Smart thermal comfort system: a development of the fundamental control algorithm

This paper reports on the development of the fundamental algorithm for a smart thermal comfort system. Using Predictive Mean Vote (PMV) as a means of measuring thermal comfort, this system would able the user to define their own expression towards the surroundings, from slightly warm to slightly cold. Here, the operator only needs to insert its respective value ofPMV (ranging from -1 to + 1) and the system will generate the compressor and fan of the air conditioning system so that it will create a thermally comfortable environment, based on the operator's desires. This differentiates the system with the conventional air conditioning system where the operator needs to set separately fan speed and degree of cooling. The PMV value here will be calculated as input instead of the normal PMV equation where these values depend on the air temperature, relative humidity and air velocity. All these parameters values are set from the standard range allowed by the ISO 7730 of thermal comfort at a workplace for sedentary activity. Since previous researches use PMV as the output value, the help of Microsoft Excel is used to obtain air temperature and air velocity for respective values ofPMV. Finally, the fundamental stage for experimentation step is implemented by building a working region in allowing the PID (Proportional, Integrative, Derivative) controller to control its duty cycle

An Enhanced Random Linear Oracle Ensemble Method using Feature Selection Approach based on Naïve Bayes Classifier

Random Linear Oracle (RLO) ensemble replaced each classifier with two mini-ensembles, allowing base classifiers to be trained using different data set, improving the variety of trained classifiers. Naïve Bayes (NB) classifier was chosen as the base classifier for this research due to its simplicity and computational inexpensive. Different feature selection algorithms are applied to RLO ensemble to investigate the effect of different sized data towards its performance. Experiments were carried out using 30 data sets from UCI repository, as well as 6 learning algorithms, namely NB classifier, RLO ensemble, RLO ensemble trained with Genetic Algorithm (GA) feature selection using accuracy of NB classifier as fitness function, RLO ensemble trained with GA feature selection using accuracy of RLO ensemble as fitness function, RLO ensemble trained with t-test feature selection, and RLO ensemble trained with Kruskal-Wallis test feature selection. The results showed that RLO ensemble could significantly improve the diversity of NB classifier in dealing with distinctively selected feature sets through its fusionselection paradigm. Consequently, feature selection algorithms could greatly benefit RLO ensemble, with properly selected number of features from filter approach, or GA natural selection from wrapper approach, it received great classification accuracy improvement, as well as growth in diversity

Thermal Comfort: A Review on Methods of AC Control in a Small Indoor Space

AbstractThis paper discusses the comparison of two methods to achieve thermal comfort utilising air conditioning (AC) system in a small indoor space – adaptive control and fuzzy control. Thermal comfort indoors is performed to provide comfortability individually or for a group of people. Due to the small indoor space which usually a bit cramped, crowded and less airy, the ambience can be very uncomfortable either for doing sedentary or active work, thus the AC system can be very useful to provide thermal comfort. Both methods can be utilised depending on how thermal comfort is viewed and how the level of thermal comfort is decided. Every method has its own advantage and limitations, and will be covered in this paper as well