","family":"THONGLEK","sequence":"first","affiliation":[{"name":"Nara Institute of Science and Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kohei","family":"ICHIKAWA","sequence":"additional","affiliation":[{"name":"Nara Institute of Science and Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Keichi","family":"TAKAHASHI","sequence":"additional","affiliation":[{"name":"Tohoku University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chawanat","family":"NAKASAN","sequence":"additional","affiliation":[{"name":"Kasetsart University"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kazufumi","family":"YUASA","sequence":"additional","affiliation":[{"name":"NTT FACILITIES, INC."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tadatoshi","family":"BABASAKI","sequence":"additional","affiliation":[{"name":"NTT FACILITIES, INC."}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hajimu","family":"IIDA","sequence":"additional","affiliation":[{"name":"Nara Institute of Science and Technology"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"1","doi-asserted-by":"crossref","unstructured":"[1] S. Jose and R.L. Itagi, \u201cSmart solar power plant,\u201d Proc. IEEE International Conference on Communications and Signal Processing (ICCSP), pp.0850-0854, 2015. 10.1109\/iccsp.2015.7322615","DOI":"10.1109\/ICCSP.2015.7322615"},{"key":"2","doi-asserted-by":"crossref","unstructured":"[2] J. Vasita, Q. Shakhiya, and J. Modi, \u201cFeasibility study and performance evaluation of a grid-connected rooftop solar pv system,\u201d Proc. IEEE International Conference on Information, Communication, Instrumentation and Control (ICICIC), pp.1-7, 2017. 10.1109\/icomicon.2017.8279067","DOI":"10.1109\/ICOMICON.2017.8279067"},{"key":"3","doi-asserted-by":"crossref","unstructured":"[3] A. Mills and J. Zambreno, \u201cTowards scalable monitoring and maintenance of rechargeable batteries,\u201d IEEE International Conference on Electro\/Information Technology, pp.624-629, 2014. 10.1109\/eit.2014.6871837","DOI":"10.1109\/EIT.2014.6871837"},{"key":"4","doi-asserted-by":"crossref","unstructured":"[4] Z. Hrad\u00edlek, P. Mold\u0159ik, and R. Chv\u00e1lek, \u201cSolar energy storage using hydrogen technology,\u201d Proc. IEEE International Conference on Environment and Electrical Engineering (EEEIC), pp.110-113, 2010. 10.1109\/eeeic.2010.5489998","DOI":"10.1109\/EEEIC.2010.5489998"},{"key":"5","doi-asserted-by":"publisher","unstructured":"[5] V. Prema and K.U. Rao, \u201cDevelopment of statistical time series models for solar power prediction,\u201d Renewable Energy, vol.83, pp.100-109, 2015. 10.1016\/j.renene.2015.03.038","DOI":"10.1016\/j.renene.2015.03.038"},{"key":"6","doi-asserted-by":"crossref","unstructured":"[6] J. Liu, J. Wang, Z. Tan, Y. Meng, and X. Xu, \u201cThe analysis and application of solar energy PV power,\u201d 2011 International Conference on Advanced Power System Automation and Protection, pp.1696-1700, 2011. 10.1109\/apap.2011.6180758","DOI":"10.1109\/APAP.2011.6180758"},{"key":"7","doi-asserted-by":"crossref","unstructured":"[7] S. Vinod, M. Naveen, A.K. Patra, and A.A.R. John, \u201cAccelerating towards larger deep learning models and datasets-A system platform view point,\u201d 2020 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), pp.1-7, 2020. 10.1109\/ipdpsw50202.2020.00169","DOI":"10.1109\/IPDPSW50202.2020.00169"},{"key":"8","unstructured":"[8] K. Thonglek, K. Ichikawa, K. Yuasa, and T. Babasaki, \u201cLSTM-based neural network model for predicting solar power generation,\u201d Proc. Technical Committee on Energy Engineering in Electronics and Communications (IEICE-EE), pp.7-12, May 2021."},{"key":"9","doi-asserted-by":"crossref","unstructured":"[9] O. Obulesu, M. Mahendra, and M. ThrilokReddy, \u201cMachine learning techniques and tools: A survey,\u201d 2018 International Conference on Inventive Research in Computing Applications (ICIRCA), pp.605-611, 2018. 