An AI-Powered Solar Management Suite Integrating Precision Forecasting, Smart Scheduling, and Financial Analytics

Abstract

The increasing global energy demand and environmental concerns have accelerated the adoption of renewable power sources, particularly solar energy. However, solar power generation is highly dependent on fluctuating atmospheric conditions, which makes accurate forecasting challenging. This study presents an intelligent solar prediction system that estimates energy generation using weather-based parameters and location-specific data. The proposed model employs Linear Regression trained on historical meteorological and solar datasets to generate reliable forecasts. Additionally, the framework features a Solar Payback Calculator, Smart Appliance Scheduling, and Automated Maintenance Alerts. By integrating automated location detection, electricity bill analysis, and PDF report generation, the framework simplifies user interaction while improving overall energy management efficiency.

Introduction

The global energy sector is shifting toward renewable sources due to increasing electricity demand and the depletion of fossil fuels. Solar energy has become an important sustainable solution because it is abundant, renewable, and environmentally friendly. However, solar power generation is highly variable since it depends on changing weather conditions such as solar radiation, temperature, humidity, and cloud cover. This variability makes accurate prediction of solar energy output difficult and can lead to inefficient energy planning and financial losses.

Traditional forecasting methods rely on static models and historical averages, which cannot effectively adapt to real-time weather changes and often require complex manual inputs from users. To address these limitations, the study proposes Heliocast, an intelligent solar energy forecasting system that combines Machine Learning with automated data acquisition. The system uses a Linear Regression model trained on historical weather and solar generation data to predict solar energy output.

Heliocast introduces several advanced features, including live geolocation to obtain precise location data automatically and Optical Character Recognition (OCR) to extract electricity consumption and tariff details directly from uploaded electricity bills. This reduces manual input errors and improves usability. The system also provides forecasts of energy production, potential cost savings, and carbon emission reductions through an interactive web interface and downloadable PDF reports.

The methodology involves collecting historical weather data, cleaning and analyzing it, and testing multiple machine learning models such as Linear Regression, Random Forest, and Gradient Boosting. Among these, Linear Regression achieved the best balance of accuracy and efficiency, with a high prediction accuracy (R² ≈ 0.995).

The proposed framework follows a client–server architecture with four layers: data acquisition, intelligent processing, AI forecasting, and reporting. It also includes features like a solar payback calculator, smart appliance scheduling recommendations, and maintenance alerts.

Results show that the system provides reliable solar energy forecasts, accurate electricity bill analysis, and improved user convenience. The system also highlights financial and environmental benefits, estimating daily savings of ?109–?143 and carbon emission reductions of 14–18 kg of CO? per day. Overall, Heliocast offers a practical and user-friendly solution for improving solar energy planning and promoting sustainable energy adoption.

Conclusion

This study introduced Heliocast, a machine learning–driven solar forecasting platform developed to address the drawbacks of conventional estimation approaches. By integrating a Linear Regression model with automated geolocation, OCR-enabled billing, and intelligent maintenance alerts, the system provides a precise and user-centric energy suite. The platform converts forecasts into actionable metrics, including appliance scheduling tips and investment payback periods, enabling holistic planning for residential and commercial solar installations.

References

[1] P. Kumari and A. Kumar, “Solar power forecasting using ARIMA model,” International Journal of Renewable Energy Research, 2018. [2] P. Sharma et al., “Enhancing and optimizing solar power forecasting in Dhar district of India using machine learning,” Smart Grids and Sustainable Energy, 2024. [3] F. Wang et al., “A review of deep learning for renewable energy forecasting,” Energy Conversion and Management. [4] A. K. Yadav et al., Intelligent Data Analytics for Solar Energy Prediction and Forecasting. Elsevier, 2021. [5] J. Antonanzas et al., “Review of photovoltaic power forecasting,” Solar Energy, 2016. [6] V. Shingne et al., “AI-based solar energy forecasting,” International Research Journal of Engineering and Technology (IRJET), vol. 12, no. 04, Apr. 2025.

Copyright

Copyright © 2026 Siddhant Gangavane, Omkar Dhoke, Rohan Ghadage, Sahil Ghodvinde, Prof. Monica Shetty. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.