Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal International Journal of Applied Power Engineering (IJAPE)
Yaser Pakzad Jafarabadi
Texas Tech University
Electrical & Electronics Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Using machine learning prediction to design an optimized renewable energy system for a remote area in Italy Ali Rezaei; Afshin Balal; Yaser Pakzad Jafarabadi
International Journal of Applied Power Engineering (IJAPE) Vol 12, No 3: September 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijape.v12.i3.pp331-340

Abstract

Due to the lack of fossil fuels, there is a significant demand to employ renewable energy systems (RES) worldwide. This paper proposes designing an optimized RES for a remote microgrid that relies solely on solar and wind sources. The proposed RES aims to provide reliable and efficient energy to the microgrid by using machine learning algorithms to forecast the power output of the solar and wind sources. This forecasting will help the system to anticipate and adjust to changes in the weather patterns that may affect the availability of solar and wind. In addition, the system advisor model (SAM) software is used to design the hybrid solar/wind system, considering factors such as the size of the microgrid and the available resources. The system comprises a 60-kW wind system of ten turbines and a 100-kW PV system spread out over the houses. The results show that random forest regression (RFR) models achieved a high level of accuracy in predicting solar power generation, as evidenced by their low mean squared error (MSE) and high R² values. Additionally, a proposed hybrid system can generate enough energy to meet the area's needs.
Co-Authors Afshin Balal Ali Rezaei