SustData: A Public Dataset for ICT4S Electric Energy Research
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Filipe Quintal | Lucas Pereira | Nuno Jardim Nunes | Rodolfo Gonçalves | N. Nunes | Filipe Quintal | Lucas Pereira | Rodolfo Gonçalves
[1] Filipe Quintal,et al. WATTSBurning: Design and Evaluation of an Innovative Eco-Feedback System , 2013, INTERACT.
[2] L. J. Becker. Joint effect of feedback and goal setting on performance: a field study of residential energy conservation , 1978 .
[3] Lucas Pereira,et al. Low Cost Framework for Non-intrusive Home Energy Monitoring and Research , 2012, SMARTGREENS.
[4] James A. Landay,et al. The design of eco-feedback technology , 2010, CHI.
[5] P. Holtberg,et al. International Energy Outlook 2016 With Projections to 2040 , 2016 .
[6] Prashant J. Shenoy,et al. Predicting solar generation from weather forecasts using machine learning , 2011, 2011 IEEE International Conference on Smart Grid Communications (SmartGridComm).
[7] S Pacala,et al. Stabilization Wedges: Solving the Climate Problem for the Next 50 Years with Current Technologies , 2004, Science.
[8] J. Zico Kolter,et al. REDD : A Public Data Set for Energy Disaggregation Research , 2011 .
[9] Corinna Fischer. Feedback on household electricity consumption: a tool for saving energy? , 2008 .
[10] Johnny Rodgers,et al. Exploring Ambient and Artistic Visualization for Residential Energy Use Feedback , 2011, IEEE Transactions on Visualization and Computer Graphics.
[11] Filipe Quintal,et al. The design of a hardware-software platform for long-term energy eco-feedback research , 2012, EICS '12.
[12] L. Suganthi,et al. Energy models for demand forecasting—A review , 2012 .
[13] J. Laherrère. International Energy Agency , 2019, Secretary-General's Report to Ministers 2019.
[14] Malcolm I. Heywood,et al. Benchmarking a coevolutionary streaming classifier under the individual household electric power consumption dataset , 2016, 2016 International Joint Conference on Neural Networks (IJCNN).
[15] Derek C. Rose,et al. Deep Machine Learning - A New Frontier in Artificial Intelligence Research [Research Frontier] , 2010, IEEE Computational Intelligence Magazine.
[16] Fred Popowich,et al. AMPds: A public dataset for load disaggregation and eco-feedback research , 2013, 2013 IEEE Electrical Power & Energy Conference.
[17] Jeannie R. Albrecht,et al. Smart * : An Open Data Set and Tools for Enabling Research in Sustainable Homes , 2012 .
[18] Jack Kelly,et al. 'UK-DALE': A dataset recording UK Domestic Appliance-Level Electricity demand and whole-house demand , 2014, ArXiv.
[19] Aoife Foley,et al. Current methods and advances in forecasting of wind power generation , 2012 .
[20] Anthony Rowe,et al. BLUED : A Fully Labeled Public Dataset for Event-Based Non-Intrusive Load Monitoring Research , 2012 .
[21] Mani B. Srivastava,et al. It's Different: Insights into home energy consumption in India , 2013, BuildSys@SenSys.