{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,18]],"date-time":"2025-06-18T04:19:00Z","timestamp":1750220340204,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":29,"publisher":"ACM","license":[{"start":{"date-parts":[[2021,12,15]],"date-time":"2021-12-15T00:00:00Z","timestamp":1639526400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,12,15]]},"DOI":"10.1145\/3508072.3508079","type":"proceedings-article","created":{"date-parts":[[2022,4,13]],"date-time":"2022-04-13T17:01:56Z","timestamp":1649869316000},"page":"31-41","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Day-ahead electricity load forecasting based on hybrid model of EEMD and Bidirectional LSTM"],"prefix":"10.1145","author":[{"given":"Thi Hoai Thu","family":"Nguyen","sequence":"first","affiliation":[{"name":"School of Electrical and Electronic Engineering, Hanoi University of Science and Technology, Vietnam"}]},{"given":"Quoc Bao","family":"Phan","sequence":"additional","affiliation":[{"name":"School of Electrical and Electronic Engineering, Hanoi University of Science and Technology, Vietnam"}]},{"given":"Vu Nhat Nam","family":"Nguyen","sequence":"additional","affiliation":[{"name":"School of Electrical and Electronic Engineering, Hanoi University of Science and Technology, Vietnam"}]},{"given":"Hai Minh","family":"Pham","sequence":"additional","affiliation":[{"name":"School of Electrical and Electronic Engineering, Hanoi University of Science and Technology, Vietnam"}]}],"member":"320","published-online":{"date-parts":[[2022,4,13]]},"reference":[{"key":"e_1_3_2_1_1_1","volume-title":"Data driven day-ahead electrical load forecasting through repeated wavelet transform assisted SVM model. Appl. Soft Comput. 111, (November","author":"Singh S.N.","year":"2021","unstructured":"Aasim, S.N. Singh , and Abheejeet Mohapatra . 2021. Data driven day-ahead electrical load forecasting through repeated wavelet transform assisted SVM model. Appl. Soft Comput. 111, (November 2021 ), 107730. DOI: https:\/\/doi.org\/10.1016\/j.asoc.2021.107730 10.1016\/j.asoc.2021.107730 Aasim, S.N. Singh, and Abheejeet Mohapatra. 2021. Data driven day-ahead electrical load forecasting through repeated wavelet transform assisted SVM model. Appl. Soft Comput. 111, (November 2021), 107730. DOI: https:\/\/doi.org\/10.1016\/j.asoc.2021.107730"},{"key":"e_1_3_2_1_2_1","volume-title":"Power Load Forecasting Using BiLSTM-Attention. IOP Conf. Ser. Earth Environ. Sci. 440","author":"Du Jie","year":"2020","unstructured":"Jie Du , Yingying Cheng , Quan Zhou , Jiaming Zhang , Xiaoyong Zhang , and Gang Li . 2020 . Power Load Forecasting Using BiLSTM-Attention. IOP Conf. Ser. Earth Environ. Sci. 440 , (March 2020), 032115. DOI: https:\/\/doi.org\/10.1088\/1755-1315\/440\/3\/032115 10.1088\/1755-1315 Jie Du, Yingying Cheng, Quan Zhou, Jiaming Zhang, Xiaoyong Zhang, and Gang Li. 2020. Power Load Forecasting Using BiLSTM-Attention. IOP Conf. Ser. Earth Environ. Sci. 440, (March 2020), 032115. DOI: https:\/\/doi.org\/10.1088\/1755-1315\/440\/3\/032115"},{"key":"e_1_3_2_1_3_1","volume-title":"Pattern-based local linear regression models for short-term load forecasting. Electr. Power Syst. Res. 130, (January","author":"Dudek Grzegorz","year":"2016","unstructured":"Grzegorz Dudek . 