Date of Award


Document Type


Degree Name



Computer Science and Engineering

First Advisor

Amin Khodaei, Ph.D.

Second Advisor

Ali Besharat

Third Advisor

Mohammad Matin

Fourth Advisor

Jun Zhang


Economic analysis, Forecasting, Neural network, Planning, PV, Solar


The rapid growth of Photovoltaic (PV) technology has been very visible over the past decade. Recently, the penetration of PV plants to the existing grid has significantly increased. Such increase in the integration of solar energy has brought attention to the solar irradiance forecasting. This thesis presents a thorough research of PV technology, how solar power can be forecasted, and PV planning under uncertainty.

Over the last decade, the PV was one of the fastest growing renewable energy technologies. However, the PV system output varies based on weather conditions. Due to the variability and the uncertainty of solar power, the integration of the electricity generated by PV system is considered one of the challenges that have confronted the PV system. This thesis proposes a new forecasting method to reduce the uncertainty of the PV output so the power operator will be able to accommodate its variability. The new forecasting method proposes different processes to be undertaken before the data is fed to the forecasting model. The method converts the data sets included in the forecasting from non-stationary data to a stationary data by applying different processes including: removing the offset, removing night time solar values, and normalization. The new forecasting method aims to reduce the forecasting error and analyzes the error effect on the long term planning through calculating the payback period considering different errors.

Publication Statement

Copyright is held by the author. User is responsible for all copyright compliance.


Received from ProQuest

Rights holder

Mohana Shandal Alanazi

File size

102 p.

File format





Engineering, Electrical engineering, Energy