{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T08:26:39Z","timestamp":1774599999735,"version":"3.50.1"},"publisher-location":"New York, NY, USA","reference-count":21,"publisher":"ACM","license":[{"start":{"date-parts":[[2022,6,28]],"date-time":"2022-06-28T00:00:00Z","timestamp":1656374400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"name":"VMware"},{"name":"NSF","award":["2105494, 2021693, 2020888"],"award-info":[{"award-number":["2105494, 2021693, 2020888"]}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2022,6,28]]},"DOI":"10.1145\/3538637.3538849","type":"proceedings-article","created":{"date-parts":[[2022,6,22]],"date-time":"2022-06-22T16:33:05Z","timestamp":1655915585000},"page":"188-192","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":25,"title":["DACF"],"prefix":"10.1145","author":[{"given":"Diptyaroop","family":"Maji","sequence":"first","affiliation":[{"name":"University of Massachusetts Amherst"}]},{"given":"Ramesh K.","family":"Sitaraman","sequence":"additional","affiliation":[{"name":"University of Massachusetts Amherst"}]},{"given":"Prashant","family":"Shenoy","sequence":"additional","affiliation":[{"name":"University of Massachusetts Amherst"}]}],"member":"320","published-online":{"date-parts":[[2022,6,28]]},"reference":[{"key":"e_1_3_2_1_1_1","unstructured":"Mart\u00edn Abadi Ashish Agarwal Paul Barham Eugene Brevdo Zhifeng Chen Craig Citro Greg S. Corrado Andy Davis Jeffrey Dean Matthieu Devin Sanjay Ghemawat Ian Goodfellow Andrew Harp Geoffrey Irving Michael Isard Yangqing Jia Rafal Jozefowicz Lukasz Kaiser Manjunath Kudlur Josh Levenberg Dan Man\u00e9 Rajat Monga Sherry Moore Derek Murray Chris Olah Mike Schuster Jonathon Shlens Benoit Steiner Ilya Sutskever Kunal Talwar Paul Tucker Vincent Vanhoucke Vijay Vasudevan Fernanda Vi\u00e9gas Oriol Vinyals Pete Warden Martin Wattenberg Martin Wicke Yuan Yu and Xiaoqiang Zheng. 2015. TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. http:\/\/tensorflow.org\/ Software available from tensorflow.org.  Mart\u00edn Abadi Ashish Agarwal Paul Barham Eugene Brevdo Zhifeng Chen Craig Citro Greg S. Corrado Andy Davis Jeffrey Dean Matthieu Devin Sanjay Ghemawat Ian Goodfellow Andrew Harp Geoffrey Irving Michael Isard Yangqing Jia Rafal Jozefowicz Lukasz Kaiser Manjunath Kudlur Josh Levenberg Dan Man\u00e9 Rajat Monga Sherry Moore Derek Murray Chris Olah Mike Schuster Jonathon Shlens Benoit Steiner Ilya Sutskever Kunal Talwar Paul Tucker Vincent Vanhoucke Vijay Vasudevan Fernanda Vi\u00e9gas Oriol Vinyals Pete Warden Martin Wattenberg Martin Wicke Yuan Yu and Xiaoqiang Zheng. 2015. TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems. http:\/\/tensorflow.org\/ Software available from tensorflow.org."},{"key":"e_1_3_2_1_2_1","volume-title":"Real-time Operating Grid. Retrieved","author":"US Energy Information Administration","year":"2022","unstructured":"US Energy Information Administration . 2018. Real-time Operating Grid. Retrieved February 6, 2022 from https:\/\/www.eia.gov\/electricity\/gridmonitor\/dashboard\/electric_overview\/US48\/US48 US Energy Information Administration. 2018. Real-time Operating Grid. Retrieved February 6, 2022 from https:\/\/www.eia.gov\/electricity\/gridmonitor\/dashboard\/electric_overview\/US48\/US48"},{"key":"e_1_3_2_1_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2020.116061"},{"key":"e_1_3_2_1_4_1","volume-title":"UK. Available online: https:\/\/github.com\/carbon-intensity\/methodology\/raw\/master\/Carbon%20Intensity%20Forecast 20","author":"Bruce A","year":"2021","unstructured":"A Bruce , Lyndon Ruff , James Kelloway , Fraser MacMillan , and Alex Rogers . 2021. Carbon intensity forecast methodology. National Grid ESO: Warwick , UK. Available online: https:\/\/github.com\/carbon-intensity\/methodology\/raw\/master\/Carbon%20Intensity%20Forecast 20 ( 2021 ). A Bruce, Lyndon Ruff, James Kelloway, Fraser MacMillan, and Alex Rogers. 2021. Carbon intensity forecast methodology. National Grid ESO: Warwick, UK. Available online: https:\/\/github.com\/carbon-intensity\/methodology\/raw\/master\/Carbon%20Intensity%20Forecast 20 (2021)."