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10 August 2023 Sequence-to-subsequence learning with 1D-CNN for behind-the-meter solar generation disaggregation
Zhukui Tan, Ming Zeng, Bin Liu, Shengyong Feng, Qingquan Luo
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Proceedings Volume 12748, 5th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2023); 127482E (2023) https://doi.org/10.1117/12.2689556
Event: 5th International Conference on Information Science, Electrical and Automation Engineering (ISEAE 2023), 2023, Wuhan, China
Abstract
In recent years, the use of rooftop photovoltaic (PV) has increased as countries upgrade their energy systems. However, estimating the impact of behind-the-meter PV on grid operation requires precise physical models and weather information, which is not practical. To address this issue, we propose a data-driven approach using a sequence-to-subsequence (Seq2subseq) PV decomposition model based on the one-dimensional convolutional neural network (1D-CNN). This model automatically extracts temporal features from net metered sequences and outputs behind-the-meter PV generation using a sliding window. We evaluated our model on 184 rooftop PV users in the SGSC dataset, demonstrating its accuracy and ability to generalize across different climates. Our proposed approach offers an effective solution for real-world applications.
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Zhukui Tan, Ming Zeng, Bin Liu, Shengyong Feng, and Qingquan Luo "Sequence-to-subsequence learning with 1D-CNN for behind-the-meter solar generation disaggregation", Proc. SPIE 12748, 5th International Conference on Information Science, Electrical, and Automation Engineering (ISEAE 2023), 127482E (10 August 2023); https://doi.org/10.1117/12.2689556
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KEYWORDS
Photovoltaics
Data modeling
Machine learning
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