Real-Time Energy Disaggregation of a Distribution Feeder's Demand Using Online Learning
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[1] Johanna L. Mathieu,et al. Characterizing the Response of Commercial and Industrial Facilities to Dynamic Pricing Signals From the Utility , 2010 .
[2] Joshua A. Taylor,et al. Index Policies for Demand Response , 2014, IEEE Transactions on Power Systems.
[3] Ram Rajagopal,et al. Online learning for demand response , 2015, 2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton).
[4] Mark Herbster,et al. Tracking the Best Expert , 1995, Machine Learning.
[5] Ward Jewell,et al. Impact of load type on power consumption and line loss in voltage reduction program , 2013, 2013 North American Power Symposium (NAPS).
[6] Duncan S. Callaway,et al. Arbitraging Intraday Wholesale Energy Market Prices With Aggregations of Thermostatic Loads , 2015, IEEE Transactions on Power Systems.
[7] Nikos D. Hatziargyriou,et al. Machine Learning Applications to Power Systems , 2001, Machine Learning and Its Applications.
[8] Lucio Soibelman,et al. Enhancing Electricity Audits in Residential Buildings with Nonintrusive Load Monitoring , 2010 .
[9] James W. Taylor. An evaluation of methods for very short-term load forecasting using minute-by-minute British data , 2008 .
[10] Shuai Lu,et al. Development and Validation of Aggregated Models for Thermostatic Controlled Loads with Demand Response , 2012, 2012 45th Hawaii International Conference on System Sciences.
[11] Eilyan Bitar,et al. Risk-Sensitive Learning and Pricing for Demand Response , 2016, IEEE Transactions on Smart Grid.
[12] Duncan S. Callaway,et al. State Estimation and Control of Electric Loads to Manage Real-Time Energy Imbalance , 2013, IEEE Transactions on Power Systems.
[13] Prashant J. Shenoy,et al. Predicting solar generation from weather forecasts using machine learning , 2011, 2011 IEEE International Conference on Smart Grid Communications (SmartGridComm).
[14] Gregory S. Ledva,et al. Inferring the behavior of distributed energy resources with online learning , 2015, 2015 53rd Annual Allerton Conference on Communication, Control, and Computing (Allerton).
[15] Tao Hong,et al. Probabilistic electric load forecasting: A tutorial review , 2016 .
[16] Rebecca Willett,et al. Online Convex Optimization in Dynamic Environments , 2015, IEEE Journal of Selected Topics in Signal Processing.
[17] Paras Mandal,et al. Machine Learning Applications for Load, Price and Wind Power Prediction in Power Systems , 2009, 2009 15th International Conference on Intelligent System Applications to Power Systems.
[18] Antoine Lesage-Landry,et al. Learning to Shift Thermostatically Controlled Loads , 2017, HICSS.
[19] Abhay Gupta,et al. Is disaggregation the holy grail of energy efficiency? The case of electricity , 2013 .
[20] Desheng Dash Wu,et al. Power load forecasting using support vector machine and ant colony optimization , 2010, Expert Syst. Appl..
[21] Ming-Guang Zhang,et al. Short-term load forecasting based on support vector machines regression , 2005, 2005 International Conference on Machine Learning and Cybernetics.
[22] Yan Xu,et al. Local voltage support from distributed energy resources to prevent air conditioner motor stalling , 2010, 2010 Innovative Smart Grid Technologies (ISGT).
[23] Kevin P. Schneider,et al. Modern Grid Initiative Distribution Taxonomy Final Report , 2008 .
[24] Gabriela Hug,et al. A moving horizon state estimator in the control of thermostatically controlled loads for demand response , 2013, 2013 IEEE International Conference on Smart Grid Communications (SmartGridComm).
[25] Alessandro Abate,et al. Aggregation and Control of Populations of Thermostatically Controlled Loads by Formal Abstractions , 2015, IEEE Transactions on Control Systems Technology.
[26] Tao Hong,et al. Short Term Electric Load Forecasting , 2012 .
[27] Bart De Schutter,et al. Residential Demand Response of Thermostatically Controlled Loads Using Batch Reinforcement Learning , 2017, IEEE Transactions on Smart Grid.
[28] T. Logenthiran,et al. Forecasting of photovoltaic power using extreme learning machine , 2015, 2015 IEEE Innovative Smart Grid Technologies - Asia (ISGT ASIA).