###
计算机系统应用:2019,28(1):156-162
←前一篇   |   后一篇→
本文二维码信息
码上扫一扫!
基于Q-Learning算法的建筑能耗预测
陈建平1,2,3, 陈其强1,2,3, 胡文1,2,3, 陆悠1,2,3, 吴宏杰1,2,3, 傅启明1,2,3
(1.苏州科技大学 电子与信息工程学院, 苏州 215009;2.
江苏省建筑智慧节能重点实验室, 苏州 215009;3.
苏州市移动网络技术与应用重点实验室, 苏州 215009)
Prediction of Building Energy Consumption Based on Q-Learning
CHEN Jian-Ping1,2,3, CHEN Qi-Qiang1,2,3, HU Wen1,2,3, LU You1,2,3, WU Hong-Jie1,2,3, FU Qi-Ming1,2,3
(1.College of Electronics and Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, China;2.
Jiangsu Key Laboratory of Building Intelligent Energy Saving, Suzhou 215009, China;3.
Suzhou Key Laboratory of Mobile Network Technology and Application, Suzhou 215009, China)
摘要
图/表
参考文献
相似文献
本文已被:浏览 64次   下载 59
投稿时间:2018-07-31    修订日期:2018-08-27
中文摘要: 提出一种基于Q-learning算法的建筑能耗预测方法.通过将建筑能耗预测问题建模为一个标准的马尔科夫决策过程,利用深度置信网对建筑能耗进行状态建模,结合Q-learning算法,实现对建筑能耗的实时预测.通过美国巴尔的摩燃气和电力公司公开的建筑能耗数据进行测试实验,结果表明,基于本文所提出的模型,利用Q-learning算法可以实现对建筑能耗的有效预测,并在此基础上,基于深度置信网的Q-learning算法具有更高的预测精度.此外,实验部分还进一步验证了算法中相关参数对实验性能的影响.
Abstract:This study proposed a building energy consumption prediction method based on Q-learning algorithm. By modeling the building energy consumption prediction problem as a standard Markov decision process, combining with the deep belief network to model the state, we use Q-learning algorithm to achieve the real-time prediction of the building energy consumption. Based on the building energy consumption data published by Baltimore Gas and Electric Power Company of the United States, the proposed model were tested and the results show that the Q-learning algorithm can be used to predict the building energy consumption successfully. Moreover, deep belief network can improve the prediction accuracy effectively. In addition, some experimental results further verify the influence of related parameters on experimental performance.
文章编号:     中图分类号:    文献标志码:
基金项目:国家自然科学基金(61502329,61876121,61772357,61750110519,61672371,61602334,61472267);江苏省重点研发计划(BE2017663);江苏省高校自然科学研究项目(18KJB520045);江苏省建设系统科技指导项目(2017ZD005)
Author NameAffiliationE-mail
CHEN Jian-Ping College of Electronics and Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, China

Jiangsu Key Laboratory of Building Intelligent Energy Saving
, Suzhou 215009, China

Suzhou Key Laboratory of Mobile Network Technology and Application
, Suzhou 215009, China 
 
CHEN Qi-Qiang College of Electronics and Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, China

Jiangsu Key Laboratory of Building Intelligent Energy Saving
, Suzhou 215009, China

Suzhou Key Laboratory of Mobile Network Technology and Application
, Suzhou 215009, China 
 
HU Wen College of Electronics and Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, China

Jiangsu Key Laboratory of Building Intelligent Energy Saving
, Suzhou 215009, China

Suzhou Key Laboratory of Mobile Network Technology and Application
, Suzhou 215009, China 
 
LU You College of Electronics and Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, China

Jiangsu Key Laboratory of Building Intelligent Energy Saving
, Suzhou 215009, China

Suzhou Key Laboratory of Mobile Network Technology and Application
, Suzhou 215009, China 
 
WU Hong-Jie College of Electronics and Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, China

Jiangsu Key Laboratory of Building Intelligent Energy Saving
, Suzhou 215009, China

Suzhou Key Laboratory of Mobile Network Technology and Application
, Suzhou 215009, China 
 
FU Qi-Ming College of Electronics and Information Engineering, Suzhou University of Science and Technology, Suzhou 215009, China

Jiangsu Key Laboratory of Building Intelligent Energy Saving
, Suzhou 215009, China

Suzhou Key Laboratory of Mobile Network Technology and Application
, Suzhou 215009, China 
fqm_1@126.com 
引用文本:
陈建平,陈其强,胡文,陆悠,吴宏杰,傅启明.基于Q-Learning算法的建筑能耗预测.计算机系统应用,2019,28(1):156-162
CHEN Jian-Ping,CHEN Qi-Qiang,HU Wen,LU You,WU Hong-Jie,FU Qi-Ming.Prediction of Building Energy Consumption Based on Q-Learning.COMPUTER SYSTEMS APPLICATIONS,2019,28(1):156-162

用微信扫一扫

用微信扫一扫