2016. Pattern-based local linear regression models for short-term load forecasting. Electr. Power Syst. Res. 130, (January 2016 ), 139\u2013147. DOI: https:\/\/doi.org\/10.1016\/j.epsr.2015.09.001 10.1016\/j.epsr.2015.09.001 Grzegorz Dudek. 2016. Pattern-based local linear regression models for short-term load forecasting. Electr. Power Syst. Res. 130, (January 2016), 139\u2013147. DOI: https:\/\/doi.org\/10.1016\/j.epsr.2015.09.001"},{"key":"e_1_3_2_1_4_1","volume-title":"Convolutional and recurrent neural network based model for short-term load forecasting. Electr. Power Syst. Res. 195, (June","author":"Eskandari Hosein","year":"2021","unstructured":"Hosein Eskandari , Maryam Imani , and Mohsen Parsa Moghaddam . 2021. Convolutional and recurrent neural network based model for short-term load forecasting. Electr. Power Syst. Res. 195, (June 2021 ), 107173. DOI: https:\/\/doi.org\/10.1016\/j.epsr.2021.107173 10.1016\/j.epsr.2021.107173 Hosein Eskandari, Maryam Imani, and Mohsen Parsa Moghaddam. 2021. Convolutional and recurrent neural network based model for short-term load forecasting. Electr. Power Syst. Res. 195, (June 2021), 107173. DOI: https:\/\/doi.org\/10.1016\/j.epsr.2021.107173"},{"key":"e_1_3_2_1_5_1","volume-title":"Electric load forecasting by the SVR model with differential empirical mode decomposition and auto regression. Neurocomputing 173, (January","author":"Fan Guo-Feng","year":"2016","unstructured":"Guo-Feng Fan , Li-Ling Peng , Wei-Chiang Hong , and Fan Sun . 2016. Electric load forecasting by the SVR model with differential empirical mode decomposition and auto regression. Neurocomputing 173, (January 2016 ), 958\u2013970. DOI: https:\/\/doi.org\/10.1016\/j.neucom.2015.08.051 10.1016\/j.neucom.2015.08.051 Guo-Feng Fan, Li-Ling Peng, Wei-Chiang Hong, and Fan Sun. 2016. Electric load forecasting by the SVR model with differential empirical mode decomposition and auto regression. Neurocomputing 173, (January 2016), 958\u2013970. DOI: https:\/\/doi.org\/10.1016\/j.neucom.2015.08.051"},{"key":"e_1_3_2_1_6_1","volume-title":"Energy 81","author":"Ghofrani M.","year":"2015","unstructured":"M. Ghofrani , M. Ghayekhloo , A. Arabali , and A. Ghayekhloo . 2015. A hybrid short-term load forecasting with a new input selection framework . Energy 81 , ( March 2015 ), 777\u2013786. DOI: https:\/\/doi.org\/10.1016\/j.energy.2015.01.028 10.1016\/j.energy.2015.01.028 M. Ghofrani, M. Ghayekhloo, A. Arabali, and A. Ghayekhloo. 2015. A hybrid short-term load forecasting with a new input selection framework. Energy 81, (March 2015), 777\u2013786. DOI: https:\/\/doi.org\/10.1016\/j.energy.2015.01.028"},{"key":"e_1_3_2_1_7_1","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"e_1_3_2_1_8_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neucom.2016.09.027"},{"key":"e_1_3_2_1_9_1","doi-asserted-by":"publisher","DOI":"10.1098\/rspa.1998.0193"},{"key":"e_1_3_2_1_10_1","volume-title":"Kovoor","author":"Jaseena K.U.","year":"2021","unstructured":"K.U. Jaseena and Binsu C . Kovoor . 2021 . Decomposition-based hybrid wind speed forecasting model using deep bidirectional LSTM networks. Energy Convers. Manag . 234, (April 2021), 113944. DOI: https:\/\/doi.org\/10.1016\/j.enconman.2021.113944 10.1016\/j.enconman.2021.113944 K.U. Jaseena and Binsu C. Kovoor. 2021. Decomposition-based hybrid wind speed forecasting model using deep bidirectional LSTM networks. Energy Convers. Manag. 