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The goal of this work is to give a full review of how machine learning (ML) is used in thermal comfort studies, highlight the most recent techniques and findings, and lay out a plan for future research. Most of the researchers focus on developing models related to thermal comfort prediction. However, only a few works look at the current state of adaptive thermal comfort studies and the ways in which it could save energy. This study showed that using ML control schemas to make buildings more comfortable in terms of temperature could cut energy by more than 27%. Finally, this paper identifies the remaining difficulties in using ML in thermal comfort investigations, including data collection, thermal comfort indices, sample size, feature selection, model selection, and real-world application. [ABSTRACT FROM AUTHOR]

Monitoring the thermal comfort of building occupants is crucial for ensuring sustainable and efficient energy consumption in residential buildings. It enables not only remote real-time detection of situations, but also a timely reaction to reduce the damage made by harmful situations in targeted buildings. In this paper, we first design a new Internet of Things (IoT) architecture in order to provide remote availability of both indoor and outdoor conditions, with respect to the limited energy of IoT devices. We then build a multi-output prediction model of indoor parameters using a random forest learning algorithm, and based on a longitudinal real dataset of one year. Our prediction model considers outdoor conditions to predict the indoor ones. Hence, it helps to detect discomfort situations in real-time when comparing predicted variables to real ones. Furthermore, when detecting an indoor thermal discomfort, we provide a new genetic-based algorithm to find the most suitable values of indoor parameters, enabling the improvement of the indoor occupants' thermal comfort. Numerical results show the efficiency of our prediction scheme, reaching an accuracy of 96%, as well as our genetic-based scheme in optimizing the indoor thermal parameters by 85%. [ABSTRACT FROM AUTHOR]

Energy efficiency (EE) is of great concern in cognitive radio networks since the throughput and energy consumption of secondary users (SUs) vary with the sensing time. However, the conditions of the detection probability and false alarm probability should be respected to better protect primary users (PUs) and to improve the sensing performance of SUs. Additionally, the PUs' minimum averaged power provision should also be regarded as a key problem of interactive linking to SUs. Therefore, an integrated design between the PU and SUs is desired for the coordination of the whole cognitive radio system, especially regarding the satisfaction of EE and performance metrics. This study formulates sensing constraints in a unified way and calculates the minimum SNR of SUs, based on which the essential PU power provision is computed. Furthermore, EE is proved as a decreasing function with the PU's active ratio, where the maximum EE is obtained corresponding to the minimum QoS requirements of the sensing process. Hence, a bisection-based method is proposed to maximize EE, which is considered as a concave function of SUs' sensing time and has only one unique optimum. EE's optimization was analyzed under different fusion rules for diverse SNR conditions. The optimum was also studied with sensing performance targets for various cases of PU power provision. [ABSTRACT FROM AUTHOR]