Cooling Load Forecasting for Chiller Plants Using Similar Day Based Wavelet Neural Networks

被引：0
|
作者：
Thota, Pavan Kumar [1 ]
Dani, Ashwin P. [1 ]
Luh, Peter B. [1 ]
Gupta, Shalabh [1 ]
机构：
[1] Univ Connecticut, Dept Elect & Comp Engn, Storrs, CT 06269 USA
来源：
2015 INTERNATIONAL CONFERENCE ON COMPLEX SYSTEMS ENGINEERING (ICCSE) | 2015年
关键词：
Cooling load forecast; neural networks; similar day selection; similarity measure; wavelet decomposition;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
Cooling load forecasting is beneficial for chiller plant operation as it can be used for energy efficient scheduling of chiller plant components (such as chillers, cooling towers etc.). This paper presents a method for forecasting next day's hourly cooling load for a chiller plant using the concepts of similar day selection, wavelet decomposition, and neural networks. Cooling load forecast is obtained from similar day's cooling load by decomposing it into different subbands (frequency components) and training a separate neural network for each component. The accuracy of similar day selection influences the accuracy of the overall method. Hence, four different measures for similar day selection are presented and their results are compared to determine the most accurate measure. The results suggest that the overall method is accurate up to 89% in predicting the cooling load of a chiller plant.
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页数：6
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