234, (April 2021), 113944. DOI: https:\/\/doi.org\/10.1016\/j.enconman.2021.113944"},{"key":"e_1_3_2_1_11_1","volume-title":"A fuzzy theory-based machine learning method for workdays and weekends short-term load forecasting. Energy Build. 245, (August","author":"Chen Li.","year":"2021","unstructured":"Chen Li. 2021. A fuzzy theory-based machine learning method for workdays and weekends short-term load forecasting. Energy Build. 245, (August 2021 ), 111072. DOI: https:\/\/doi.org\/10.1016\/j.enbuild.2021.111072 10.1016\/j.enbuild.2021.111072 Chen Li. 2021. A fuzzy theory-based machine learning method for workdays and weekends short-term load forecasting. Energy Build. 245, (August 2021), 111072. DOI: https:\/\/doi.org\/10.1016\/j.enbuild.2021.111072"},{"key":"e_1_3_2_1_12_1","volume-title":"Comparison of echo state network and feed-forward neural networks in electrical load forecasting for demand response programs. Math. Comput. Simul. 184, (June","author":"Mansoor Muhammad","year":"2021","unstructured":"Muhammad Mansoor , Francesco Grimaccia , Sonia Leva , and Marco Mussetta . 2021. Comparison of echo state network and feed-forward neural networks in electrical load forecasting for demand response programs. Math. Comput. Simul. 184, (June 2021 ), 282\u2013293. DOI: https:\/\/doi.org\/10.1016\/j.matcom.2020.07.011 10.1016\/j.matcom.2020.07.011 Muhammad Mansoor, Francesco Grimaccia, Sonia Leva, and Marco Mussetta. 2021. Comparison of echo state network and feed-forward neural networks in electrical load forecasting for demand response programs. Math. Comput. Simul. 184, (June 2021), 282\u2013293. DOI: https:\/\/doi.org\/10.1016\/j.matcom.2020.07.011"},{"key":"e_1_3_2_1_13_1","volume-title":"Short-term electricity load and price forecasting by a new optimal LSTM-NN based prediction algorithm. Electr. Power Syst. Res. 192, (March","author":"Memarzadeh Gholamreza","year":"2021","unstructured":"Gholamreza Memarzadeh and Farshid Keynia . 2021. Short-term electricity load and price forecasting by a new optimal LSTM-NN based prediction algorithm. Electr. Power Syst. Res. 192, (March 2021 ), 106995. DOI: https:\/\/doi.org\/10.1016\/j.epsr.2020.106995 10.1016\/j.epsr.2020.106995 Gholamreza Memarzadeh and Farshid Keynia. 2021. Short-term electricity load and price forecasting by a new optimal LSTM-NN based prediction algorithm. Electr. Power Syst. Res. 192, (March 2021), 106995. DOI: https:\/\/doi.org\/10.1016\/j.epsr.2020.106995"},{"key":"#cr-split#-e_1_3_2_1_14_1.1","doi-asserted-by":"crossref","unstructured":"M. Moazzami A. Khodabakhshian and R. Hooshmand. 2013. A new hybrid day-ahead peak load forecasting method for Iran's National Grid. Appl. Energy 101 (January 2013) 489-501. DOI: https:\/\/doi.org\/10.1016\/j.apenergy.2012.06.009 10.1016\/j.apenergy.2012.06.009","DOI":"10.1016\/j.apenergy.2012.06.009"},{"key":"#cr-split#-e_1_3_2_1_14_1.2","doi-asserted-by":"crossref","unstructured":"M. Moazzami A. Khodabakhshian and R. Hooshmand. 2013. A new hybrid day-ahead peak load forecasting method for Iran's National Grid. Appl. Energy 101 (January 2013) 489-501. DOI: https:\/\/doi.org\/10.1016\/j.apenergy.2012.06.009","DOI":"10.1016\/j.apenergy.2012.06.009"},{"key":"e_1_3_2_1_15_1","volume-title":"Short-Term Load Forecasts Using LSTM Networks. Energy Procedia 158, (February","author":"Muzaffar Shahzad","year":"2019","unstructured":"Shahzad Muzaffar and Afshin Afshari . 