TY - GEN
T1 - Quantum Machine Learning for Photovoltaic Topology Optimization
AU - Uehara, Glen S.
AU - Narayanaswamy, Vivek
AU - Tepedelenlioglu, Cihan
AU - Spanias, Andreas
N1 - Funding Information:
ACKNOWLEDGMENT This research is supported in part by the NSF MRI Award number 2019068, the NSF I/UCRC award 1540040 and the Quantum Computing NCSS SenSIP I/UCRC project.
Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Photovoltaic array topology optimization was shown to improve efficiency in renewable energy plants. Previous studies demonstrated improvements via simulation at the level of 7-12% or more. In this paper, we describe solar array topology optimization systems based on quantum machine learning algorithms. The idea of using quantum machine learning can be useful in cases where the objective is to optimize power output in large sites with several thousands of panels. We specifically propose and assess a quantum circuit for a neural network implementation for photovoltaic topology optimization. Results and comparisons are presented using classical and quantum neural network implementations. In addition, solar array topology optimization simulations and comparisons using a quantum neural network are described for different numbers of qubits.
AB - Photovoltaic array topology optimization was shown to improve efficiency in renewable energy plants. Previous studies demonstrated improvements via simulation at the level of 7-12% or more. In this paper, we describe solar array topology optimization systems based on quantum machine learning algorithms. The idea of using quantum machine learning can be useful in cases where the objective is to optimize power output in large sites with several thousands of panels. We specifically propose and assess a quantum circuit for a neural network implementation for photovoltaic topology optimization. Results and comparisons are presented using classical and quantum neural network implementations. In addition, solar array topology optimization simulations and comparisons using a quantum neural network are described for different numbers of qubits.
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U2 - 10.1109/IISA56318.2022.9904368
DO - 10.1109/IISA56318.2022.9904368
M3 - Conference contribution
AN - SCOPUS:85141050973
T3 - 13th International Conference on Information, Intelligence, Systems and Applications, IISA 2022
BT - 13th International Conference on Information, Intelligence, Systems and Applications, IISA 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 13th International Conference on Information, Intelligence, Systems and Applications, IISA 2022
Y2 - 18 July 2022 through 20 July 2022
ER -