کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
263271 | 504071 | 2013 | 6 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: Development of an RDP neural network for building energy consumption fault detection and diagnosis Development of an RDP neural network for building energy consumption fault detection and diagnosis](/preview/png/263271.png)
Fault detection and diagnosis (FDD) is an important issue in building energy conservation. This paper proposes a new option for solving this problem at the building level by using a recursive deterministic perceptron (RDP) neural network. Results show a higher than 97% level of generalization in all the designed experiments. Based on this high detection ability of RDP model, a new diagnostic architecture is proposed. Our experiments demonstrate that it is able to not only report correct source of faults but also sort sources in the order of degradation likelihood.
► Fault detection and diagnosis in building energy conservation.
► Use of a recursive deterministic perceptron (RDP) neural network.
► Results show generalization levels above 97%.
► New diagnostic architecture.
► System reports source of faults and sort them on degradation likelihood.
Journal: Energy and Buildings - Volume 62, July 2013, Pages 133–138