{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T10:22:42Z","timestamp":1743157362568,"version":"3.40.3"},"publisher-location":"Cham","reference-count":32,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030611040"},{"type":"electronic","value":"9783030611057"}],"license":[{"start":{"date-parts":[[2020,10,9]],"date-time":"2020-10-09T00:00:00Z","timestamp":1602201600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2020,10,9]],"date-time":"2020-10-09T00:00:00Z","timestamp":1602201600000},"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\/978-3-030-61105-7_41","type":"book-chapter","created":{"date-parts":[[2020,10,8]],"date-time":"2020-10-08T09:03:40Z","timestamp":1602147820000},"page":"410-419","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Forecasting Electricity Consumption Using Weather Data in an Edge-Fog-Cloud Data Analytics Architecture"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5302-1786","authenticated-orcid":false,"given":"Juan C.","family":"Olivares-Rojas","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3374-0059","authenticated-orcid":false,"given":"Enrique","family":"Reyes-Archundia","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7898-604X","authenticated-orcid":false,"given":"Jos\u00e9 A.","family":"Guti\u00e9rrez-Gnecchi","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8310-6040","authenticated-orcid":false,"given":"Ismael","family":"Molina-Moreno","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7561-5673","authenticated-orcid":false,"given":"Arturo","family":"M\u00e9ndez-Pati\u00f1o","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6542-4718","authenticated-orcid":false,"given":"Jaime","family":"Cerda-Jacobo","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2020,10,9]]},"reference":[{"issue":"2","key":"41_CR1","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1016\/j.jsis.2019.01.003","volume":"28","author":"G Vial","year":"2019","unstructured":"Vial, G.: Understanding digital transformation: a review and a research agenda. J. Strateg. Inf. Syst. 28(2), 118\u2013144 (2019). \nhttps:\/\/doi.org\/10.1016\/j.jsis.2019.01.003","journal-title":"J. Strateg. Inf. Syst."},{"key":"41_CR2","doi-asserted-by":"publisher","first-page":"2589","DOI":"10.1016\/j.renene.2019.08.092","volume":"146","author":"G Dileep","year":"2020","unstructured":"Dileep, G.: A survey on smart grid technologies and applications. Renew. Energy 146, 2589\u20132625 (2020). \nhttps:\/\/doi.org\/10.1016\/j.renene.2019.08.092","journal-title":"Renew. Energy"},{"key":"41_CR3","doi-asserted-by":"publisher","unstructured":"Borovina, D., et al.: Error performance analysis and modeling of narrow-band PLC technology enabling smart metering systems. Int. J. Electr. Power Energy Syst. 116 (2019). \nhttps:\/\/doi.org\/10.1016\/j.ijepes.2019.105536","DOI":"10.1016\/j.ijepes.2019.105536"},{"key":"41_CR4","doi-asserted-by":"publisher","unstructured":"Balaji, J., et al.: Machine learning approaches to electricity consumption forecasting in automated metering infrastructure (AMI) systems: an empirical study. In: Silhavy, R., Senkerik, R., Kominkova Oplatkova, Z., Prokopova, Z., Silhavy, P. (eds.) Cybernetics and Mathematics Applications in Intelligent Systems. CSOC 2017. Advances in Intelligent Systems and Computing, vol. 574. Springer (2017). \nhttps:\/\/doi.org\/10.1007\/978-3-319-57264-2_26","DOI":"10.1007\/978-3-319-57264-2_26"},{"key":"41_CR5","doi-asserted-by":"publisher","unstructured":"Rokan, B., Kotb, Y.: Towards a real IoT-based smart meter system. In: Luhach, A., Kosa, J., Poonia, R., Gao, X.Z., Singh, D. (eds.) First International Conference on Sustainable Technologies for Computational Intelligence. Advances in Intelligent Systems and Computing, vol. 1045. Springer (2020). \nhttps:\/\/doi.org\/10.1007\/978-981-15-0029-9_11","DOI":"10.1007\/978-981-15-0029-9_11"},{"key":"41_CR6","doi-asserted-by":"publisher","unstructured":"Adam, A., et