Development of a generalised neural network model to detect faults in building energy performance. Part 2.

Part I of this paper discussed the theoretical considerations of creating a nonlinear black box model. In P art II, the constraints on the nonlinear model imposed by the application are discussed followed by presentation of the model structure, training method, input selection, and input transformation. The test results of applying the proposed model with the selected features to five test buildings are discussed next. One of the test buildings (Zachry Engineering C enter) selected for this study was also used in a previous study as a p art of energy prediction competition (Haber!

Development of a generalised neural network model to detect faults in building energy performance - Part 1.

A building energy management system (BEMS) generally monitors and manages energy usage in commercial buildings. With the ability to monitor a plant and to recall the collected data at a later time, actual building energy performance can be measured and compared with the expected performance. The comparison will help in detecting possible abnormalities with the building energy usage and in identifying opportunities to optimize the building energy performance. In order to predict expected building energy performance, a reasonably accurate building energy model is needed.

Dynamic thermal sensation in PDEC buildings.

In buildings with passive downdraught evaporative cooling (PDEC), occupants are subjected to environmental conditions which might be characterised by elevated relative humidities, increased air speeds, and time-varying internal conditions. A new physiological model which describes the human thermophysical system, and the active control exercised on it, has been produced. The model predicts skin and core temperatures, sweat rates, etc. on different parts of a seated, standing or exercising human.