Data-driven Optimization of Energy Efficiency and Comfort in an Apartment

被引：0
作者：
Avendano, Diego Nieves [1 ]
Ruyssinck, Joeri [1 ]
Vandekerckhove, Steven [2 ]
Van Hoecke, Sofie [1 ]
Deschrijver, Dirk [1 ]
机构：
[1] Univ Ghent, IMEC, IDLab, Ghent, Belgium
[2] Renson Ventilat, Waregem, Belgium
来源：
2018 9TH INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS (IS) | 2018年
关键词：
Model predictive control; Deep reinforcement learning; Multi-objective optimization; Home automation; Energy efficiency; ARTIFICIAL NEURAL-NETWORK; CONTROL-SYSTEM; HVAC SYSTEMS; TIME; PREDICTION;
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
An important challenge in home automation is the energy efficient optimization of the indoor environment. This relies on the solution of a multi-objective optimization problem where energy efficiency and comfort parameters are maximized simultaneously. This paper presents three data-driven control algorithms based on machine learning techniques, which offer an alternative to traditional control methods. The results demonstrate that some data-driven methods can achieve similar results than rule-based systems. Moreover, they require no prior expert knowledge and have better scalability than standard approaches.
引用
收藏
页码：174 / 182
页数：9
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