Version 1
: Received: 6 October 2021 / Approved: 7 October 2021 / Online: 7 October 2021 (10:29:25 CEST)
How to cite:
Cho, M.; Lee, C.; Chang, J. Application of Parallel ANN-PSO to Hourly Solar PV Estimation. Preprints2021, 2021100112. https://doi.org/10.20944/preprints202110.0112.v1
Cho, M.; Lee, C.; Chang, J. Application of Parallel ANN-PSO to Hourly Solar PV Estimation. Preprints 2021, 2021100112. https://doi.org/10.20944/preprints202110.0112.v1
Cho, M.; Lee, C.; Chang, J. Application of Parallel ANN-PSO to Hourly Solar PV Estimation. Preprints2021, 2021100112. https://doi.org/10.20944/preprints202110.0112.v1
APA Style
Cho, M., Lee, C., & Chang, J. (2021). Application of Parallel ANN-PSO to Hourly Solar PV Estimation. Preprints. https://doi.org/10.20944/preprints202110.0112.v1
Chicago/Turabian Style
Cho, M., Chien-Hsing Lee and Jyh-Ming Chang. 2021 "Application of Parallel ANN-PSO to Hourly Solar PV Estimation" Preprints. https://doi.org/10.20944/preprints202110.0112.v1
Abstract
Based on collected power generation data associated with solar irradiance, PV system conversion efficiency and cell temperature, an hourly solar PV power estimation model by a parallel artificial neural network (ANN) and particle swarm optimization (PSO) algorithm is proposed. Weight matrices related to different seasons and geographic areas for estimation power generations have been trained by real measured operation data. The parallel PSO algorithm with heuristic global optimization technique assists the ANN training process to get near optimal solutions precisely. The accuracy and reliability of estimation is audited by actual details of photovoltaic power stations in various regions and scales. The results of estimation model can not only assist the electricity dispatcher to explicitly monitor the trend of solar power generation in different areas, but also coordinate with traditional power plants to meet load demand more accurately. The presented model will bring benefits to power dispatching for larger scales of intermittent and unstable solar power generation.
Engineering, Electrical and Electronic Engineering
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
