A machine learning frontier for predicting LCOE of photovoltaic system economics

Bhatti, S., Khan, A. R., Zoha, A. , Hussain, S. and Ghannam, R. (2024) A machine learning frontier for predicting LCOE of photovoltaic system economics. Advanced Energy and Sustainability Research, (doi: 10.1002/aesr.202300178) (Early Online Publication)

Abstract

In this research article, the objective is to determine the return on investment (ROI) of photovoltaic (PV) power plants by employing machine learning (ML) techniques. Special focus is done on the levelized cost of electricity (LCOE) as a pivotal economic parameter crucial for facilitating economic decision-making and enabling quantitative comparisons among different energy generation technologies. Traditional methods of calculating LCOE often rely on fixed singular input values, which may fall short in addressing uncertainties associated with assessing the financial feasibility of PV projects. In response, a dynamic model that integrates essential demographic, energy, and policy data, is introduced encompassing factors such as interest rates, inflation rates, and energy yield, which are anticipated to undergo changes over the lifetime of a PV system. This dynamic model provides a more accurate estimation of LCOE. The comparative analysis of ML algorithms indicates that the auto-regression integration moving average (ARIMA) model exhibits a high accuracy of 93.8% in predicting consumer electricity prices. The validation of the model is highlighted through two case studies in the United States and the Philippines underscores the potential impact on LCOE values. For instance, in California, LCOE values could vary by nearly 30% (5.03 cents kWh−1 for singular values vs 7.09 cents kWh−1 using our ML model), influencing the perceived risk or economic feasibility of a PV power plant. Additionally, the ML model estimates the ROI for a grid-connected PV plant in the Philippines at 5.37 years, in contrast to 4.23 years using traditional methods.

• Item Type: Articles
• Status: Early Online Publication
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Zoha, Dr Ahmed and Khan, Ahsan Raza and Ghannam, Professor Rami and Bhatti, Satyam and Hussain, Dr Sajjad
• Authors: Bhatti, S., Khan, A. R., Zoha, A., Hussain, S., and Ghannam, R.
• College/School: College of Science and Engineering; College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity; College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
• Journal Name: Advanced Energy and Sustainability Research
• Publisher: Wiley
• ISSN: 2699-9412
• ISSN (Online): 2699-9412
• Published Online: 22 March 2024
• Copyright Holders: Copyright © 2024 The Authors
• First Published: First published in Advanced Energy and Sustainability Research 2024
• Publisher Policy: Reproduced under a Creative Commons license