al.: The fog cloud of things: a survey on concepts, architecture, standards, tools, and applications. Internet Things 9 (2020). \nhttps:\/\/doi.org\/10.1016\/j.iot.2020.100177","DOI":"10.1016\/j.iot.2020.100177"},{"key":"41_CR7","doi-asserted-by":"publisher","unstructured":"Forcan, M., Maksimovi\u0107, M.: Cloud-fog-based approach for smart grid monitoring. Simul. Model. Pract. Theory 101 (2020). \nhttps:\/\/doi.org\/10.1016\/j.simpat.2019.101988","DOI":"10.1016\/j.simpat.2019.101988"},{"key":"41_CR8","doi-asserted-by":"crossref","unstructured":"Dehalwar, V.: Electricity load forecasting for urban area using weather forecast information. In: 2016 IEEE International Conference on Power and Renewable Energy (ICPRE), Shanghai, pp. 355\u2013359 (2016). \nhttp:\/\/doi.org\/10.1109\/ICPRE.2016.7871231","DOI":"10.1109\/ICPRE.2016.7871231"},{"key":"41_CR9","doi-asserted-by":"crossref","unstructured":"Zeng, Q., et al.: An optimum regression approach for analyzing weather influence on the energy consumption. In: 2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), Beijing, pp. 1\u20136 (2016). \nhttp:\/\/doi.org\/10.1109\/PMAPS.2016.7764178","DOI":"10.1109\/PMAPS.2016.7764178"},{"issue":"4","key":"41_CR10","doi-asserted-by":"publisher","first-page":"2078","DOI":"10.1109\/TPWRS.2005.857397","volume":"20","author":"C Hor","year":"2005","unstructured":"Hor, C., et al.: Analyzing the impact of weather variables on monthly electricity demand. IEEE Trans. Power Syst. 20(4), 2078\u20132085 (2005). \nhttps:\/\/doi.org\/10.1109\/TPWRS.2005.857397","journal-title":"IEEE Trans. Power Syst."},{"key":"41_CR11","doi-asserted-by":"crossref","unstructured":"Prabakar, A., et al.: Applying machine learning to study the relationship between electricity consumption and weather variables using open data. In: 2018 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe), Sarajevo, pp. 1\u20136 (2018). \nhttp:\/\/doi.org\/10.1109\/ISGTEurope.2018.8571430","DOI":"10.1109\/ISGTEurope.2018.8571430"},{"issue":"2","key":"41_CR12","doi-asserted-by":"publisher","first-page":"695","DOI":"10.1016\/j.ijforecast.2019.08.008","volume":"36","author":"S Moreno-Carbonell","year":"2020","unstructured":"Moreno-Carbonell, S., et al.: Rethinking weather station selection for electric load forecasting using genetic algorithms. Int. J. Forecast. 36(2), 695\u2013712 (2020). \nhttps:\/\/doi.org\/10.1016\/j.ijforecast.2019.08.008","journal-title":"Int. J. Forecast."},{"key":"41_CR13","doi-asserted-by":"publisher","first-page":"265","DOI":"10.1016\/j.apenergy.2018.06.087","volume":"228","author":"A Ag\u00fcera-P\u00e9rez","year":"2018","unstructured":"Ag\u00fcera-P\u00e9rez, A., et al.: Weather forecasts for microgrid energy management: review, discussion and recommendations. Appl. Energy 228, 265\u2013278 (2018). \nhttps:\/\/doi.org\/10.1016\/j.apenergy.2018.06.087","journal-title":"Appl. Energy"},{"key":"41_CR14","doi-asserted-by":"crossref","unstructured":"Jose, D., et al.: Weather dependency of electricity demand: a case study in warm humid tropical climate. In: 2016 3rd International Conference on Electrical Energy Systems (ICEES), Chennai, pp. 102\u2013105 (2016). \nhttp:\/\/doi.org\/10.1109\/ICEES.2016.7510624","DOI":"10.1109\/ICEES.2016.7510624"},{"key":"41_CR15","doi-asserted-by":"crossref","unstructured":"Rusina, A., et al.: Short-term electricity consumption forecast in Siberia IPS using climate aspects. In: 2018 19th International Conference of Young Specialists on Micro\/Nanotechnologies and Electron Devices (EDM), Erlagol, pp. 6403\u20136407 (2018). \nhttp:\/\/doi.org\/10.1109\/EDM.2018.8435002","DOI":"10.1109\/EDM.2018.8435002"},{"key":"41_CR16","unstructured":"Parkpoom, S., et al.: Climate change impacts on electricity demand. In: 39th International Universities Power Engineering Conference. UPEC 2004, Bristol, UK, 2004, vol. 2, pp. 1342\u20131346 (2004). \nhttps:\/\/ieeexplore.ieee.org\/abstract\/document\/1492245"},{"issue":"3","key":"41_CR17","doi-asserted-by":"publisher","first-page":"1441","DOI":"10.1109\/TPWRS.2008.922254","volume":"23","author":"S Parkpoom","year":"2008","unstructured":"Parkpoom, S., Harrison, G.: Analyzing the impact of climate change on future electricity demand in Thailand. IEEE Trans. Power Syst. 23(3), 1441\u20131448 (2008). \nhttps:\/\/doi.org\/10.1109\/TPWRS.2008.922254","journal-title":"IEEE Trans. Power Syst."