## 1 Introduction  
In recent years, we have witnessed the expansion of an edge computing approach in building complex and distributed IT systems. This expansion is accompanied by the growing tendency to implement machine learning (ML) in edge devices. Thus, edge computing (EC) with integrated ML facilitates the main goal of the EC paradigm, moving data processing to edge devices and reducing the latency caused by cloud-based processing. All these goals are further enhanced with machine learning techniques utilized to increase the efficiency of the edge nodes. This paper deals with the importance of the usage of renewable energy sources in a particular field of interest covering IoT systems and sensor networks. This paper targets the problem of powering the sensor node, especially in complex systems. The importance of powering wireless sensor nodes in IoT and sensor networks grows together with the increasing number of utilized sensor nodes. The complexity of the system, the largernumber of nodes, and the dispersity and non-easy reachability of the locations where the sensor nodes are deployed increase the importance of efficient powering of sensor nodes. Considering the available types of renewable energy sources, solar power distinguished itself as the most suitable one and emerged as the major source for powering sensor nodes. So, the consideration of using sensor nodes and collecting sensor data for estimating solar panel performances and therefore solar panels and power potential can improve the efforts in this direction. The utilization of artificial intelligence (AI) on sensor nodes results in the attainment of edge intelligence (EI). EI represents the data analysis and solution recommendations at the point where the data are generated or acquired, thus saving the time and sometimes bandwidth of the sensor networks. Generally, edge intelligence means the implementation of AI at the far end of IoT and sensor networks, more precisely on sensor nodes and microcontroller boards. EI [1] can improve the process of the collection of solar radiation data and make the estimation of solar panel performance more efficient and more accurate. In addition, the utilization of edge intelligence can make solar radiation data collection more massive and can be enabled on the sensor nodes without solar panels. In an effort to define and simplify the process of the efficient implementation of AI on sensor nodes in existing and future sensor networks, this paper presents the methodology for implementing edge intelligence on wireless sensor nodes for solar panel performance (voltage output) estimation and forecasting. The AI on the edge devices is used to predict solar panel behavior depending on various ambient parameters (visible and UV light intensity, air temperature humidity, and solar panel temperature). AI-enhanced estimation should enable the estimation of solar panel performance at sensor stations without solar panels--with the usage of other sensors. Generally, PV module performance parameters are evaluated based on I-V and P-V curves and numerous other parameters [2]. In this research, we used the open-circuit voltage (V\({}_{\text{oc}}\)) parameter. V\({}_{\text{oc}}\) is measured under the standard test (STC) or real-time operating conditions. It is measured with a voltmeter or voltage sensor when the panel is not connected to any equipment. The value of the voltage in this case is generally higher than the maximum voltage of the panel. There are several reasons for such an approach. First, open-circuit voltage (V\({}_{\text{oc}}\)) is valuable for system planning to avoid overpowering electronics; in this case, it is interesting for the potential design of solar-powered sensor nodes. Second, it is interesting to investigate the influence of light intensity and temperature on solar panel performance and V\({}_{\text{oc}}\) is suitable for this research due to its dependency.