## 2 Related Work  
The first part of the literature review addresses the studies related to monitoring of PV energy systems. In the initial research efforts concerning the monitoring of PV energy systems, wired systems utilizing RS232 and RS485 communication protocols were employed for data transmission [5; 6]. Due to exposure to environmental factors, such as rain, temperature, and humidity, the cables carrying data in these systems necessitated additional maintenance costs. In contrast, wireless monitoring systems are less affected by environmental conditions compared to wired monitoring systems and, especially in real-time applications, they possess quicker decision-making capability. Additionally, they convey information over a longer range with higher accuracy. In their work, [PERSON] et al. [7] developed a low-cost IoT-based tracking system for maximum power point track ing (MPPT) in PV systems. [PERSON] and [PERSON] [8] addressed the automation of solar PV power generation. An IoT platform was utilized to monitor and control solar energy production. [PERSON] et al. [9] aimed to provide a cost-effective and open-source IoT solution using the ESP32 board to intelligently gather and real-time monitor the generated power and environmental conditions of solar stations. [PERSON] and co-workers [10] discussed an IoT-based, low-cost monitoring system for solar PV installations. [PERSON] and [PERSON] [11] developed an IoT device that individually monitored each PV array and provided feedback on their efficiencies to prevent power losses in large solar farms. [PERSON] et al. [12] described an IoT-based software architecture for continuous monitoring of solar panel efficiency. [PERSON] et al. [13] introduced IoT-based application innovation to a low-cost Arduino microcontroller-based solar data logger. [PERSON] et al. [14] proposed a fully open-source software-based IoT solution for monitoring PV installations. [PERSON] et al. [15] systematically presented all design stages of a low-cost IoT-based data collection system. In [PERSON]'s study [16], an IoT-based real-time monitoring system for renewable stand-alone power plants was discussed. [PERSON] et al. [17] presented an open-source hardware and software-based monitoring system for tracking the temperature of PV generators in smart microgrids. The monitoring system they developed utilized an Arduino microcontroller and a Raspberry Pi microcomputer. [PERSON] et al. [18] assessed a remote monitoring system for photovoltaic power generation stations using IoT and a state-of-the-art tool for virtual supervision. Continuing the literature review, the second part of the study examines the power forecasting methods used in solar PV systems. [PERSON] et al. [19] presented a comparative study of solar irradiance predictions obtained through multiple linear regression methods and Artificial Neural Networks (ANN) models, revealing PV panel power output characteristics using weather data. [PERSON] and colleagues [20], apart from the aforementioned studies, concluded that ANN is the most suitable method for predicting PV power outputs. [PERSON] et al. [21] conducted research using Support Vector Machines (SVM), a machine learning approach, to predict PV system power outputs. [PERSON] et al. [22] utilized a backpropagation-trained ANN structure along with meteorological data to forecast solar panel output power. [PERSON] et al. [23] hybridized the Particle Swarm Optimization (PSO) evolutionary algorithm with an ANN, incorporating irradiance values as inputs, to obtain solar radiation prediction in their training approach. [PERSON] and [PERSON] [24] designed a multi-layered feedforward ANN using panel outputs from differently located solar panels along with satellite data. [PERSON] et al. [25] employed wavelet transform to extract useful information from complex PV output power data and constructed an ANN model.