Creating optimized machine learning pipelines for PV power generation forecasting using the grid search and tree-based pipeline optimization tool

Abstract

Demand for electric power, especially amidst limited fossil fuel-based generation capacity, has elevated renewable energy sources to a forefront solution for the growing energy needs. Solar energy, a key renewable source through photovoltaic (PV) panels, faces challenges such as intermittency and non-dispatchability. Thus, recent research has focused on developing programs to predict near-future solar energy generation, with machine learning being a pivotal approach. This article details the creation of an effective machine-learning pipeline for predicting future hourly power generation based on weather data (e.g. temperature, humidity, irradiance). The pipeline, aimed at a scheduling system in a farm equipped with a Solar Power System (SPS) in Al-Salt, Jordan, was optimized using Genetic Algorithm and Grid Search methods. The objective of this article is to create an optimal pipeline with minimal loss. The evaluation shows that ensemble regressors, especially Gradient Boosting Regressors, are effective. This is evidenced in the grid search pipeline, which outperformed the TPOT optimization pipeline-derived pipeline, the latter including stacked ensemble regressors and sequential preprocessors.

Keywords:

• Solar energy forecasting
• renewable energy systems
• energy scheduling optimization
• demand response
• machine learning
• genetic algorithm
• grid search
• tree-based pipeline optimization tool

Reviewing Editor:

• Ai Qingsong, Senior Editor, Wuhan University of Technology, China

Subjects:

• OptimizationRenewable EnergyMachine LearningSolar energy

Acknowledgment

We would like to express our sincere thanks to Beyond Limits – an Industrial and Enterprise-grade AI technology company – for their collaboration and for providing mentorship during the research period.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

Data are available on request from the authors.

Additional information

Notes on contributors

Hussam J. Khasawneh

Hussam J. Khasawneh is an Associate Professor of Mechatronics Engineering at the University of Jordan and Associate Professor of Electrical Engineering Al Hussein Technical University (HTU). His research focuses on advanced mechatronics and energy systems.

Zaid A. Ghazal

Zaid A. Ghazal is mechatronics engineering graduate from the University of Jordan.

Waseem M. Al-Khatib

Waseem M. Al-Khatib is mechatronics engineering graduate from the University of Jordan.

Ahmad M. Al-Hadi

Ahmad M. Al-Hadi is mechatronics engineering graduate from the University of Jordan.

Zaid M. Arabiyat

Zaid M. Arabiyat is a mechatronics engineering graduate from the University of Jordan.