Short-term power prediction of the photovoltaic power generation on the basis of the AOA-DELM

Ziwei Liu; Lijia Liu

doi:10.1117/12.3024369

13 May 2024 Short-term power prediction of the photovoltaic power generation on the basis of the AOA-DELM

Ziwei Liu, Lijia Liu

Proceedings Volume 13159, Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023); 131590F (2024) https://doi.org/10.1117/12.3024369
Event: Eighth International Conference on Energy System, Electricity and Power (ESEP 2023), 2023, Wuhan, China

Abstract

To enhance the accuracy of short-term power prediction for photovoltaic (PV) power generation and ensure stable power system operations following the grid connection of PV power plants, a combined model is proposed. This model utilises an Algorithmic Optimisation Algorithm (AOA) to optimise a Deep Extreme Learning Machine (DELM) for short-term power prediction of PV power generation. The DELM model’s hyperparameters are optimised with AOA to predict the PV data’s output power. The simulation results show that for measured data of a PV power plant in the Australian region, the AOA-DELM model has greater power prediction accuracy compared to other prediction models. This verifies the efficacy and adaptability of the model.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Ziwei Liu and Lijia Liu "Short-term power prediction of the photovoltaic power generation on the basis of the AOA-DELM", Proc. SPIE 13159, Eighth International Conference on Energy System, Electricity, and Power (ESEP 2023), 131590F (13 May 2024); https://doi.org/10.1117/12.3024369

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available