## 1 Introduction  
The awareness regarding the utilization of renewable energy sources in energy production has been steadily increasing worldwide due to factors such as the rising global energy demand, the depletion of fossil fuel reserves, and the adverse effects of CO\({}^{2}\) emissions. Among the most widely employed renewable energy sources, solar energy is harnessed through solar panels to convert sunlight into electricity in photovoltaic (PV) systems. The annual average solar irradiance in Turkey is 1527.46 kWh/m\({}^{2}\)/year, with an average sunshine duration of 2741.07 h, approximately equivalent to 27 million TOE (Tons of Oil Equivalent). As of the end of December 2022, the installed capacity of solar power plants in Turkey reached 9425.4 MW, indicating a growth of 1609.8 MW compared to the previous year [1]. This global increase in solar energy installation capacity, observed in Turkey, brings about the necessity to address aspects such as power control, optimal energy generation, mitigation of power losses, power estimation, and maintenance and repair requirements in solar energy systems. In this context, the conducted study comprises two fundamental parts. In the first part, a data recording and monitoring system is designed. Particularly crucial is the development of an automation system for remote monitoring of large-scale PV energy systems situated in remote areas, enabling early intervention against potential power losses. Considering the aforementioned attributes, this study designs an Internet of Things (IoT)-based data recording and monitoring system to real-time record and monitor parameters obtained from the PV panels, batteries, and energy system within their operational environment. The gathered data are stored in a database and can be tracked in real-time using the designed web and mobile interfaces. The data are presented both numerically and graphically to the user. Furthermore, the implemented monitoring system sends alert messages to the user in the event of a malfunction in the energy system, facilitating informed actions to prevent potential power loss through early intervention. The second part of the study involves power forecasting using the collected data. The power output from PV panels varies based on factors such as geographical location, seasonal changes, and environmental conditions. Accurate power forecasting is essential for the efficient and economical utilization of solar panels as a reliable energy source. This enables the installation of controllable PV energy systems, guides electric companies, manages energy, optimizes energy levels, and identifies necessary panel adaptations to reach maximum production capacity. Moreover, it holds significant importance in terms of time savings and reduction of additional labor costs. Therefore, the estimation of power output values and load trends for renewable power facilities like PV energy systems emerges as a fundamental process [2]. Presently, the prevalent approach for power forecasting in PV energy systems involves analyzing historical data and considering seasonal, daily, and hourly variations to predict future power generation. Artificial neural networks and regression models are among the methods employed for this purpose [3]. In this study, power forecasting is conducted using the data obtained from Karabuk province in Turkey's Western Black Sea region, where the annual sunshine duration is 2402 h, and the annual radiation value is 1369 kW/h per square meter. Meteorological data, such as humidity, temperature, pressure, and time information, are utilized in power forecasting through machine learning techniques [4]. To determine the most successful machine learning method for power forecasting, linear regression, support vector machines (SVM), decision trees, random forests, and k-nearest neighbors (KNN) algorithms are sequentially employed. The results obtained from each algorithm are presented in a comparative manner. The subsequent section provides an in-depth review of the relevant literature on the topic.