The energy saving potential of retrofitting a smart heating system: A residence hall pilot study
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Ann Nowé | Yannick De Bock | Andres Auquilla | Joost R. Duflou | Ellen Bracquené | A. Nowé | Y. Bock | Andrés Auquilla | J. Duflou | Ellen Bracquené
[1] Therese Peffer,et al. How people use thermostats in homes: A review , 2011, Building and Environment.
[2] Joost Duflou,et al. User Adapting System Design for Improved Energy Efficiency During the Use Phase of Products: Case Study of an Occupancy-Driven, Self-Learning Thermostat , 2015 .
[3] L. H. Shu,et al. Do-it-yourselfers as Lead users for Environmentally Conscious Behavior☆ , 2014 .
[4] Kent Larson,et al. Adding GPS-Control to Traditional Thermostats: An Exploration of Potential Energy Savings and Design Challenges , 2009, Pervasive.
[5] John Krumm,et al. PreHeat: controlling home heating using occupancy prediction , 2011, UbiComp '11.
[6] T. Ferguson. A Bayesian Analysis of Some Nonparametric Problems , 1973 .
[7] Tuan Anh Nguyen,et al. Energy intelligent buildings based on user activity: A survey , 2013 .
[8] Anind K. Dey,et al. TherML: occupancy prediction for thermostat control , 2013, UbiComp.
[9] Jayesh Srivastava,et al. Affordances and Product Design to Support Environmentally Conscious Behavior , 2013 .
[10] T. Nemecek,et al. Overview and methodology: Data quality guideline for the ecoinvent database version 3 , 2013 .
[12] Sanjoy Paul,et al. iSense: a wireless sensor network based conference room management system , 2009, BuildSys '09.
[13] P. O. Fanger,et al. Thermal comfort: analysis and applications in environmental engineering, , 1972 .
[14] Jayaran Sethuramant. A CONSTRUCTIVE DEFINITION OF DIRICHLET PRIORS , 1991 .
[15] Tomohiko Sakao,et al. Design for reduced resource consumption during the use phase of products , 2017 .
[16] Mark W. Newman,et al. Learning from a learning thermostat: lessons for intelligent systems for the home , 2013, UbiComp.
[17] Silvia Santini,et al. Predicting household occupancy for smart heating control: A comparative performance analysis of state-of-the-art approaches , 2014 .
[18] Sarvapali D. Ramchurn,et al. Forecasting Multi-Appliance Usage for Smart Home Energy Management , 2013, IJCAI.
[19] Standard Ashrae. Thermal Environmental Conditions for Human Occupancy , 1992 .
[20] Jochen Schmidt,et al. Appliance Usage Prediction for the Smart Home with an Application to Energy Demand Side Management - And Why Accuracy is not a Good Performance Metric for this Problem , 2017, SMARTGREENS.
[21] C. Antoniak. Mixtures of Dirichlet Processes with Applications to Bayesian Nonparametric Problems , 1974 .
[22] Hojung Cha,et al. Occupancy Prediction Algorithms for Thermostat Control Systems Using Mobile Devices , 2013, IEEE Transactions on Smart Grid.
[23] Alberto E. Cerpa,et al. POEM: Power-efficient occupancy-based energy management system , 2013, 2013 ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN).
[24] Mark Modera,et al. Do occupancy-responsive learning thermostats save energy? A field study in university residence halls , 2016 .
[25] Ann Nowé,et al. Nonparametric user activity modelling and prediction , 2020, User Modeling and User-Adapted Interaction.
[26] John Krumm,et al. Learning Time-Based Presence Probabilities , 2011, Pervasive.
[27] Lancelot F. James,et al. Gibbs Sampling Methods for Stick-Breaking Priors , 2001 .
[28] János Unger,et al. The most problematic variable in the course of human-biometeorological comfort assessment — the mean radiant temperature , 2011 .
[29] Kamin Whitehouse,et al. The smart thermostat: using occupancy sensors to save energy in homes , 2010, SenSys '10.
[30] Joost R. Duflou,et al. Intelligent occupancy-driven thermostat by dynamic user profiling , 2016, 2016 Electronics Goes Green 2016+ (EGG).
[31] M. Muggeo,et al. segmented: An R package to Fit Regression Models with Broken-Line Relationships , 2008 .
[32] Michael C. Mozer,et al. The Neurothermostat: Predictive Optimal Control of Residential Heating Systems , 1996, NIPS.
[33] Rajiv T. Maheswaran,et al. Improving building energy efficiency with a network of sensing, learning and prediction agents , 2012, AAMAS.
[34] Joost Duflou,et al. Impact reduction potential by usage anticipation under comfort trade-off conditions , 2016 .