标题:Analysis of Key Factors in Heat Demand Prediction with Neural Networks
作者:Xie, Jiyang; Li, Hailong; Ma, Zhanyu; Sun, Qie; Wallin, Fredrik; Si, Zhongwei; Guo, Jun
通讯作者:Li, Hailong
作者机构:[Xie, Jiyang; Ma, Zhanyu; Guo, Jun] Beijing Univ Posts & Telecommun, Pattern Recognit & Intelligent Syst Lab, Beijing, Peoples R China.; [Li, Hailon 更多
会议名称:8th International Conference on Applied Energy (ICAE)
会议日期:OCT 08-11, 2016
来源:8TH INTERNATIONAL CONFERENCE ON APPLIED ENERGY (ICAE2016)
出版年:2017
卷:105
页码:2965-2970
DOI:10.1016/j.egypro.2017.03.704
关键词:District heating; data processing; prediction; heat demand; CHP plant
摘要:The development of heat metering has promoted the development of statistic models for the prediction of heat demand, due to the large amount of available data, or big data. Weather data have been commonly used as input in such statistic models. In order to understand the impacts of direct solar radiance and wind speed on the model performance comprehensively, a model based on Elman neural networks (ENN) was adopted, of which the results can help heat producers to optimize their production and thus mitigate costs. Compared with the measured heat demand, the introduction of wind speed and direct solar radiation has opposite impacts on the performance of ENN and the inclusion of wind speed can improve the prediction accuracy of ENN. However, ENN cannot benefit from the introduction of both wind speed and direct solar radiation simultaneously. (C) 2017 The Authors. Published by Elsevier Ltd.
收录类别:CPCI-S;EI;SCOPUS
Scopus被引频次:2
资源类型:会议论文;期刊论文
原文链接:https://www.scopus.com/inward/record.uri?eid=2-s2.0-85020715688&doi=10.1016%2fj.egypro.2017.03.704&partnerID=40&md5=8deca03c95316421816040c0e1e10080
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