Online Model Learning of Buildings Using Stochastic Hybrid Systems Based on Gaussian Processes

Hamzah Abdel-Aziz,Xenofon Koutsoukos

doi:10.1155/2017/3035892

Hamzah Abdel-Aziz, Xenofon Koutsoukos

Open Access

https://doi.org/10.1155/2017/3035892

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Abstract

Dynamical models are essential for model-based control methodologies which allow smart buildings to operate autonomously in an energy and cost efficient manner. However, buildings have complex thermal dynamics which are affected externally by the environment and internally by thermal loads such as equipment and occupancy. Moreover, the physical parameters of buildings may change over time as the buildings age or due to changes in the buildings’ configuration or structure. In this paper, we introduce an online model learning methodology to identify a nonparametric dynamical model for buildings when the thermal load is latent (i.e., the thermal load cannot be measured). The proposed model is based on stochastic hybrid systems, where the discrete state describes the level of the thermal load and the continuous dynamics represented by Gaussian processes describe the thermal dynamics of the air temperature. We demonstrate the evaluation of the proposed model using two-zone and five-zone buildings. The data for both experiments are generated using the EnergyPlus software. Experimental results show that the proposed model estimates the thermal load level correctly and predicts the thermal behavior with good performance.

Highlights

Heating, ventilation, and air conditioning (HVAC) in buildings is a major source of energy consumption
We introduce a nonparametric stochastic hybrid systems (SHS) model based on Gaussian processes (GPs)
We evaluate the performance of the proposed framework and its efficacy to learn thermal models for buildings when the applied thermal load cannot be measured

Summary

Introduction

Ventilation, and air conditioning (HVAC) in buildings is a major source of energy consumption. The model parameters may change over time as the buildings age or due to changes in the configuration or structure Such challenges can be addressed using nonparametric modeling approaches based on online model learning. We introduce a novel modeling framework to learn multizone data-driven dynamical models for buildings in an online fashion. (1) We introduce a nonparametric SHS model based on Gaussian processes which can be used to model complex coupled discrete and continuous dynamics required to develop data-driven models of smart buildings. The efficiency of the proposed model is evaluated using datasets of twozone and five-zone buildings In both experiments, we show the model accuracy of estimating the level of the thermal load and predicting the zone air temperature.

Related Work

Background

Stochastic Hybrid Systems

Online Clustering-Based Model Learning for SHS

Evaluation

Conclusion