},{"key":"e_1_3_2_1_5_1","volume-title":"Open Access Same-time Information System (OASIS). Retrieved","author":"California ISO.","year":"2022","unstructured":"California ISO. 2005--2022. Open Access Same-time Information System (OASIS). Retrieved February 8, 2022 from http:\/\/oasis.caiso.com\/mrioasis\/logon.do California ISO. 2005--2022. Open Access Same-time Information System (OASIS). Retrieved February 8, 2022 from http:\/\/oasis.caiso.com\/mrioasis\/logon.do"},{"key":"e_1_3_2_1_6_1","unstructured":"Fran\u00e7ois Chollet et al. 2015. Keras. https:\/\/keras.io.  Fran\u00e7ois Chollet et al. 2015. Keras. https:\/\/keras.io."},{"key":"e_1_3_2_1_7_1","volume-title":"II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. [Core Writing Team","author":"Change Climate","year":"2022","unstructured":"Climate Change . 2014. Synthesis Report. Contribution of Working Groups I , II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. [Core Writing Team , R.K. Pachauri and L.A. Meyer (eds.)]. IPCC , Geneva, Switzerland , 151 pp. Retrieved February 8, 2022 from https:\/\/archive.ipcc.ch\/pdf\/assessment-report\/ar5\/wg3\/ipcc_wg3_ar5_annex-iii.pdf#page=7 Climate Change. 2014. Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. [Core Writing Team, R.K. Pachauri and L.A. Meyer (eds.)]. IPCC, Geneva, Switzerland, 151 pp. Retrieved February 8, 2022 from https:\/\/archive.ipcc.ch\/pdf\/assessment-report\/ar5\/wg3\/ipcc_wg3_ar5_annex-iii.pdf#page=7"},{"key":"e_1_3_2_1_8_1","volume-title":"Energy and Industrial Startegy","author":"Department of Business","year":"2021","unstructured":"Department of Business , Energy and Industrial Startegy . 2021 . 2021 Government Greenhouse Gas Conversion Factors for Company Reporting . Retrieved April 26, 2022 from https:\/\/assets.publishing.service.gov.uk\/government\/uploads\/system\/uploads\/attachment_data\/file\/1049346\/2021-ghg-conversion-factors-methodology.pdf Department of Business, Energy and Industrial Startegy. 2021. 2021 Government Greenhouse Gas Conversion Factors for Company Reporting. Retrieved April 26, 2022 from https:\/\/assets.publishing.service.gov.uk\/government\/uploads\/system\/uploads\/attachment_data\/file\/1049346\/2021-ghg-conversion-factors-methodology.pdf"},{"key":"e_1_3_2_1_9_1","volume-title":"Zone bounding boxes. Retrieved","year":"2022","unstructured":"ElectricityMap. 2020. Zone bounding boxes. Retrieved February 8, 2022 from https:\/\/github.com\/electricityMap\/electricitymap-contrib\/blob\/master\/config\/zones.json ElectricityMap. 2020. Zone bounding boxes. Retrieved February 8, 2022 from https:\/\/github.com\/electricityMap\/electricitymap-contrib\/blob\/master\/config\/zones.json"},{"key":"e_1_3_2_1_10_1","unstructured":"ElectricityMap. 2022. ElectricityMap. Retrieved February 8 2022 from https:\/\/electricitymap.org\/  ElectricityMap. 2022. ElectricityMap. Retrieved February 8 2022 from https:\/\/electricitymap.org\/"},{"key":"e_1_3_2_1_11_1","volume-title":"ENTSOE transparency platform. Retrieved","author":"European","year":"2022","unstructured":"European association for the cooperation of transmission system operators. 2008. ENTSOE transparency platform. Retrieved February 8, 2022 from https:\/\/transparency.entsoe.eu\/ European association for the cooperation of transmission system operators. 2008. ENTSOE transparency platform. Retrieved February 8, 2022 from https:\/\/transparency.entsoe.eu\/"},{"key":"e_1_3_2_1_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclepro.2020.124766"},{"key":"e_1_3_2_1_13_1","volume-title":"Global Energy & CO2 Status Report 2019: Emissions. Retrieved","author":"International Energy Agency","year":"2022","unstructured":"International Energy Agency . 2019. Global Energy & CO2 Status Report 2019: Emissions. Retrieved February 8, 2022 from https:\/\/www.epa.gov\/ghgemissions\/global-greenhouse-gas-emissions-data International Energy Agency. 