TY - GEN
T1 - Artificial Intelligence-based Irradiance and Power consumption prediction for PV installations
AU - Valeria-Aguirre, Pablo
AU - Risso, Nathalie
AU - Campos, Pedro G.
AU - Lagos-Carvajal, Karla
AU - Caro, Isidora A.
AU - Salgado, Fabricio
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021
Y1 - 2021
N2 - Currently, several countries are seeking to change their energy matrices towards more sustainable sources. In Chile, one of the renewable sources with increased participation is photovoltaics. However, photovoltaic energy sources have an intrinsic variability, which combined with variable demand imposes a challenge for proper design. Currently, tools available for the study of this variability are either complex or expensive. With the advent of digitalization, there is an opportunity to incorporate tools based on Artificial Intelligence to improve forecasting for medium and low power installations. This work presents an application of machine learning tools for irradiance and power consumption forecasting. The methodology is intended to be implemented as a low cost solution for small scale generation. The results show that it is possible to predict irradiance and energy consumption through historical data, concluding that the methodology based on Machine Learning is able to support the decision making for the improvement of photovoltaic systems.
AB - Currently, several countries are seeking to change their energy matrices towards more sustainable sources. In Chile, one of the renewable sources with increased participation is photovoltaics. However, photovoltaic energy sources have an intrinsic variability, which combined with variable demand imposes a challenge for proper design. Currently, tools available for the study of this variability are either complex or expensive. With the advent of digitalization, there is an opportunity to incorporate tools based on Artificial Intelligence to improve forecasting for medium and low power installations. This work presents an application of machine learning tools for irradiance and power consumption forecasting. The methodology is intended to be implemented as a low cost solution for small scale generation. The results show that it is possible to predict irradiance and energy consumption through historical data, concluding that the methodology based on Machine Learning is able to support the decision making for the improvement of photovoltaic systems.
KW - PV systems
KW - Renewable Energy
KW - Sustainability
UR - https://www.scopus.com/pages/publications/85127001822
UR - https://www.scopus.com/pages/publications/85127001822#tab=citedBy
U2 - 10.1109/CHILECON54041.2021.9702890
DO - 10.1109/CHILECON54041.2021.9702890
M3 - Conference contribution
AN - SCOPUS:85127001822
T3 - 2021 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies, CHILECON 2021
BT - 2021 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies, CHILECON 2021
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2021 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies, CHILECON 2021
Y2 - 6 December 2021 through 9 December 2021
ER -