## 6 Conclusions  
The importance of the increase in alternative energy sources, especially solar power, reflects the field of powering electronic devices. In almost the last two decades, the number of electronic devices has increased multiple times, raising the problem of their electrical powering. This problem arises more with the introduction of wireless sensor networks, and even more with IoT and Smart technologies, such as Smart Cities, Smart Agriculture, Smart Manufacturing, etc. The problem we are facing can be solved with solar-powered sensor nodes. Therefore, it is very important to find a way for efficient powering of sensor nodes. This paper proposes a methodology that includes ML for the assessment of the solar panel performance and solar potential of the sensor node location, in cases when sensor nodes do not have solar panels.  
In summary, this paper presents the methodology for implementing edge intelligence on sensor nodes. Edge intelligence helps in forecasting solar panel voltage generation. The methodology uses acquired solar data in building ML models to be implemented on microcontrollers. The set of tools includes Python, the Scikit-learn package, the micromlgen library, and ESP8266-based development boards. The proposed model predicts solar panel voltage generation based on a single-sensor reading using a UV or BH1750 light sensor. The Random Forest and Decision Tree regressors are implemented on the ESP8266-based development board--Wemos D1 R2. The estimation accuracy of the RF model is an MSE of approximately 0.10, an MAE of 0.07 for UV and 0.04 for BH1750, and an R\({}^{2}\) of  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Parameters** & **MSE Py** & **MAE Py** & **MSE Real** & **MAE Real** & **R\({}^{2}\) Real** \\ \hline UV RF & 0.10416 & 0.05263 & 0.10683 & 0.06947 & 0.93309 \\ UV DT & 0.17864 & 0.09456 & 0.127568 & 0.07404 & 0.90461 \\ Lux RF & 0.09789 & 0.05105 & 0.10278 & 0.03912 & 0.93808 \\ Lux DT & 0.00000 & 0.00000 & 0.13726 & 0.04509 & 0.88956 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of DT and RF accuracy implemented on the ESP8266 platform.  
approximately 0.93 for both the UV and BH1750 light sensors. The Decision Tree model has a lower accuracy with an MSE between 0.13 and 0.14, MAE of 0.07 for UV and 0.04 for BH1750, and R\({}^{2}\) of 0.90 and 0.89 for the UV and BH1750 sensors, respectively. Both metrics justify the usage of the proposed methodology.  
Further work should cover analyses of implementing ESP32 sensor boards, and their comparison with ESP8266 boards. Including multiple sensor readings for predicting voltage output values of solar panels. Finally, the comparison of other toolsets will be explored in further research.  
Conceptualization, D.D. and J.P.; methodology, D.D. and V.O.; validation, D.D., J.P. and E.D.; formal analysis, V.O. and E.D.; investigation, J.P. and E.D.; resources, D.D. and J.P.; data curation, D.D. and V.O.; writing--original draft preparation, D.D., J.P. and V.O.; writing--review and editing, E.D. and V.O.; visualization, D.D.; supervision, J.P. and E.D.; project administration, E.D.; funding acquisition, E.D. All authors have read and agreed to the published version of the manuscript.  
This research was conducted through the project \"Creating laboratory conditions for research, development, and education in the field of the use of solar resources in the Internet of Things\", at the Technical Faculty \"[PERSON]\" Zrenjamin, financed by the Provincial Secretariat for Higher Education and Scientific Research, Republic of Serbia, Autonomous Province of Vojvodina, project number 142-451-3118/2023-01.  
Not applicable.  
The datasets presented in this article are not readily available because the data are part of an ongoing study.  
The authors declare no conflicts of interest.