2019. Short-Term Load Forecasts Using LSTM Networks. Energy Procedia 158, (February 2019 ), 2922\u20132927. DOI: https:\/\/doi.org\/10.1016\/j.egypro.2019.01.952 10.1016\/j.egypro.2019.01.952 Shahzad Muzaffar and Afshin Afshari. 2019. Short-Term Load Forecasts Using LSTM Networks. Energy Procedia 158, (February 2019), 2922\u20132927. DOI: https:\/\/doi.org\/10.1016\/j.egypro.2019.01.952"},{"key":"e_1_3_2_1_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.energy.2008.05.008"},{"key":"e_1_3_2_1_17_1","volume-title":"Appl. Soft Comput. 54","author":"Qiu Xueheng","year":"2017","unstructured":"Xueheng Qiu , Ye Ren , Ponnuthurai Nagaratnam Suganthan , and Gehan A.J. Amaratunga . 2017. Empirical Mode Decomposition based ensemble deep learning for load demand time series forecasting . Appl. Soft Comput. 54 , ( May 2017 ), 246\u2013255. DOI: https:\/\/doi.org\/10.1016\/j.asoc.2017.01.015 10.1016\/j.asoc.2017.01.015 Xueheng Qiu, Ye Ren, Ponnuthurai Nagaratnam Suganthan, and Gehan A.J. Amaratunga. 2017. Empirical Mode Decomposition based ensemble deep learning for load demand time series forecasting. Appl. Soft Comput. 54, (May 2017), 246\u2013255. DOI: https:\/\/doi.org\/10.1016\/j.asoc.2017.01.015"},{"key":"e_1_3_2_1_18_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.enpol.2012.02.064"},{"key":"e_1_3_2_1_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/TPWRS.2019.2963109"},{"key":"e_1_3_2_1_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/ICASSP.2011.5947265"},{"key":"e_1_3_2_1_21_1","volume-title":"Multi-step-ahead electricity load forecasting using a novel hybrid architecture with decomposition-based error correction strategy. Chaos Solitons Fractals 152, (November","author":"Wang Deyun","year":"2021","unstructured":"Deyun Wang , Chenqiang Yue , and Adnen ElAmraoui . 2021. Multi-step-ahead electricity load forecasting using a novel hybrid architecture with decomposition-based error correction strategy. Chaos Solitons Fractals 152, (November 2021 ), 111453. DOI: https:\/\/doi.org\/10.1016\/j.chaos.2021.111453 10.1016\/j.chaos.2021.111453 Deyun Wang, Chenqiang Yue, and Adnen ElAmraoui. 2021. Multi-step-ahead electricity load forecasting using a novel hybrid architecture with decomposition-based error correction strategy. Chaos Solitons Fractals 152, (November 2021), 111453. DOI: https:\/\/doi.org\/10.1016\/j.chaos.2021.111453"},{"key":"e_1_3_2_1_22_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.enconman.2013.02.010"},{"key":"e_1_3_2_1_23_1","doi-asserted-by":"publisher","DOI":"10.1142\/S1793536909000047"},{"key":"e_1_3_2_1_24_1","volume-title":"Sequential grid approach based support vector regression for short-term electric load forecasting. Appl. Energy 238, (March","author":"Yang Youlong","year":"2019","unstructured":"Youlong Yang , Jinxing Che , Chengzhi Deng , and Li Li. 2019. Sequential grid approach based support vector regression for short-term electric load forecasting. Appl. Energy 238, (March 2019 ), 1010\u20131021 DOI: https:\/\/doi.org\/10.1016\/j.apenergy.2019.01.127 10.1016\/j.apenergy.2019.01.127 Youlong Yang, Jinxing Che, Chengzhi Deng, and Li Li. 2019. Sequential grid approach based support vector regression for short-term electric load forecasting. Appl. Energy 238, (March 2019), 1010\u20131021 DOI: https:\/\/doi.org\/10.1016\/j.apenergy.2019.01.127"},{"key":"e_1_3_2_1_25_1","volume-title":"Residential load forecasting based on LSTM fusing self-attention mechanism with pooling. Energy 229, (August","author":"Zang Haixiang","year":"2021","unstructured":"Haixiang Zang , Ruiqi Xu , Lilin Cheng , Tao Ding , Ling Liu , Zhinong Wei , and Guoqiang Sun . 