Implementation of a Hybrid Technique for the Predictive Control of the Residential Heating Ventilation and Air Conditioning Systems

被引：0

作者：

Ray, Mitali ^{[1
]}

Samal, Padarbinda ^{[1
]}

Panigrahi, Chinmoy Kumar ^{[1
]}

机构：

[1] KIIT Deemed Univ, Sch Elect Engn, Bhubaneswar, Odisha, India

来源：

ENGINEERING TECHNOLOGY & APPLIED SCIENCE RESEARCH | 2022年 / 12卷 / 03期

关键词：

HVAC loads; neural networks; energy management; hybrid networks; ARTIFICIAL-INTELLIGENCE; ENERGY; PERFORMANCE;

D O I：

暂无

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

Since daily energy needs are increasing, it is imperative to find ways to save energy, such as improving the energy consumption of buildings. Heating Ventilating and Air Conditioning (HVAC) loads account for the majority of a building's energy use. The accurate estimation of energy consumption and the examination of various ways to improve the energy efficiency of buildings are very important. This paper presents an analysis of HVAC loads in a residential building by examining three Neural Networks (NNs): Feed-Forward (FF), Cascaded Forward Backpropagation (CFBP), and Elman Backpropagation (EBP) networks, based on Mean Absolute Error (MAE), Mean Square Error (MSE), and Mean Relative Error (MRE). Furthermore, these networks were combined in hybrid NNs to obtain more optimized results. These results were also compared with other approaches and showed better prediction performance.

引用

页码：8772 / 8776

页数：5

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