2019. Global Energy & CO2 Status Report 2019: Emissions. Retrieved February 8, 2022 from https:\/\/www.epa.gov\/ghgemissions\/global-greenhouse-gas-emissions-data"},{"key":"e_1_3_2_1_14_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.apenergy.2020.115527"},{"key":"e_1_3_2_1_15_1","doi-asserted-by":"publisher","DOI":"10.1177\/0143624418774738"},{"key":"e_1_3_2_1_16_1","volume-title":"The Correct Way to Average the Globe. Retrieved","author":"George Luke","year":"2022","unstructured":"Luke George . 2021. The Correct Way to Average the Globe. Retrieved February 8, 2022 from https:\/\/towardsdatascience.com\/the-correct-way-to-average-the-globe-92ceecd172b7 Luke George. 2021. The Correct Way to Average the Globe. Retrieved February 8, 2022 from https:\/\/towardsdatascience.com\/the-correct-way-to-average-the-globe-92ceecd172b7"},{"key":"e_1_3_2_1_17_1","doi-asserted-by":"publisher","DOI":"10.5065\/D65D8PWK"},{"key":"e_1_3_2_1_18_1","volume-title":"Retrieved","author":"United Satets Environmental Protection Agency.","year":"2021","unstructured":"United Satets Environmental Protection Agency. 2021 . Greenhouse Gas Protocol: Scope 2 Guidance . Retrieved April 26, 2022 from https:\/\/ghgprotocol.org\/sites\/default\/files\/standards\/Scope%202%20Guidance_Final_Sept26.pdf United Satets Environmental Protection Agency. 2021. Greenhouse Gas Protocol: Scope 2 Guidance. Retrieved April 26, 2022 from https:\/\/ghgprotocol.org\/sites\/default\/files\/standards\/Scope%202%20Guidance_Final_Sept26.pdf"},{"key":"e_1_3_2_1_19_1","volume-title":"Global Greenhouse Gas Emissions Data. Retrieved","author":"US Environmental Protection Agency","year":"2022","unstructured":"US Environmental Protection Agency . 2021. Global Greenhouse Gas Emissions Data. Retrieved February 8, 2022 from https:\/\/www.epa.gov\/ghgemissions\/global-greenhouse-gas-emissions-data US Environmental Protection Agency. 2021. Global Greenhouse Gas Emissions Data. Retrieved February 8, 2022 from https:\/\/www.epa.gov\/ghgemissions\/global-greenhouse-gas-emissions-data"},{"key":"e_1_3_2_1_20_1","volume-title":"Sources of Greenhouse Gas Emissions. Retrieved","author":"US Environmental Protection Agency","year":"2022","unstructured":"US Environmental Protection Agency . 2021. Sources of Greenhouse Gas Emissions. Retrieved February 8, 2022 from https:\/\/www.epa.gov\/ghgemissions\/sources-greenhouse-gas-emissions#:~:text=Larger%20image%20to%20save%20or%20print%20The%20Electricity%20sector%20involves,2O)%20are%20also%20emitted. US Environmental Protection Agency. 2021. Sources of Greenhouse Gas Emissions. Retrieved February 8, 2022 from https:\/\/www.epa.gov\/ghgemissions\/sources-greenhouse-gas-emissions#:~:text=Larger%20image%20to%20save%20or%20print%20The%20Electricity%20sector%20involves,2O)%20are%20also%20emitted."},{"key":"e_1_3_2_1_21_1","unstructured":"Watttime. 2022. Watttime. Retrieved February 8 2022 from https:\/\/www.watttime.org\/  Watttime. 2022. Watttime. Retrieved February 8 2022 from https:\/\/www.watttime.org\/"}],"event":{"name":"e-Energy '22: The Thirteenth ACM International Conference on Future Energy Systems","location":"Virtual Event","acronym":"e-Energy '22","sponsor":["SIGEnergy ACM Special Interest Group on Energy Systems and Informatics"]},"container-title":["Proceedings of the Thirteenth ACM International Conference on Future Energy Systems"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3538637.3538849","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3538637.3538849","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3538637.3538849","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,17]],"date-time":"2025-06-17T19:03:01Z","timestamp":1750186981000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3538637.3538849"}},"subtitle":["day-ahead carbon intensity forecasting of power grids using machine learning"],"short-title":[],"issued":{"date-parts":[[2022,6,28]]},"references-count":21,"alternative-id":["10.1145\/3538637.3538849","10.1145\/3538637"],"URL":"https:\/\/doi.org\/10.1145\/3538637.3538849","relation":{},"subject":[],"published":{"date-parts":[[2022,6,28]]},"assertion":[{"value":"2022-06-28","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}