Nesnelerin interneti tabanlı güneş enerjili su ısıtma sisteminin geliştirilmesi

Despite the advantages they offer, the unpredictable nature of hot water demand poses a significant impediment to optimizing their operation. There is an observable gap in existing research concerning the prediction of stochastic hot water demand and optimal operation of SWHSs. While several studies have shed light on the prediction of volumetric hot water usage and the optimization of water heating systems based on demand-side predictions, a comprehensive approach that integrates advanced learning techniques and the Internet of Things (IoT) for both prediction and optimization remains largely unexplored. This gap presents an opportunity to enhance the efficiency of SWHSs, especially in residential settings. This study aims to fill this lacuna by developing an IoT-based Solar Water Heating System and mainly on the axis of its supervising unit termed 'The Solar Water Heating System Controller Unit (SWHSCU)' that leverages IoT technology, deep neural networks (DNNs), and deep reinforcement learning (DRL) to ensure optimal hot water demand throughout the day. The proposed controller aims to not only expand the prediction of stochastic hot water demand but also integrate these predictions into the real-time operation of SWHSs, thereby enhancing system efficiency and operational reliability. The development process entails three core phases. Firstly, the design and installation of the SWHSs, during which two iterations were developed, each representing an advancement from its predecessor. Secondly, the development of the SWHSCU, incorporating IoT technology and advanced programming techniques. Lastly, the creation of a DRL algorithm framework supported by DNNs, utilizing a Long Short-Term Memory (LSTM) approach to handle time-series data effectively. The research focuses on enhancing the operation of residential solar water heating systems by optimizing hot water demand prediction through advanced AI and IoT methodologies. This advancement could catalyze significant improvements in the energy efficiency of solar water heating systems, resulting in the reduction of environmental impacts and energy costs. The research data is based on real data collected through sensors and the operating statuses of actuators connected to the controller unit of the second-generation solar water heating system, which was designed by the researcher and employed in the redesigned second-generation solar water heating system in the Konyaaltı district of Antalya, between March 15, 2023, and June 15, 2023. In addition, the data includes meteorological information obtained from the mgm.gov.tr website for the corresponding dates in the region where the solar water heating system was installed. Future work in this field will further enhance the viability and security of the developed SWHS, providing additional perspectives for cost reduction and data security. This study contributes to the literature by offering a novel approach to optimize the operation of residential SWHSs through advanced AI and IoT methodologies.