A Simulator for Intelligent Energy Demand Side Management

被引:0
|
作者
Platt, Glenn [1 ]
Guo, Ying [2 ]
机构
[1] CSIRO, Div Energy Technol, Newcastle, NSW, Australia
[2] CSIRO, Computat Informat, Sydney, NSW, Australia
关键词
demand side management; smart meters consumer satisfaction; machine learning; reinforcement learning;
D O I
10.1109/AIMS.2013.64
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.
引用
收藏
页码:348 / 353
页数:6
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