System Identification I

The energy performance in buildings is influenced by many factors, such as ambient weather conditions, building structure and characteristics, the operation of sub-level components like lighting and HVAC systems, occupancy and their behavior. This complex situation makes it very difficult to accurately implement the prediction of building energy consumption. This paper reviews recently developed models for solving this problem, which include elaborate and simplified engineering methods, statistical methods and artificial intelligence methods. Previous research work concerning these models and relevant applications are introduced. Based on the analysis of previous work, further prospects are proposed for additional research reference.

A review on the prediction of building energy consumption

This paper is the second of a two-part review of methods for automated fault detection and diagnostics (FDD) and prognostics whose intent is to increase awareness of the HVAC&R research and development community to the body of FDD and prognostics developments in other fields as well as advancements in the field of HVAC&R. The first part of the review focused on generic FDD and prognostics, provided a framework for categorizing methods, described them, and identified their primary strengths and weaknesses (Katipamula and Brambley 2005). In this paper we address research and applications specific to the fields of HVAC&R, provide a brief discussion on the current state of diagnostics in buildings, and discuss the future of automated diagnostics in buildings.

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Methods for Fault Detection, Diagnostics and Prognostics for Building Systems - A Review Part II

Theory and applications of HVAC control systems – A review of model predictive control (MPC)

A building manager includes a communications interface configured to receive information from a smart energy grid. The building manager further includes an integrated control layer configured to receive inputs from and to provide outputs to a plurality of building subsystems. The integrated control layer includes a plurality of control algorithm modules configured to process the inputs and to determine the outputs. The building manager further includes a fault detection and diagnostics layer configured to use statistical analysis on the inputs received from the integrated control layer to detect and diagnose faults. The building manager yet further includes a demand response layer configured to process the information received from the smart energy grid to determine adjustments to the plurality of control algorithms of the integrated control layer.

Smart building manager

A rule-based fault detection method for air handling units

The effect of a centered, square, heat-conducting body on natural convection in a vertical square enclosure was examined numerically. The analysis reveals that the fluid flow and heat transfer processes are governed by the Rayleigh and Prandtl numbers, the dimensionless body size, and the ratio of the thermal conductivity of the body to that of the fluid. For Pr = 0.71 and relatively wide ranges of the other parameters, results are reported in terms of streamlines, isotherms, and the overall heat transfer across the enclosure as described by the Nusselt number. Heat transfer across the enclosure, in comparison to that in the absence of a body, may be enhanced (reduced) by a body with a thermal conductivity ratio less (greater) than unity. Furthermore, the heat transfer may attain a minimum as the body size is increased. These and other findings are justified through a careful examination of the local heat and fluid flow phenomena.

Effect of a centered conducting body on natural convection heat transfer in an enclosure

Subsystem level fault diagnosis of a building's air-handling unit using general regression neural networks

The objective of this study is to describe the application of artificial neural networks to the problem of fault diagnosis in an air-handling unit. Initially, residuals of system variables that can be used to quantify the dominant symptoms of fault modes of operation are selected. Idealized steady-state patterns of the residuals are then defined for each fault mode of operation. The steady-state relationship between the dominant symptoms and the faults is learned by an artificial neural network using the backpropagation algorithm. The trained neural network is applied to experimental data for various faults and successfully identifies each fault.

John M. House

Papers

A rule-based fault detection method for air handling units

Effect of a centered conducting body on natural convection heat transfer in an enclosure

Subsystem level fault diagnosis of a building's air-handling unit using general regression neural networks

Fault diagnosis of an air-handling unit using artificial neural networks

Modeling and fault diagnosis design for HVAC systems using recurrent neural networks