The DOI matches its Crossref record; 39 of the 39 references it cites were found in a registry or an index.
Spatio-Temporal Graph Neural Networks for Multi-Site PV Power Forecasting
Open accessAbstract
Accurate forecasting of solar power generation with fine temporal and spatial resolution is vital for the operation of the power grid. However, state-of-the-art approaches that combine machinelearning with numerical weather predictions (NWP) have coarse resolution. In this paper, we take a graph signal processing perspective and model multi-site photovoltaic (PV) production time series as signals on a graph to capture their spatio-temporal dependencies and achieve higher spatial and temporal resolution forecasts. We present two novel graph neural network models for deterministic multi-site PV forecasting dubbed the graph-convolutional long short term memory (GCLSTM) and the graph-convolutional transformer (GCTrafo) models. These methods rely solely on production data and exploit the intuition that PV systems provide a dense network of virtual weather stations. The proposed methods were evaluated in two data sets for an entire year: 1) production data from 304 real PV systems, and 2) simulated production of 1000 PV systems, both distributed over Switzerland. The proposed models outperform state-of-the-art multi-site forecasting methods for prediction horizons of six hours ahead. Furthermore, the proposed models outperform state-of-the-art single-site methods with NWP as inputs on horizons up to four hours ahead.
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Cites
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Exploiting Generative AI to Scale up Intelligent Tutoring Systems
Jakubův, J., Chvalovský, K., Goertzel, Z., Kaliszyk, C., Olšák, M., Piotrowski, B., Schulz, S., Suda, M., & Urban, J. (2023) · DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · doi:10.4230/lipics.itp.2023.19 · Cited by 76,658
A Comprehensive Survey on Graph Neural Networks
Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., & Yu, P. S. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.2978386 · Cited by 11,000
Graph neural networks: A review of methods and applications
Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., Wang, L., Li, C., & Sun, M. (2020) · AI Open · doi:10.1016/j.aiopen.2021.01.001 · Cited by 6,880
Advances In Deep Learning On Graphs (Gsp'18 Workshop)
Defferrard, M. (2016) · Figshare · doi:10.5281/zenodo.1286817 · Cited by 4,613
Spectral Networks and Locally Connected Networks on Graphs
Bruna, J., Zaremba, W., Szlam, A., & LeCun, Y. (2013) · arXiv · doi:10.48550/arxiv.1312.6203 · Cited by 3,983
Spatio-Temporal Graph Convolutional Networks: A Deep Learning Framework for Traffic Forecasting
Yu, B., Yin, H., & Zhu, Z. (2018) · Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2018/505 · Cited by 3,728
A Comprehensive Survey on Graph Neural Networks
Wu, Z., Pan, S., Chen, F., Long, G., Zhang, C., & Yu, P. (2020) · UTS ePRESS (University of Technology Sydney) · Cited by 3,174
Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks
Lai, G., Chang, W.-C., Yang, Y., & Liu, H. (2018) · The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval · doi:10.1145/3209978.3210006 · Cited by 2,152
Cited by
DiffSTG: Probabilistic Spatio-Temporal Graph Forecasting with Denoising Diffusion Models
Wen, H., Lin, Y., Xia, Y., Wan, H., Wen, Q., Zimmermann, R., & Liang, Y. (2023) · Proceedings of the 31st ACM International Conference on Advances in Geographic Information Systems · doi:10.1145/3589132.3625614 · Cited by 130
Metadata
- Authors
- Simeunovic, J., Schubnel, B., Alet, P.-J., & Carrillo, R. E.
- Venue
- IEEE Transactions on Sustainable Energy
- Published
- 2022-04
- Volume
- 13
- Issue
- 2
- Pages
- 1210-1220
- ISSN
- 1949-3029 (ISSN-L)
- 1949-3037
- OpenAlex
- W3183572711
- Cited by
- 268
- References
- 39
- FWCI
- 11.47
- Type
- journal article
- Language
- en
- Open access
- green
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- Simeunovic, J., Schubnel, B., Alet, P.-J., & Carrillo, R. E. (2022). Spatio-Temporal Graph Neural Networks for Multi-Site PV Power Forecasting. IEEE Transactions on Sustainable Energy. https://doi.org/10.1109/tste.2021.3125200
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
