## 9 Conclusions  
This work outlines an enhanced MPC system for measuring human comfort index and maintaining the indoor thermal comfort in the optimal level. An MLR based PMV predictive model  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Title** & **ISE** & **IAE** & **ITAE** \\ \hline Existing approach & 15.772 & 2.645 & 102.645 \\ Proposed approach & 15.112 & 2.512 & 90.704 \\ \hline \hline \end{tabular}  
ISE: integral square error; IAE: integral of the absolute value of error; ITAE: integral of time-multiplied absolute value of error.  
\end{table}
Table 9: Comparison analysis between the Proposed Approach and the Existing Approach.  
is proposed with a simplified PMV equation. The biggest novelty of the presented work is the fuzzy controller, taking outdoor environment conditions into account in order to maintain the proper comfort level with the seasons. An IoT smart space prototype with various IoT devices has been implemented as the proof of concept and experimentally tested in an indoor lab of a building on Jeju Island, Korea to validate the performance of the proposed approach. These physical devices are integrated with Intel Edison boards and all communication among them happens over CoAP. A series of experiments are performed throughout June in 2017, which indicate a very stable behavior, allowing effective and fast control of the indoor thermal comfort condition according to the outdoor environment. In addition, the temperature regulating capabilities of the proposed approach have been validated by a comparison study with respect to existing approaches without adopting outdoor environment factors, performing better in accordance with IAE, ISE and ITAE performance indices. These features make the proposed system a promising HVAC controller for indoor environments. It is necessary to point out that this work can be applied to any other space not just in buildings. The only requirement is that the space should provide a suitable network of physical sensors in order to estimate the PMV index. For instance, it can be extended to the application case like hospitals, which also have high demands for environment conditions. The future research directions of this work are to deploy the designed approach into larger scale space while using more environment sensors and to apply other algorithms to reduce energy consumption.  
[PERSON] conceived the idea for this paper, designed the experiments and wrote this paper; [PERSON]-H.K. conceived the overall idea of Model-based Predictive Control for indoor thermal comfort, and proof-read the manuscript.  
This research was supported by the MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2017-2016-0-00313) supervised by the IITP (Institute for Information & communications Technology Promotion), and this research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (2015R1D1A1A01060493)). Any correspondence related to this paper should be addressed to [PERSON]; [EMAIL_ADDRESS].  
The authors declare no conflict of interest.