## 2 Related Works
Similar but newer and more advanced ESP32-based development boards implemented with ML are used in the following projects. The authors present the Wi-CaL crowd counting and localization system [18] with implemented machine learning (ML) and deep learning (DL). ESP32 also used machine learning in [19] for distance estimation appliances in real time. Paper [20] analyses the feasibility of deploying deep networks on ESP32 devices with TensorFlow. Research presented in [21] proposes the use of IoT and ML in hydroponic systems. The goal of the system is to enhance the growth of Holy Basil. The research [22] presents the Respiration DT (ResDT) model based on Wi-Fi Carrier State Information (CSI). The model uses machine learning for the monitoring and classification of patient respiration. Furthermore, it is interesting to investigate the utilization of ML in evaluating and estimating solar panel performance. The authors of [23] suggest a decision-making model data processing technique and machine learning. The research findings justify the implementation of data science and machine learning in a solar PV panel cleaning system. Paper [24] introduces semi-supervised learning and one-class classification methods based on autoencoders. The presented methodology improves the nonlinear data representation of solar behavior. The authors in [25] evaluated more than 100 research articles to investigate ML implementation in solar cell fabrication. The findings show that the Random Forest (RF), linear regression (LR), XGBoost, and artificial neural network (ANN) algorithms are the most commonly employed techniques. Further, research results show XGBoost's superior performance in optoelectronic prediction, while RF, LR, and ANN algorithms are better suited for predicting electrical parameters. The review of existing machine learning (ML) approaches used in PV power forecasting, focusing on short-term horizons, is presented in [26]. The overview contains factors affecting solar PV power forecasting. It is interesting to examine the tools for implementing ML in edge devices. TinyML supports running machine learning at embedded edge devices with limited processor and memory resources. The important issue for edge devices is power consumption, which should be minimal. So, TinyML enables migration to low-power IoT-based embedded edge devices and allows the development of novel applications without the need for processing on the cloud [27; 28; 29]. The overview of TinyML benefits is given in [30]. Authors in [30] conclude that TinyML is considered a promising AI alternative for extremely low-profile devices. The article [31] presents prediction methods based on artificial neural network (ANN) models. TinyML allows importing pre-trained ML models on the edge devices, thus achieving ML-as-a-Service (MLaaS). Paper [32] presents a TinyMLaaS (TMLaaS) architecture that presents several design variations in terms of energy consumption, security, privacy, and latency. The implementation of TinyML is possible with the numerous frameworks. A good overview of TinyML frameworks is given in [28; 29], and benefits are given in [33]. One of these frameworks is Micromlgen [34], and this framework is used in this research. The contribution presented in this paper is a methodology developed for the efficient implementation of ML on sensor nodes to estimate solar panel performance. The estimation should be made on sensor nodes not having solar panels; thus, it will be applicable for already designed and deployed sensor nodes, which can be used for estimating the solar potential of the current sensor location and its feasibility for upgrading solar-powered sensor nodes. This method includes the definition of the toolset: Python, Scikit-learn package, micromlgen library, and ESP8266-based development boards. This paper evaluates the methodology by evaluating the model's accuracy.