Intelligent HVAC Control Prediction

Intelligent HVAC Control Prediction

Mohamed Alkhadashi, Adnan K. Shaout
Copyright: © 2022 |Pages: 25
DOI: 10.4018/IJSVST.315648
OnDemand:
(Individual Articles)
Available
$37.50
No Current Special Offers
TOTAL SAVINGS: $37.50

Abstract

Comfort where an occupant is present is the subject of marketing in many sectors. This research paper focuses on heating, ventilation, and air conditioning (HVAC) in the transportation sector. A literature survey has been conducted to understand historic HVAC control and optimization approaches. Many control approaches were captured/compared, and this provides great potential, but also shows that there is still room for improvement. This research explores a unique control opportunity using linear discriminant analysis (LDA) to predict the occupant, and then follows it with kalman decomposition (KD) for real time controllability/observability post-LDA operation. Integrating these two tools provides results as new combined approach for HVAC control. Prediction algorithm LDA shows approximately 79% accuracy score for prediction, which is above average when compared to other algorithms and sensors used. KD is manipulated to be controllable and observable to maintain cabin temperature in real-time once the occupant is identified.
Article Preview
Top

Background

The demand and use of new technology has increased rapidly lately as a result of its growth (Ra et al., 2019). While it continues to develop and widen its broadband, it often provides more opportunity of integration and other tools/applications to be utilized more frequently. HVAC has been around for years and its need in almost every space where living beings exist has made it a good apparatus for development.

Since the discovery of HVAC many different types of control approaches have been discovered (Mirinejad, Sadati, Ghasemian, & Torab, 2008), (USA Patent No. US2175985A, 1935), (Soyguder, Karakose, & Alli, 2009) to provide a steady yet controllable temperature environment. As time goes on and other tools become available, control strategies are more robust and accurate as they are support by other discoveries such as big data (Zhu, Gong, Zhang, Zhao, & Zhou, 2018) or IoT (Wan, Lim, Wang, & Tseng, 2021). Smart thermostats have also shown significant growth in usage as they aid with efficiency and controllability (Van der Ham, Tabatabaei, Thilakarathne, & Treur, 2016), (Ellis, Scott, Hazas, & Krumm, 2012), (Gao & Keshav, 2013), (Hernandez, Arias, Buentello, & Jin, 2015). Although with smart/wireless system, there is always the challenge network security (Dean & Agyeman, 2018), which tends to be vulnerable to outsiders. Many studies have shown that HVAC requires significant energy consumption compared to other apparatuses within a vehicle or even a building in some case that maintain occupant comfort (Imal, 2015), (Prasad, Lee, Kang, & Kim, 2019), (Yang, Hu, & Spanos, 2019), (Ku, Liaw, Tsai, & Liu, 2015), (Sun, Luh, Jia, & Jiang, 2013), (Kambly & Bradley, 2014), (Farrington & Rugh, 2000), (Cvok, Škugor, & Deur, 2021), (Zhang, Gao, Gao, & Wang, 2015). Mitigations are being studied to increased efficiency when it comes to energy consumption (Faruque & Vatanparvar, 2016), (Knoedler, Steinmann, Laversanne, & Jones, 2012), (Neubauer & Wood, 2014), (Ajanovic & Haas, 2018).

Complete Article List

Search this Journal:
Reset
Volume 7: 1 Issue (2024): Forthcoming, Available for Pre-Order
Volume 6: 1 Issue (2023)
Volume 5: 2 Issues (2022): 1 Released, 1 Forthcoming
Volume 4: 1 Issue (2021)
Volume 3: 2 Issues (2020)
View Complete Journal Contents Listing