Modeling the simultaneity coefficient used in electrical interior wiring projects with artificial intelligence

Electricity has become an indispensable part of our lives in the light of technological advancements. Therefore, the secure delivery of electricity to end-users is crucial in today's world. The aim of the planning and design process when delivering electricity to end-users is to provide energy distribution with minimum cost and maximum quality, meeting the consumers' needs. In this thesis, the electricity consumption of consumers in low-voltage facilities is addressed within the framework of the Electrical Internal Installations Regulation. The synchronization coefficients in the Electrical Internal Installations Regulation, as well as the installed power, calculated demand power, and realized demand power values, have been examined. In this examination, the installed power, calculated demand power, and realized demand power of 2709 buildings in Çankırı province were analyzed. The dataset used for this analysis was evaluated using a two-layered, feedback, and sigmoid activation function artificial neural network method. Additionally, this evaluation aims to achieve the closest prediction of the demand data obtained from the meters and project data using the artificial neural network method and compare the difference between the calculated demand powers based on the synchronization coefficients mentioned in the Electrical Internal Installations Regulation and the real powers. In this study, an artificial neural network model has been developed that learns more effectively and continuously when fed with continuous data according to the coefficients specified in the Electrical Internal Installations Regulation.