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With the increasing penetration of renewable power sources such as wind and solar, accurate short-term, nowcasting renewable power prediction is becoming increasingly important.
Y. Ren, P. Suganthan, and N. Srikanth, “Ensemble methods for wind and solar power forecasting—a state-of-the-art review,” Renewable and Sustainable Energy Reviews , vol. 50, pp. 82–91, 2015
2015
Earlier work this paper cites.
D. Lahat, T. Adalı, and C. Jutten, “Multimodal data fusion: An overview of methods, challenges, and prospects,” Proceedings of the IEEE , vol. 103, pp. 1449–1477, 2015
2015
Earlier work this paper cites.
H. M. Bui, M. Lech, E. Cheng, K. Neville, and I. S. Burnett, “Using grayscale images for object recognition with convolutional-recursive neural network,” 2016 IEEE 6th International Conference on Communications and Electronics, IEEE ICCE 2016 , pp. 321–325, 9 2016
2016
Earlier work this paper cites.
M. Q. Raza, M. Nadarajah, and C. Ekanayake, “On recent advances in PV output power forecast,” Solar Energy , vol. 136, pp. 125–144, 10 2016
2016
Earlier work this paper cites.
P. Donti, B. Amos, and J. Z. Kolter, “Task-based end-to-end model learning in stochastic optimization,” Advances in neural information processing systems , vol. 30, 2017
2017
Earlier work this paper cites.
A. Zadeh, M. Chen, S. Poria, E. Cambria, and L.-P. Morency, “Tensor fusion network for multimodal sentiment analysis,” in Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing , 2017, pp. 1103–1114
2017
Earlier work this paper cites.
BDEW, “Standardlastprofile Strom,” 2017, data retrieved from World Development Indicators, https://www.bdew.de/energie/standardlastprofile-strom/
2017
Cited alongside, same era.
J. Zhang, R. Verschae, S. Nobuhara, and J.-F. Lalonde, “Deep photovoltaic nowcasting,” Solar Energy , vol. 176, pp. 267–276, 2018
2018
Cited alongside, same era.
T. Baltrušaitis, C. Ahuja, and L.-P. Morency, “Multimodal machine learning: A survey and taxonomy,” IEEE transactions on pattern analysis and machine intelligence , vol. 41, no. 2, pp. 423–443, 2018
2018
Cited alongside, same era.
E. Vrettos and C. Gehbauer, “A Hybrid Approach for Short-Term PV Power Forecasting in Predictive Control Applications,” in 2019 IEEE Milan PowerTech , 2019, pp. 1–6
2019
Cited alongside, same era.
S. Leva, M. Mussetta, A. Nespoli, and E. Ogliari, “PV power forecasting improvement by means of a selective ensemble approach,” in 2019 IEEE Milan PowerTech , 2019, pp. 1–5
A. Agrawal, B. Amos, S. Barratt, S. Boyd, S. Diamond, and Z. Kolter, “Differentiable convex optimization layers,” in Advances in Neural Information Processing Systems , 2019
2019
Later among the works it cites.
A. Geron, Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow . O’Reilly, 2019
2019
Later among the works it cites.
R. Ahmed, V. Sreeram, Y. Mishra, and M. D. Arif, “A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization,” Renewable and Sustainable Energy Reviews , vol. 124, 2020
2020
Later among the works it cites.
P. L. Donti, D. Rolnick, and J. Z. Kolter, “DC3: A learning method for optimization with hard constraints,” in International Conference on Learning Representations , 2021
2021
Later among the works it cites.
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2019
Cited alongside, same era.
Z. Li, K. Wang, C. Li, M. Zhao, and J. Cao, “Multimodal Deep Learning for Solar Irradiance Prediction,” in 2019 International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData) , 2019, pp. 784–792
2019
Cited alongside, same era.
Q. Xian and W. Liang, “A multi-modal time series intelligent prediction model,” in Proceeding of 2021 International Conference on Wireless Communications, Networking and Applications , Z. Qian, M. Jabbar, and X. Li, Eds. Singapore: Springer Nature Singapore, 2022, pp. 1150–1157
2022
Later among the works it cites.
D. Wahdany, C. Schmitt, and J. L. Cremer, “More than accuracy: end-to-end wind power forecasting that optimises the energy system,” Electric Power Systems Research , vol. 221, p. 109384, 2023. [Online]. Available: https://www.sciencedirect.com/science/article/pii/S0378779623002730
2023
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