Occupancy-Based HVAC Control with Short-Term Occupancy Prediction Algorithms for Energy-Efficient Buildings
This study aims to develop a concrete occupancy prediction as well as an optimal occupancy-based control solution for improving the efficiency of Heating, Ventilation, and Air-Conditioning (HVAC) systems. Accurate occupancy prediction is a key enabler for demand-based HVAC control so as to ensure HV...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-09-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1996-1073/11/9/2427 |
| _version_ | 1798005537594408960 |
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| author | Jin Dong Christopher Winstead James Nutaro Teja Kuruganti |
| author_facet | Jin Dong Christopher Winstead James Nutaro Teja Kuruganti |
| author_sort | Jin Dong |
| collection | DOAJ |
| description | This study aims to develop a concrete occupancy prediction as well as an optimal occupancy-based control solution for improving the efficiency of Heating, Ventilation, and Air-Conditioning (HVAC) systems. Accurate occupancy prediction is a key enabler for demand-based HVAC control so as to ensure HVAC is not run needlessly when when a room/zone is unoccupied. In this paper, we propose simple yet effective algorithms to predict occupancy alongside an algorithm for automatically assigning temperature set-points. Utilizing past occupancy observations, we introduce three different techniques for occupancy prediction. Firstly, we propose an identification-based approach, which identifies the model via Expectation Maximization (EM) algorithm. Secondly, we study a novel finite state automata (FSA) which can be reconstructed by a general systems problem solver (GSPS). Thirdly, we introduce an alternative stochastic model based on uncertain basis functions. The results show that all the proposed occupancy prediction techniques could achieve around 70% accuracy. Then, we have proposed a scheme to adaptively adjust the temperature set-points according to a novel temperature set algorithm with customers’ different discomfort tolerance indexes. By cooperating with the temperature set algorithm, our occupancy-based HVAC control shows 20% energy saving while still maintaining building comfort requirements. |
| first_indexed | 2024-04-11T12:40:52Z |
| format | Article |
| id | doaj.art-594e4f2842bd486fb32493c46e452449 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-11T12:40:52Z |
| publishDate | 2018-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-594e4f2842bd486fb32493c46e4524492022-12-22T04:23:30ZengMDPI AGEnergies1996-10732018-09-01119242710.3390/en11092427en11092427Occupancy-Based HVAC Control with Short-Term Occupancy Prediction Algorithms for Energy-Efficient BuildingsJin Dong0Christopher Winstead1James Nutaro2Teja Kuruganti3Energy and Transportation Science Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USAComputational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USAComputational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USAComputational Sciences and Engineering Division, Oak Ridge National Laboratory, Oak Ridge, TN 37831, USAThis study aims to develop a concrete occupancy prediction as well as an optimal occupancy-based control solution for improving the efficiency of Heating, Ventilation, and Air-Conditioning (HVAC) systems. Accurate occupancy prediction is a key enabler for demand-based HVAC control so as to ensure HVAC is not run needlessly when when a room/zone is unoccupied. In this paper, we propose simple yet effective algorithms to predict occupancy alongside an algorithm for automatically assigning temperature set-points. Utilizing past occupancy observations, we introduce three different techniques for occupancy prediction. Firstly, we propose an identification-based approach, which identifies the model via Expectation Maximization (EM) algorithm. Secondly, we study a novel finite state automata (FSA) which can be reconstructed by a general systems problem solver (GSPS). Thirdly, we introduce an alternative stochastic model based on uncertain basis functions. The results show that all the proposed occupancy prediction techniques could achieve around 70% accuracy. Then, we have proposed a scheme to adaptively adjust the temperature set-points according to a novel temperature set algorithm with customers’ different discomfort tolerance indexes. By cooperating with the temperature set algorithm, our occupancy-based HVAC control shows 20% energy saving while still maintaining building comfort requirements.http://www.mdpi.com/1996-1073/11/9/2427occupancy modeloccupancy-based controlmodel predictive controlenergy efficiencybuilding climate control |
| spellingShingle | Jin Dong Christopher Winstead James Nutaro Teja Kuruganti Occupancy-Based HVAC Control with Short-Term Occupancy Prediction Algorithms for Energy-Efficient Buildings Energies occupancy model occupancy-based control model predictive control energy efficiency building climate control |
| title | Occupancy-Based HVAC Control with Short-Term Occupancy Prediction Algorithms for Energy-Efficient Buildings |
| title_full | Occupancy-Based HVAC Control with Short-Term Occupancy Prediction Algorithms for Energy-Efficient Buildings |
| title_fullStr | Occupancy-Based HVAC Control with Short-Term Occupancy Prediction Algorithms for Energy-Efficient Buildings |
| title_full_unstemmed | Occupancy-Based HVAC Control with Short-Term Occupancy Prediction Algorithms for Energy-Efficient Buildings |
| title_short | Occupancy-Based HVAC Control with Short-Term Occupancy Prediction Algorithms for Energy-Efficient Buildings |
| title_sort | occupancy based hvac control with short term occupancy prediction algorithms for energy efficient buildings |
| topic | occupancy model occupancy-based control model predictive control energy efficiency building climate control |
| url | http://www.mdpi.com/1996-1073/11/9/2427 |
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