10.1109\/icirca.2018.8597302","DOI":"10.1109\/ICIRCA.2018.8597302"},{"key":"10","doi-asserted-by":"crossref","unstructured":"[10] N. Sharma, P. Sharma, D. Irwin, and P. Shenoy, \u201cPredicting solar generation from weather forecasts using machine learning,\u201d Proc. IEEE International Conference on Smart Grid Communications (SmartGridComm), pp.528-533, 2011. 10.1109\/smartgridcomm.2011.6102379","DOI":"10.1109\/SmartGridComm.2011.6102379"},{"key":"11","doi-asserted-by":"crossref","unstructured":"[11] J. Heaton, \u201cAn empirical analysis of feature engineering for predictive modeling,\u201d Proc. IEEE SoutheastCon, pp.1-6, 2016. 10.1109\/secon.2016.7506650","DOI":"10.1109\/SECON.2016.7506650"},{"key":"12","doi-asserted-by":"publisher","unstructured":"[12] F. Rodr\u00edguez, A. Fleetwood, A. Galarza, and L. Font\u00e1n, \u201cPredicting solar energy generation through artificial neural networks using weather forecasts for microgrid control,\u201d Renewable Energy, vol.126, pp.855-864, 2018. 10.1016\/j.renene.2018.03.070","DOI":"10.1016\/j.renene.2018.03.070"},{"key":"13","doi-asserted-by":"publisher","unstructured":"[13] M. Sajjad, Z.A. Khan, A. Ullah, T. Hussain, W. Ullah, M.Y. Lee, and S.W. Baik, \u201cA novel CNN-GRU-based hybrid approach for short-term residential load forecasting,\u201d IEEE Access, vol.8, pp.143759-143768, 2020. 10.1109\/access.2020.3009537","DOI":"10.1109\/ACCESS.2020.3009537"},{"key":"14","doi-asserted-by":"crossref","unstructured":"[14] D. Wei, \u201cPrediction of stock price based on lstm neural network,\u201d 2019 International Conference on Artificial Intelligence and Advanced Manufacturing (AIAM), pp.544-547, 2019. 10.1109\/aiam48774.2019.00113","DOI":"10.1109\/AIAM48774.2019.00113"},{"key":"15","doi-asserted-by":"crossref","unstructured":"[15] S. Hochreiter and J. Schmidhuber, \u201cLong short-term memory,\u201d Neural Computation, vol.9, no.8, pp.1735-1780, Nov. 1997. 10.1162\/neco.1997.9.8.1735","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"16","doi-asserted-by":"crossref","unstructured":"[16] J. Zhang, Y. Chi, and L. Xiao, \u201cSolar power generation forecast based on LSTM,\u201d Proc. IEEE International Conference on Software Engineering and Service Science (ICSESS), pp.869-872, 2018. 10.1109\/icsess.2018.8663788","DOI":"10.1109\/ICSESS.2018.8663788"},{"key":"17","doi-asserted-by":"crossref","unstructured":"[17] M.R. Islam, M.A.M. Hasan, and A. Sayeed, \u201cTransfer learning based diabetic retinopathy detection with a novel preprocessed layer,\u201d Proc. IEEE Region 10 Symposium (TENSYMP), pp.888-891, 2020. 10.1109\/tensymp50017.2020.9230648","DOI":"10.1109\/TENSYMP50017.2020.9230648"},{"key":"18","unstructured":"[18] D. Sarkar, R. Bali, and T. Ghosh, Hands-On Transfer Learning with Python: Implement Advanced Deep Learning and Neural Network Models Using TensorFlow and Keras, Packt Publishing, 2018."},{"key":"19","doi-asserted-by":"crossref","unstructured":"[19] T. Alshalali and D. Josyula, \u201cFine-tuning of pre-trained deep learning models with extreme learning machine,\u201d 2018 International Conference on Computational Science and Computational Intelligence (CSCI), pp.469-473, 2018. 10.1109\/csci46756.2018.00096","DOI":"10.1109\/CSCI46756.2018.00096"},{"key":"20","doi-asserted-by":"publisher","unstructured":"[20] S. Chhabra, P. Majumdar, M. Vatsa, and R. Singh, \u201cData fine-tuning,\u201d Proc. AAAI Conference on Artificial Intelligence, pp.8223-8230, July 2019. 10.1609\/aaai.v33i01.33018223","DOI":"10.1609\/aaai.v33i01.33018223"},{"key":"21","doi-asserted-by":"crossref","unstructured":"[21] A. Senior, G. Heigold, M. Ranzato, and K. Yang, \u201cAn empirical study of learning rates in deep neural networks for speech recognition,\u201d 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, pp.6724-6728, 2013. 