Overview The growing demand for clean and sustainable energy has made solar power one of the most promising renewable energy sources across the world. However, the amount of electricity generated by solar panels is greatly influenced by changing environmental conditions such as temperature, humidity, wind speed, cloud cover, and solar irradiance. Predicting solar power generation accurately is essential for improving energy management, optimizing system performance, and supporting the efficient use of renewable resources. This research presents an Artificial Intelligence (AI)-based solar power monitoring system that utilizes Machine Learning techniques to estimate solar power generation using environmental data. The primary objective of this work is to develop a simple, intelligent, and cost-effective prediction model capable of forecasting solar energy output without relying on expensive monitoring equipment or complex hardware installations. The proposed system demonstrates how historical weather-related data can be transformed into meaningful predictions through data-driven analysis. By using readily available environmental information, this approach provides a practical alternative to traditional monitoring systems while reducing implementation costs and increasing accessibility for educational and research purposes. The study also highlights the growing role of AI in addressing challenges associated with renewable energy generation and resource optimization. Research Methodology The prediction model was developed using Python and implemented with the Random Forest Regression algorithm, which is widely recognized for its accuracy, robustness, and ability to model complex relationships between multiple variables. The research followed a structured workflow consisting of data collection from publicly available sources, data preprocessing, exploratory data analysis (EDA), feature selection, model training, model testing, and performance evaluation. The dataset was carefully cleaned and prepared before dividing it into training and testing subsets to ensure reliable model performance. To evaluate the effectiveness of the prediction model, standard Machine Learning performance metrics, including Mean Squared Error (MSE) and the Coefficient of Determination (R² Score), were used. These evaluation methods provided valuable insights into the model's predictive capability and overall performance. Results and Discussion The developed model successfully captured the relationship between environmental parameters and solar power generation, producing reliable predictions with good accuracy. The evaluation results demonstrate that the proposed approach can effectively estimate expected solar energy output under different weather conditions. The findings indicate that Machine Learning can significantly improve solar power forecasting while reducing dependence on specialized monitoring infrastructure. The proposed system provides an efficient and practical solution that is suitable for academic research, educational projects, and introductory renewable energy forecasting applications. Research Significance This work demonstrates the practical application of Artificial Intelligence, Machine Learning, and Data Analytics in the field of renewable energy. It shows how intelligent prediction models can contribute to better energy planning, efficient utilization of solar resources, and improved operational decision-making. The research also provided valuable practical experience in data preprocessing, predictive modeling, feature engineering, model evaluation, data visualization, and Python programming. More importantly, it emphasizes how interdisciplinary technologies can be combined to develop innovative and sustainable engineering solutions that address real-world energy challenges. Future Scope Although the current implementation focuses on historical weather data and predictive modeling, the proposed framework offers significant opportunities for future enhancement. The system can be extended by integrating real-time weather APIs, IoT-enabled sensors for live solar panel monitoring, cloud-based data storage, interactive dashboards for visualization, and advanced Deep Learning algorithms to further improve prediction accuracy. Future versions may also incorporate smart grid technologies, mobile applications, and automated energy management systems, making the solution more practical for real-world deployment. Such improvements would transform the proposed model into a comprehensive intelligent solar energy monitoring platform capable of supporting sustainable energy management and advanced renewable energy research. Conclusion This research demonstrates that Machine Learning can be effectively applied to improve solar power monitoring and prediction using environmental data. By utilizing the Random Forest Regression algorithm, the proposed system successfully estimates solar power generation with reliable accuracy while eliminating the need for expensive monitoring infrastructure. The results highlight the potential of data-driven approaches to support more efficient renewable energy management and informed decision-making. Beyond the technical implementation, this project reflects the growing importance of integrating Artificial Intelligence with sustainable energy solutions to address real-world challenges. The proposed framework is simple, scalable, and adaptable, making it suitable for educational purposes, research applications, and future enhancements. With the integration of real-time weather data, IoT devices, cloud computing, and advanced Machine Learning techniques, this work has the potential to evolve into a comprehensive intelligent solar energy monitoring platform. Overall, this research provides a strong foundation for future innovation in AI-driven renewable energy systems while contributing to the broader goal of building smarter, cleaner, and more sustainable energy solutions. It also demonstrates how accessible Machine Learning techniques can be leveraged to solve practical engineering problems and encourages further exploration of intelligent technologies in advancing the global transition toward sustainable energy.
Soumalya Ghosh (Sun,) studied this question.