},{"key":"41_CR18","doi-asserted-by":"crossref","unstructured":"Shakouri, H., Nadimi, R., et al.: Investigation on the short-term variations of electricity demand due to the climate changes via a hybrid TSK-FR model. In: 2007 IEEE International Conference on Industrial Engineering and Engineering Management, Singapore, pp. 807\u2013811 (2007). \nhttp:\/\/doi.org\/10.1109\/IEEM.2007.4419302","DOI":"10.1109\/IEEM.2007.4419302"},{"key":"41_CR19","doi-asserted-by":"crossref","unstructured":"Gastli, A., et al.: Correlation between climate data and maximum electricity demand in Qatar. In: 2013 7th IEEE GCC Conference and Exhibition (GCC), Doha, pp. 565\u2013570 (2013). \nhttp:\/\/doi.org\/10.1109\/IEEEGCC.2013.6705841","DOI":"10.1109\/IEEEGCC.2013.6705841"},{"key":"41_CR20","doi-asserted-by":"crossref","unstructured":"Fidalgo, J., et al.: Impact of climate changes on the Portuguese energy generation mix. In: 2019 16th International Conference on the European Energy Market (EEM), Ljubljana, Slovenia, pp. 1\u20136 (2019). \nhttp:\/\/doi.org\/10.1109\/EEM.2019.8916539","DOI":"10.1109\/EEM.2019.8916539"},{"issue":"2","key":"41_CR21","doi-asserted-by":"publisher","first-page":"744","DOI":"10.1016\/j.enpol.2009.10.019","volume":"38","author":"T Zachariadis","year":"2010","unstructured":"Zachariadis, T.: Forecast of electricity consumption in Cyprus up to the year 2030: the potential impact of climate change. Energy Policy 38(2), 744\u2013750 (2010). \nhttps:\/\/doi.org\/10.1016\/j.enpol.2009.10.019","journal-title":"Energy Policy"},{"key":"41_CR22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.apenergy.2018.11.039","volume":"236","author":"D Burillo","year":"2019","unstructured":"Burillo, D., et al.: Forecasting peak electricity demand for Los Angeles considering higher air temperatures due to climate change. Appl. Energy 236, 1\u20139 (2019). \nhttps:\/\/doi.org\/10.1016\/j.apenergy.2018.11.039","journal-title":"Appl. Energy"},{"key":"41_CR23","doi-asserted-by":"publisher","first-page":"756","DOI":"10.1016\/j.egypro.2018.09.241","volume":"152","author":"G Li","year":"2018","unstructured":"Li, G., et al.: Relations of total electricity consumption to climate change in Nanjing. Energy Procedia 152, 756\u2013761 (2018). \nhttps:\/\/doi.org\/10.1016\/j.egypro.2018.09.241","journal-title":"Energy Procedia"},{"key":"41_CR24","doi-asserted-by":"publisher","unstructured":"Ahmad, T., et al.: Smart energy forecasting strategy with four machine learning models for climate-sensitive and non-climate sensitive conditions. Energy 198 (2020). \nhttps:\/\/doi.org\/10.1016\/j.energy.2020.117283","DOI":"10.1016\/j.energy.2020.117283"},{"key":"41_CR25","doi-asserted-by":"publisher","first-page":"1259","DOI":"10.1007\/s11069-019-03653-w","volume":"99","author":"C Zhang","year":"2019","unstructured":"Zhang, C., Liao, H., Mi, Z.: Climate impacts: temperature and electricity consumption. Nat. Hazards 99, 1259\u20131275 (2019). \nhttps:\/\/doi.org\/10.1007\/s11069-019-03653-w","journal-title":"Nat. Hazards"},{"key":"41_CR26","doi-asserted-by":"publisher","first-page":"65","DOI":"10.1016\/j.energy.2017.12.051","volume":"145","author":"I Staffell","year":"2018","unstructured":"Staffell, I., Pfenninger, S.: The increasing impact of weather on electricity supply and demand. Energy 145, 65\u201378 (2018). \nhttps:\/\/doi.org\/10.1016\/j.energy.2017.12.051","journal-title":"Energy"},{"key":"41_CR27","doi-asserted-by":"publisher","unstructured":"Aslam, Z., et al.: An enhanced