Most household photovoltaic (PV) panels are installed in a fixed arrangement (fixed-type) based on a rule of thumb that comes from experience. The azimuth angle is usually set facing south with a tilt angle equivalent to the location's latitude. However, research shows that this is not the best for all geographical locations and during all hours of the day. Consequently, this brings up the hypothesis that optimizing the PV system's azimuth and tilt angles would ensure that the PV panel produces the most possible output as it is adjusted to face the sun most effectively during peak sunlight hours. This work provides two optimization platforms to maximize the harvested energy from fixed-type solar photovoltaic (PV) systems. The first platform is based on a combination of factors such as climate data, geographical location, and seasonal variations to maximize the solar energy harvested. Results from the implementation of the proposed platform indicate that reasonable adjustments to the tilt and azimuth angles based on the combined information enhanced the power generation by the PV system over one year. The proposed platform was verified experimentally, and the results show a 4.5% increase in power generation over one year, considering the Tyler, TX, location. In certain months, the increase in power generation reached almost 13.37%. Moreover, the second optimization system is developed as an extension of the initial platform, taking into account the electricity requirements to minimize the cost of energy (COE). This platform aligns the number of solar panels and the angles of solar modules in a way that ensures a substantial portion of the household's electricity needs are met by solar power, all while keeping costs at their lowest. The second optimization underwent testing to attain the most favorable cost-saving ratio. It was developed using various scenarios that align with user requirements and behavior. For instance, it was determined that selling excess solar panel electricity to the grid could significantly contribute to overall system cost savings. The platform can accommodate additional scenarios, adapting to user habits and delivering optimal outcomes in terms of cost reduction. The system has successfully demonstrated a significant cost reduction of 7–11% for Tyler, TX, for different scenarios and 13–18% for Corpus Christi, TX, in the results, along with additional energy savings that can either be sold to the grid or stored for future use.

Date of publication

Fall 2023

Document Type




Persistent identifier


Committee members

Muath Salim,Nael Barakat, Shih-feng Chou


Masters in Mechanical Engineering

Available for download on Wednesday, December 03, 2025