Online Model Learning of Buildings Using Stochastic Hybrid Systems Based on Gaussian Processes
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 e...
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| Format: | Article |
| Language: | English |
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Wiley
2017-01-01
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| Series: | Journal of Control Science and Engineering |
| Online Access: | http://dx.doi.org/10.1155/2017/3035892 |
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| author | Hamzah Abdel-Aziz Xenofon Koutsoukos |
| author_facet | Hamzah Abdel-Aziz Xenofon Koutsoukos |
| author_sort | Hamzah Abdel-Aziz |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-5207a6708fe4499da064900bfd3a7fd4 |
| institution | Kabale University |
| issn | 1687-5249 1687-5257 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Control Science and Engineering |
| spelling | doaj-art-5207a6708fe4499da064900bfd3a7fd42025-02-03T01:32:44ZengWileyJournal of Control Science and Engineering1687-52491687-52572017-01-01201710.1155/2017/30358923035892Online Model Learning of Buildings Using Stochastic Hybrid Systems Based on Gaussian ProcessesHamzah Abdel-Aziz0Xenofon Koutsoukos1Department of Electrical Engineering and Computer Science, Institute for Software Integrated Systems, Vanderbilt University, Nashville, TN 37235, USADepartment of Electrical Engineering and Computer Science, Institute for Software Integrated Systems, Vanderbilt University, Nashville, TN 37235, USADynamical 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.http://dx.doi.org/10.1155/2017/3035892 |
| spellingShingle | Hamzah Abdel-Aziz Xenofon Koutsoukos Online Model Learning of Buildings Using Stochastic Hybrid Systems Based on Gaussian Processes Journal of Control Science and Engineering |
| title | Online Model Learning of Buildings Using Stochastic Hybrid Systems Based on Gaussian Processes |
| title_full | Online Model Learning of Buildings Using Stochastic Hybrid Systems Based on Gaussian Processes |
| title_fullStr | Online Model Learning of Buildings Using Stochastic Hybrid Systems Based on Gaussian Processes |
| title_full_unstemmed | Online Model Learning of Buildings Using Stochastic Hybrid Systems Based on Gaussian Processes |
| title_short | Online Model Learning of Buildings Using Stochastic Hybrid Systems Based on Gaussian Processes |
| title_sort | online model learning of buildings using stochastic hybrid systems based on gaussian processes |
| url | http://dx.doi.org/10.1155/2017/3035892 |
| work_keys_str_mv | AT hamzahabdelaziz onlinemodellearningofbuildingsusingstochastichybridsystemsbasedongaussianprocesses AT xenofonkoutsoukos onlinemodellearningofbuildingsusingstochastichybridsystemsbasedongaussianprocesses |