TY - GEN
T1 - Data-Driven Model for Photovoltaic Generation
T2 - 2024 IEEE Power and Energy Society General Meeting, PESGM 2024
AU - Colón, Marcos R.Pesante
AU - Salamán, Alberto I.Cruz
AU - Figueroa, Dylan Cruz
AU - Sundararajan, Aditya
AU - Ferrari, Maximiliano
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Photovoltaic (PV) generation is a critical component of microgrids, but its accurate modeling is challenging due to the complex and dynamic interactions between solar irradiance, temperature, and PV system installation. This paper develops a multilayer perceptron (MLP) model that inputs solar irradiance and temperature to estimate the PV generation, and it compares the proposed data-driven model's performance to two well-known physical models: the single-diode model and the inverter model. The results demonstrate that all the models can reach high levels of accuracy. However, the MLP model outperforms the physical models on average by 4.5 to 6.6 percent in R squared scores and 220 to 290 Watts in RMSE scores, and it does not require physical system parameters. Moreover, the data-driven model can overcome the limitations of the lack of real-time PV generation data.
AB - Photovoltaic (PV) generation is a critical component of microgrids, but its accurate modeling is challenging due to the complex and dynamic interactions between solar irradiance, temperature, and PV system installation. This paper develops a multilayer perceptron (MLP) model that inputs solar irradiance and temperature to estimate the PV generation, and it compares the proposed data-driven model's performance to two well-known physical models: the single-diode model and the inverter model. The results demonstrate that all the models can reach high levels of accuracy. However, the MLP model outperforms the physical models on average by 4.5 to 6.6 percent in R squared scores and 220 to 290 Watts in RMSE scores, and it does not require physical system parameters. Moreover, the data-driven model can overcome the limitations of the lack of real-time PV generation data.
KW - Data-driven modeling
KW - PV generation modeling
KW - meteorological data
KW - microgrids
KW - multilayer perceptron
UR - http://www.scopus.com/inward/record.url?scp=85207450613&partnerID=8YFLogxK
U2 - 10.1109/PESGM51994.2024.10689007
DO - 10.1109/PESGM51994.2024.10689007
M3 - Conference contribution
AN - SCOPUS:85207450613
T3 - IEEE Power and Energy Society General Meeting
BT - 2024 IEEE Power and Energy Society General Meeting, PESGM 2024
PB - IEEE Computer Society
Y2 - 21 July 2024 through 25 July 2024
ER -