2021. Residential load forecasting based on LSTM fusing self-attention mechanism with pooling. Energy 229, (August 2021 ), 120682. DOI: https:\/\/doi.org\/10.1016\/j.energy.2021.120682 10.1016\/j.energy.2021.120682 Haixiang Zang, Ruiqi Xu, Lilin Cheng, Tao Ding, Ling Liu, Zhinong Wei, and Guoqiang Sun. 2021. Residential load forecasting based on LSTM fusing self-attention mechanism with pooling. Energy 229, (August 2021), 120682. DOI: https:\/\/doi.org\/10.1016\/j.energy.2021.120682"},{"key":"e_1_3_2_1_26_1","volume-title":"Application of variational mode decomposition and chaotic grey wolf optimizer with support vector regression for forecasting electric loads. Knowl.-Based Syst. 228, (September","author":"Zhang Zichen","year":"2021","unstructured":"Zichen Zhang and Wei-Chiang Hong . 2021. Application of variational mode decomposition and chaotic grey wolf optimizer with support vector regression for forecasting electric loads. Knowl.-Based Syst. 228, (September 2021 ), 107297. DOI: https:\/\/doi.org\/10.1016\/j.knosys.2021.107297 10.1016\/j.knosys.2021.107297 Zichen Zhang and Wei-Chiang Hong. 2021. Application of variational mode decomposition and chaotic grey wolf optimizer with support vector regression for forecasting electric loads. Knowl.-Based Syst. 228, (September 2021), 107297. DOI: https:\/\/doi.org\/10.1016\/j.knosys.2021.107297"},{"key":"e_1_3_2_1_27_1","unstructured":"Hyperbolic Tangent Function - an overview ScienceDirect Topics. Retrieved October 26 2021 from https:\/\/www.sciencedirect.com\/topics\/engineering\/hyperbolic-tangent-function  Hyperbolic Tangent Function - an overview ScienceDirect Topics. Retrieved October 26 2021 from https:\/\/www.sciencedirect.com\/topics\/engineering\/hyperbolic-tangent-function"},{"key":"e_1_3_2_1_28_1","unstructured":"Sigmoid Function - an overview | ScienceDirect Topics. Retrieved October 26 2021 from https:\/\/www.sciencedirect.com\/topics\/engineering\/sigmoid-function  Sigmoid Function - an overview | ScienceDirect Topics. Retrieved October 26 2021 from https:\/\/www.sciencedirect.com\/topics\/engineering\/sigmoid-function"}],"event":{"name":"ICFNDS 2021: The 5th International Conference on Future Networks & Distributed Systems","acronym":"ICFNDS 2021","location":"Dubai United Arab Emirates"},"container-title":["The 5th International Conference on Future Networks & Distributed Systems"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3508072.3508079","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3508072.3508079","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T20:12:30Z","timestamp":1750191150000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3508072.3508079"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,15]]},"references-count":29,"alternative-id":["10.1145\/3508072.3508079","10.1145\/3508072"],"URL":"https:\/\/doi.org\/10.1145\/3508072.3508079","relation":{},"subject":[],"published":{"date-parts":[[2021,12,15]]},"assertion":[{"value":"2022-04-13","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}