Karyono, K (2023) A Novel Indoor Adaptive Thermal Comfort System to Reduce the Energy Consumption for the Residential Dwellings. Doctoral thesis, LJMU.

Preview	Text 2023karyonophd (002).pdf - Published Version Available under License Creative Commons Attribution Non-commercial. Download (9MB) | Preview
Preview	Text 2023karyonophdappendix.pdf - Supplemental Material Available under License Creative Commons Attribution Non-commercial. Download (23MB) | Preview

Abstract

The percentage of households in fuel poverty, who cannot afford adequate heating, has reached 25% in the United Kingdom (UK), resulting in a critical threat to life. Therefore, this issue is currently of interest to UK policymakers and stakeholders. Currently, the main areas of interest relating to thermal comfort are factors relating to indoor health, the energy crisis, and Global Climate Change.
There was a gap in the acknowledgements of adaptive thermal comfort (psychological and human behaviour aspects) due to the focus on human physiology (Predicted Mean Vote - Predicted Percentage Dissatisfied/ PMV-PPD). Furthermore, existing heating control systems need to be optimized for using an electric radiant heating panel to anticipate the future focus on renewable energy sources.
This work has developed a novel base system model that better reflects the user conditions for the future indoor thermal control system based on the existing ASHRAE RP-884 and Global Thermal Comfort Database II combined with new data collections and case studies. The system model has the compatibility to control the heating panels based on the network of sensors and flexible user control with a low-cost system approach to suit residential needs.
The artificial intelligence (AI) model with shallow supervised learning implemented in the system can enhance the existing model to produce a 98.49% comfort zone from all ASHRAE multiple databases. In contrast, the PMV-PPD only gives 69.91% comfort, while the Givoni approach gives 89.19%. With a 6.62% wider comfort area and the assumption of direct conversion to saving, the base system model can contribute to about 783.5 thousand tonnes of CO2 equivalent per year with the 2030 emission factor. Widening the thermal comfort zone also acknowledges a particular group that needs a different set point. This work also recognized that acknowledging the human presence during the thermal comfort assessment can increase the comfort level more than 10% with the same heating arrangement.
The initial model assessment was also developed using MATLAB to represent the UK’s indoor conditions for typical residential properties built prior to the 1920s and after the 2010s and highlights the suitable parameters for indoor comfort with lower energy use. The simulation results recommend lowering the thermal set point for thermal comfort. The result is based on hourly thermal data across the UK on the different housing typologies.
This solution can bridge the physiology and psychology aspects and benefit the engineers and the researchers in the thermal comfort area.

Item Type:	Thesis (Doctoral)
Uncontrolled Keywords:	Adaptive thermal comfort; Artificial Intelligence; Field studies; Simulation and modelling; Internet of things; Thermal Comfort Review; Energy saving and sustainability
Subjects:	T Technology > T Technology (General) T Technology > TA Engineering (General). Civil engineering (General)
Divisions:	Civil Engineering and Built Environment
SWORD Depositor:	A Symplectic
Date Deposited:	20 Nov 2023 14:28
Last Modified:	31 Oct 2024 00:50
DOI or ID number:	10.24377/LJMU.t.00021766
Supervisors:	Abdullah, B, Cotgrave, A, Armada Bras, A and Cullen, J
URI:	https://researchonline.ljmu.ac.uk/id/eprint/21766

View Item