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

页码：174 / 182

页数：9

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