A real-time carbon emission estimation framework for industrial parks with non-intrusive load monitoring
Tài liệu tham khảo
Yu, 2020, An emissions accounting framework for industrial parks in China, J Clean Prod, 244
Dong, 2013, Carbon footprint evaluation at industrial park level: A hybrid life cycle assessment approach, Energy Policy, 57, 298, 10.1016/j.enpol.2013.01.057
Meinshausen, 2009, Greenhouse-gas emission targets for limiting global warming to 2 ℃, Nature, 458, 1158, 10.1038/nature08017
Berndes, 2016, Forest biomass, carbon neutrality, and climate change mitigation, From Sci Policy, 3, 3
Nisbet, 1979, Top-down versus bottom-up, Science, 2010, 1241
Pan, 2021, The potential of CO2 satellite monitoring for climate governance: a review, J Environ Manage, 277
Bo, 2020, The spatial-temporal pattern of sintered flue gas emissions in iron and steel enterprises of China, J Clean Prod, 266, 121667, 10.1016/j.jclepro.2020.121667
Liu, 2020, A methodology to constrain carbon dioxide emissions from coal-fired power plants using satellite observations of co-emitted nitrogen dioxide, Atmos Chem Phys, 20, 99, 10.5194/acp-20-99-2020
Navarro, 2017, Product vs corporate carbon footprint: Some methodological issues. A case study and review on the wine sector, Sci Total Environ, 581-582, 722, 10.1016/j.scitotenv.2016.12.190
Radu, 2013, Carbon footprint analysis: towards a projects evaluation model for promoting sustainable development, Procedia Econ Finance, 6, 353, 10.1016/S2212-5671(13)00149-4
Liu, 2022, Real-time carbon emission accounting technology toward carbon neutrality, Engineering
Eggleston S, Buendia L, Miwa K, Ngara T, Tanabe K. 2006 IPCC guidelines for national greenhouse gas inventories 2006.
Intergovernmental Panel On Climate Change. IPCC Emission Factor Database. Environmental Protection 2007.
U.S. Environmental Protection Agency. Emission Factors for Greenhouse Gas Inventories 2015.
Hong, 1994, Carbon dioxide emission factors for coal, Quarterly Coal Rep
Hart GW. Nonintrusive appliance load monitoring. Proc IEEE 1992;80:1870–91. https://doi.org/10.1109/5.192069.
Zoha, 2012, Non-intrusive Load Monitoring approaches for disaggregated energy sensing: a survey, Sensors (Switzerland), 12, 16838, 10.3390/s121216838
Klemenjak C, Goldsborough P. Non-intrusive load monitoring: A review and outlook. ArXiv Preprint ArXiv:161001191 2016.
Batra N, Dutta H, Singh A. Indic: Improved non-intrusive load monitoring using load division and calibration. 2013 12th International Conference on Machine Learning and Applications, vol. 1, 2013, p. 79–84.
Kolter, 2012, Approximate inference in additive factorial hmms with application to energy disaggregation, Artificial Intellig Statist, 1472
Parson O, Ghosh S, Weal M, Rogers A. Non-intrusive load monitoring using prior models of general appliance types. Twenty-Sixth AAAI Conference on Artificial Intelligence 2012.
Nalmpantis, 2019, Machine learning approaches for non-intrusive load monitoring: from qualitative to quantitative comparation, Artif Intell Rev, 52, 217, 10.1007/s10462-018-9613-7
Figueiredo, 2012, Home electrical signal disaggregation for non-intrusive load monitoring (NILM) systems, Neurocomputing, 96, 66, 10.1016/j.neucom.2011.10.037
Chang, 2013, Power-spectrum-based wavelet transform for nonintrusive demand monitoring and load identification, IEEE Trans Ind Appl, 50, 2081, 10.1109/TIA.2013.2283318
Batra N, Kelly J, Parson O, Dutta H, Knottenbelt W, Rogers A, et al. NILMTK: An open source toolkit for non-intrusive load monitoring. Proceedings of the 5th ACM International Conference on Future Energy Systems 2014:265–76.
Kelly J, Knottenbelt W. Neural nilm: Deep neural networks applied to energy disaggregation. Proceedings of the 2nd ACM International Conference on Embedded Systems for Energy-Efficient Built Environments, 2015, p. 55–64.
Zhang, 2019, Non-intrusive load monitoring based on convolutional neural network with differential input, Procedia CIRP, 83, 670, 10.1016/j.procir.2019.04.110
Yang, 2020, An event-driven convolutional neural architecture for non-intrusive load monitoring of residential appliance, IEEE Trans Consum Electron, 66, 173, 10.1109/TCE.2020.2977964
Mauch, 2015, A new approach for supervised power disaggregation by using a deep recurrent LSTM network, 63
Kim, 2019, Appliance classification by power signal analysis based on multi-feature combination multi-layer LSTM, Energies (Basel), 12, 2804, 10.3390/en12142804
Sirojan, 2018, Deep neural network based energy disaggregation, 73
Kaselimi M, Doulamis N. Long-term recurrent convolutional networks for non-intrusive load monitoring. Proceedings of the 13th ACM International Conference on PErvasive Technologies Related to Assistive Environments, 2020, p. 1–4.
Liu, 2020, Real-time carbon emission monitoring in prefabricated construction, Autom Constr, 110, 10.1016/j.autcon.2019.102945
Zhang Q. An approach to rough set decomposition of incomplete information systems. ICIEA 2007: 2007 Second IEEE Conference on Industrial Electronics and Applications 2007:2455–60. https://doi.org/10.1109/ICIEA.2007.4318851.
Faustine, 2020, Adaptive weighted recurrence graphs for appliance recognition in non-intrusive load monitoring, IEEE Trans Smart Grid, 3053, 1
Passricha V, Aggarwal RK. A Hybrid of Deep CNN and bidirectional LSTM for automatic speech recognition 2020;29:1261–74.
Dozat T. Incorporating nesterov momentum into adam 2016.
Zimmerman, 1997, 10
Faustine A, Mvungi NH, Kaijage S, Michael K. A survey on non-intrusive load monitoring methodies and techniques for energy disaggregation problem. ArXiv 2017.
Dollár P, Singh M, Girshick R. Fast and Accurate Model Scaling 2021:924–32.
GB/T 32151 (All Parts) Requirements of the Greenhouse Gas Emission Accounting and Reporting 2015.