DocumentCode :
693810
Title :
A Simulator for Intelligent Energy Demand Side Management
Author :
Platt, Glenn ; Ying Guo
Author_Institution :
Div. of Energy Technol., CSIRO, Newcastle, NSW, Australia
fYear :
2013
fDate :
3-5 Dec. 2013
Firstpage :
348
Lastpage :
353
Abstract :
Demand Side Management or DSM refers to the reduction or postponement of energy consumption. Current DSM technology can now provide automated off-site control of domestic and industrial devices. Many questions arise in regards to controlling a potentially large proportion of the population´s electricity: To what level can we reduce demand? What incentives could retailers offer customers? How do we ensure consumers are satisfied? Previous trials of DSM control techniques have had various levels of success in reducing demand and in changing the consumption habits of individuals over time. The main criticism of existing automated control techniques is that they do not account for customer satisfaction and therefore do not survive in the long term. We propose a novel automated machine learning approach that incorporates customer satisfaction into automated demand reduction, satisfying both customers and retailers. Through a simulation of 200,000 households equipped with automated demand control, we conduct experiments measuring electricity levels alongside population satisfaction levels under different energy control policies. We illustrate that significant energy and cost savings can be achieved without compromising consumer satisfaction.
Keywords :
demand side management; learning (artificial intelligence); power engineering computing; DSM control techniques; DSM technology; customer satisfaction; energy consumption; energy control policy; intelligent energy demand side management; machine learning approach; Australia; Customer satisfaction; Electricity; Energy consumption; Generators; Home appliances; Optimization; demand side management; machine learning; reinforcement learning; smart meters consumer satisfaction;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Artificial Intelligence, Modelling and Simulation (AIMS), 2013 1st International Conference on
Conference_Location :
Kota Kinabalu
Print_ISBN :
978-1-4799-3250-4
Type :
conf
DOI :
10.1109/AIMS.2013.64
Filename :
6959942
Link To Document :
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