Development of an RDP neural network for building energy consumption fault detection and diagnosis

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.