TY - GEN
T1 - Hybrid Random Forest and Particle Swarm Optimization Algorithm for Solar Radiation Prediction
AU - Gupta, Sunayana
AU - Katta, Anudeep Reddy
AU - Baldaniya, Yashi
AU - Kumar, Rajesh
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/10/30
Y1 - 2020/10/30
N2 - Due to increased pollution, greenhouse effect and global warming resulting from power production using fossil fuels, there is increased penetration of renewable energy sources into the power system. The concept of microgrids has made solar radiation an indispensable source of power in the distribution system. But the power production using solar energy is highly variable and weather dependent which creates a power imbalance into the system when it is penetrated without forecasting. Therefore, solar power prediction plays a critical role in the proper usage of solar energy while keeping the system stable. For automating the power system, the forecast needs to be very accurate and thus, it is needed to improve the existing forecasting techniques. In this study, we have proposed a solar radiation scheme based on various meteorological factors, including temperature, humidity, wind speed, and others. We have introduced a hybrid model for prediction which optimizes the parameters of Random Forest using Particle Swarm Optimization technique.
AB - Due to increased pollution, greenhouse effect and global warming resulting from power production using fossil fuels, there is increased penetration of renewable energy sources into the power system. The concept of microgrids has made solar radiation an indispensable source of power in the distribution system. But the power production using solar energy is highly variable and weather dependent which creates a power imbalance into the system when it is penetrated without forecasting. Therefore, solar power prediction plays a critical role in the proper usage of solar energy while keeping the system stable. For automating the power system, the forecast needs to be very accurate and thus, it is needed to improve the existing forecasting techniques. In this study, we have proposed a solar radiation scheme based on various meteorological factors, including temperature, humidity, wind speed, and others. We have introduced a hybrid model for prediction which optimizes the parameters of Random Forest using Particle Swarm Optimization technique.
KW - Decision Tree
KW - Distribution Automation
KW - Particle Swarm Optimization
KW - Radiation
KW - Random Forest
UR - http://www.scopus.com/inward/record.url?scp=85097639915&partnerID=8YFLogxK
U2 - 10.1109/ICCCA49541.2020.9250715
DO - 10.1109/ICCCA49541.2020.9250715
M3 - Conference contribution
AN - SCOPUS:85097639915
T3 - 2020 IEEE 5th International Conference on Computing Communication and Automation, ICCCA 2020
SP - 302
EP - 307
BT - 2020 IEEE 5th International Conference on Computing Communication and Automation, ICCCA 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th IEEE International Conference on Computing Communication and Automation, ICCCA 2020
Y2 - 30 October 2020 through 31 October 2020
ER -