Towards a Semantically-Enabled Control Strategy for Building Simulations: Integration of Semantic Technologies and Model Predictive Control

Published

August 9, 2017

Author(s)

Parastoo Delgoshaei, Mark Austin, Amanda Pertzborn, Mohammad Heidarinejad, Vikas Chandan

Abstract

State-of-the-art building simulation control methods incorporate physical constraints into their mathematical models, but omit implicit constraints associated with policies of operation and dependency relationships among rules representing those constraints. To overcome these shortcomings, there is a recent trend in enabling the control strategies with inference-based rule checking capabilities. One solution is to exploit semantic web technologies in building simulation control. Such approaches provide the tools for semantic modeling of domains, and the ability to deduce new information based on the models through use of Description Logic (DL). In a step toward enabling this capability, this paper presents a cross-disciplinary data-driven control strategy for building energy management simulation that integrates semantic modeling and formal rule checking mechanisms into a Model Predictive Control (MPC) formulation. The results show that MPC provides superior levels of performance when initial conditions and inputs are derived from inference-based rules.

Proceedings Title

Proceedings of the 15th IBPSA Conference

Conference Dates

August 7-9, 2017

Conference Location

San Francisco, CA, US

Conference Title

Building Simulation 2017

Pub Type

Conferences

Download Paper

https://doi.org/10.26868/25222708.2017.258

Local Download

Keywords

MPC, OWL, ontology, control, building simulation

Citation

Delgoshaei, P. , Austin, M. , Pertzborn, A. , Heidarinejad, M. and Chandan, V. (2017), Towards a Semantically-Enabled Control Strategy for Building Simulations: Integration of Semantic Technologies and Model Predictive Control, Proceedings of the 15th IBPSA Conference , San Francisco, CA, US, [online], https://doi.org/10.26868/25222708.2017.258, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=922592 (Accessed October 13, 2025)