Abstract
The growing utilization of solar energy and other renewable energy sources has heightened the demand for a proper forecasting model that enables efficient control of the energy supply as well as grid reliability. In this work, we concentrate about the use of novel techniques in ML models, namely Deep Neural Networks (DNN) and Convolutional Neural Networks (CNN), to try to forecast power output in a solar-powered plant using past weather and power output records. Production of solar energy is naturally volatile because it is dependent on the weather conditions and is problematic in nature when integrated into power grids. Conventional statistical forecasting models tend to miss the nonlinear and complicated manifestations between the environmental factors and the energy production. The study is rooted in the necessity to address these shortcomings by creating machine learning models capable of more dependably mirroring the trends in solar power production. The weather parameters that were used in the dataset are the ambient temperature, module temperature, and irradiation as three weather variables, whereas the parameter used in the data that were collected in a solar power plant was the DC power, AC power, daily yield, and total yield. There was a lot of preprocessing of data in the study to handle missing values to make the data consistent. These were DNN models to ensure that the complex non-linear dependency was captured, and CNN models that were used to recognize the spatial elements of the structured data. Principal research questions were aimed at discovering the key weather parameters that influence the production of solar power, as well as the determination of the suitability of DNN and CNN models to achieve correct forecasts. The data show that the accuracy of predictions using both models stands higher than traditional approaches, and CNN is better than DNN in terms of the error values (such as RMSE and MAPE). The study finds that deep learning s and specifically and ML models generally, is a feasible approach to improve solar power forecasting. Other areas for future research include hybrid models, real-time data integration and deployment in various locations all over the world to enhance the accuracy and usability of power components systems. Keywords: Solar Power Forecasting, Deep Learning, DNN, CNN, Renewable Energy, Energy Management, Machine Learning, Weather Data, Grid Reliability, Sustainable Energy.
Publication Date
10-2025
Document Type
Thesis
Student Type
Graduate
Degree Name
Professional Studies (MS)
Department, Program, or Center
Graduate Programs & Research
Advisor
Sanjay Modak
Advisor/Committee Member
Ehsan Warriach
Recommended Citation
Abdulrahman, Fatma, "Solar Power Forecasting Using Deep Learning Techniques" (2025). Thesis. Rochester Institute of Technology. Accessed from
https://repository.rit.edu/theses/12354
Campus
RIT Dubai