10.1109\/icassp.2013.6638963","DOI":"10.1109\/ICASSP.2013.6638963"},{"key":"22","doi-asserted-by":"crossref","unstructured":"[22] Y. Wu, L. Liu, J. Bae, K.H. Chow, A. Iyengar, C. Pu, W. Wei, L. Yu, and Q. Zhang, \u201cDemystifying learning rate policies for high accuracy training of deep neural networks,\u201d 2019 IEEE International Conference on Big Data (Big Data), pp.1971-1980, 2019. 10.1109\/bigdata47090.2019.9006104","DOI":"10.1109\/BigData47090.2019.9006104"},{"key":"23","doi-asserted-by":"publisher","unstructured":"[23] K. Goutam, S. Balasubramanian, D. Gera, and R.R. Sarma, \u201cLayerout: Freezing layers in deep neural networks,\u201d SN Computer Science, vol.1, p.295, Sept. 2020. 10.1007\/s42979-020-00312-x","DOI":"10.1007\/s42979-020-00312-x"},{"key":"24","doi-asserted-by":"crossref","unstructured":"[24] X. Xiao, T. Bamunu Mudiyanselage, C. Ji, J. Hu, and Y. Pan, \u201cFast deep learning training through intelligently freezing layers,\u201d 2019 International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), pp.1225-1232, 2019. 10.1109\/ithings\/greencom\/cpscom\/smartdata.2019.00205","DOI":"10.1109\/iThings\/GreenCom\/CPSCom\/SmartData.2019.00205"},{"key":"25","doi-asserted-by":"crossref","unstructured":"[25] M. Oyeleye, T. Chen, S. Titarenko, and G. Antoniou, \u201cA predictive analysis of heart rates using machine learning techniques,\u201d International Journal of Environmental Research and Public Health, vol.19, no.4, 2022. 10.3390\/ijerph19042417","DOI":"10.3390\/ijerph19042417"},{"key":"26","unstructured":"[26] S. Dhar, T. Mukherjee, and A.K. Ghoshal, \u201cPerformance evaluation of neural network approach in financial prediction: Evidence from indian market,\u201d 2010 International Conference on Communication and Computational Intelligence (INCOCCI), pp.597-602, 2010."},{"key":"27","doi-asserted-by":"crossref","unstructured":"[27] J. Konar, P. Khandelwal, and R. Tripathi, \u201cComparison of various learning rate scheduling techniques on convolutional neural network,\u201d 2020 IEEE International Students&apos; Conference on Electrical, Electronics and Computer Science (SCEECS), pp.1-5, 2020. 10.1109\/sceecs48394.2020.94","DOI":"10.1109\/SCEECS48394.2020.94"},{"key":"28","doi-asserted-by":"publisher","unstructured":"[28] K. Saito, T. Fujita, Y. Yamada, J. Ishida, Y. Kumagai, K. Aranami, S. Ohmori, R. Nagasawa, S. Kumagai, C. Muroi, T. Kato, H. Eito, and Y. Yamazaki, \u201cThe operational JMA nonhydrostatic mesoscale model,\u201d Monthly Weather Review, vol.134, no.4, pp.1266-1298, 2006. 10.1175\/mwr3120.1","DOI":"10.1175\/MWR3120.1"},{"key":"29","doi-asserted-by":"crossref","unstructured":"[29] V. Ramasamy, D. Feldman, J. Desai, and R. Margolis, \u201cU.S. solar photovoltaic system and energy storage cost benchmarks: Q1 2021,\u201d Technical Report, National Renewable Energy Laboratory, Nov. 2021. 10.2172\/1829460","DOI":"10.2172\/1829460"}],"container-title":["IEICE Transactions on Communications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transcom\/E106.B\/7\/E106.B_2022EBT0003\/_pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,10]],"date-time":"2024-01-10T15:00:49Z","timestamp":1704898849000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.jstage.jst.go.jp\/article\/transcom\/E106.B\/7\/E106.B_2022EBT0003\/_article"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,1]]},"references-count":29,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2023]]}},"URL":"https:\/\/doi.org\/10.1587\/transcom.2022ebt0003","relation":{},"ISSN":["0916-8516","1745-1345"],"issn-type":[{"value":"0916-8516","type":"print"},{"value":"1745-1345","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,1]]},"article-number":"2022EBT0003"}}