Tools for Uniied Prediction and Diagnosis in Hvac Systems: the Riso Project

This report describes the riso project, a system for uniied prediction and diagnosis in HVAC systems based on a class of probabilistic models called belief networks. Progress has been made in both theoretical and practical problems: a scheme for the representation of belief networks with heterogeneous conditional distributions has been devised, an algorithm for inference in a polytree network with arbitrary distributions has been implemented, and software for distributed belief networks has been implemented. After reviewing the motivation for the use of belief networks, the heterogeneous polytree algorithm is described and several important details are discussed. Distributed belief networks as a framework for reasoning under uncertainty in functionally and geographically distributed systems are then described. Several interesting questions arise in connection with distributed belief networks, such as the control of communication, publishing information in probabilistic form, and coping with induced temporal dependencies. A prototype implementation of distributed belief networks is described, and the report closes with a discussion of how distributed, heterogeneous belief networks may be applied in HVAC systems. 1 Overview of the riso project This report describes work in progress on the riso project, which aims to provide tools for uniied prediction and diagnosis in heating, ventilating, and airconditioning (HVAC) systems. riso is based on probabilistic modeling techniques , especially those probability models which are called \belief networks." Such models are conveniently represented as an acyclic directed graph together 1