@article {107, title = {A Data-Driven Linear Approximation of HVAC Utilization for Predictive Control and Optimization}, journal = {IEEE Transactions on Control Systems Technology}, volume = {99}, year = {2014}, pages = {1}, doi = {10.1109/TCST.2014.2332873}, url = {http://dx.doi.org/10.1109/TCST.2014.2332873}, author = {Xiao Qin and Susan Lysecky and Jonathan Sprinkle} } @article {214, title = {A Modular Framework to Enable Rapid Evaluation and Exploration of Energy Management Methods in Smart Home Platforms}, journal = {Energy Systems}, year = {2014}, month = {04/2014}, pages = {Online First}, abstract = {

Numerous efforts focus on developing smart grid and smart home plat- forms to provide monitoring, management, and optimization solutions. In order to more effectively manage energy resources, a holistic view is needed; however the involved platforms are complex and require integration of a multitude of parameters such as the end-user behavior, underlying hardware components, environment, etc., many of which operate on varying time scale at various levels of detail. A general and modular framework is presented to enable designers to focus on modeling, simulating, analyzing, or optimizing specific sub-components without requiring a detailed imple- mentation across all levels. We incorporate two case studies in which the proposed framework is utilized to help an end user evaluate platform configurations given an energy usage model, as well as integrate an energy optimization module to investigate rescheduling of appliance usage times in an effort to lower cost.\ 

}, issn = {1868-3967}, doi = {10.1007/s12667-014-0121-9}, url = {http://dx.doi.org/10.1007/s12667-014-0121-9}, author = {Xiao Qin and Lin Lin and Susan Lysecky and Janet Roveda and Young-Jun Son and Jonathan Sprinkle} }