convolutional neural network model based on weather parameters for short-term electricity supply and demand. In: Barolli, L., Amato, F., Moscato, F., Enokido, T., Takizawa, M. (eds.) Advanced Information Networking and Applications. AINA 2020. Advances in Intelligent Systems and Computing, vol. 1151. Springer (2020). \nhttps:\/\/doi.org\/10.1007\/978-3-030-44041-1_3","DOI":"10.1007\/978-3-030-44041-1_3"},{"key":"41_CR28","doi-asserted-by":"publisher","first-page":"657","DOI":"10.1016\/j.proeng.2015.12.087","volume":"129","author":"I Nadtoka","year":"2015","unstructured":"Nadtoka, I., Al-Zihery, A.: Mathematical modelling and short-term forecasting of electricity consumption of the power system, with due account of air temperature and natural illumination based on support vector machine and particle swarm. Procedia Eng. 129, 657\u2013663 (2015). \nhttps:\/\/doi.org\/10.1016\/j.proeng.2015.12.087","journal-title":"Procedia Eng."},{"key":"41_CR29","doi-asserted-by":"publisher","first-page":"200","DOI":"10.1016\/j.resconrec.2016.01.016","volume":"123","author":"H Son","year":"2017","unstructured":"Son, H., Kim, C.: Short-term forecasting of electricity demand for the residential sector using weather and social variables. Resour. Conserv. Recycl. 123, 200\u2013207 (2017). \nhttps:\/\/doi.org\/10.1016\/j.resconrec.2016.01.016","journal-title":"Resour. Conserv. Recycl."},{"key":"41_CR30","doi-asserted-by":"publisher","first-page":"435","DOI":"10.1016\/j.apenergy.2014.10.030","volume":"137","author":"M De Felice","year":"2015","unstructured":"De Felice, M., et al.: Seasonal climate forecasts for medium-term electricity demand forecasting. Appl. Energy 137, 435\u2013444 (2015). \nhttps:\/\/doi.org\/10.1016\/j.apenergy.2014.10.030","journal-title":"Appl. Energy"},{"key":"41_CR31","doi-asserted-by":"publisher","first-page":"435","DOI":"10.1016\/j.future.2018.06.042","volume":"90","author":"X Fei","year":"2019","unstructured":"Fei, X., et al.: CPS data streams analytics based on machine learning for Cloud and Fog Computing: a survey. Future Gener. Comput. Syst. 90, 435\u2013450 (2019). \nhttps:\/\/doi.org\/10.1016\/j.future.2018.06.042","journal-title":"Future Gener. Comput. Syst."},{"key":"41_CR32","doi-asserted-by":"publisher","unstructured":"Spiliotis, E., et al.: Cross-temporal aggregation: Improving the forecast accuracy of hierarchical electricity consumption. Appl. Energy 261 (2020). \nhttps:\/\/doi.org\/10.1016\/j.apenergy.2019.114339","DOI":"10.1016\/j.apenergy.2019.114339"}],"container-title":["Lecture Notes in Networks and Systems","Advances on P2P, Parallel, Grid, Cloud and Internet Computing"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-61105-7_41","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,10,8]],"date-time":"2020-10-08T09:25:24Z","timestamp":1602149124000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-61105-7_41"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,10,9]]},"ISBN":["9783030611040","9783030611057"],"references-count":32,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-61105-7_41","relation":{},"ISSN":["2367-3370","2367-3389"],"issn-type":[{"type":"print","value":"2367-3370"},{"type":"electronic","value":"2367-3389"}],"subject":[],"published":{"date-parts":[[2020,10,9]]},"assertion":[{"value":"9 October 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"3PGCIC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on P2P, Parallel, Grid, Cloud and Internet Computing","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Yonago","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Japan","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":"28 October 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 October 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pgcic2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/voyager.ce.fit.ac.jp\/conf\/3pgcic\/2020\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}}]}}