Fetching the paper…
Reading the bibliography…
Electrical load forecasting plays a crucial role in decision-making for power systems, including unit commitment and economic dispatch.
J. E. Matheson and R. L. Winkler, “Scoring rules for continuous probability distributions,” Management science , vol. 22, no. 10, pp. 1087–1096, 1976
1976
Earlier work this paper cites.
A. G. Journel, “Modeling uncertainty: some conceptual thoughts,” in Geostatistics for the Next Century: An International Forum in Honour of Michel David Contribution to Geostatistics, Montreal, 1993 . Springer, 1994, pp. 30–43
1994
Earlier work this paper cites.
P. G. Georgiou, P. Tsakalides, and C. Kyriakakis, “Alpha-stable modeling of noise and robust time-delay estimation in the presence of impulsive noise,” IEEE Trans. Multimedia , vol. 1, no. 3, pp. 291–301, 1999
1999
Earlier work this paper cites.
P. J. Huber, “Robust statistics,” in International encyclopedia of statistical science . Springer, 2011, pp. 1248–1251
2011
Earlier work this paper cites.
A. Vaghefi, M. A. Jafari, E. Bisse, Y. Lu, and J. Brouwer, “Modeling and forecasting of cooling and electricity load demand,” Applied Energy , vol. 136, pp. 186–196, 2014
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
Y. Gal and Z. Ghahramani, “Dropout as a bayesian approximation: Representing model uncertainty in deep learning,” in international conference on machine learning . PMLR, 2016, pp. 1050–1059
2016
Earlier work this paper cites.
T. Hong, P. Pinson, S. Fan, H. Zareipour, A. Troccoli, and R. J. Hyndman, “Probabilistic energy forecasting: Global energy forecasting competition 2014 and beyond,” pp. 896–913, 2016
2016
Earlier work this paper cites.
A. Almalaq and G. Edwards, “A review of deep learning methods applied on load forecasting,” in 2017 16th IEEE international conference on machine learning and applications (ICMLA) , 2017, pp. 511–516
2017
Earlier work this paper cites.
W. Kong, Z. Y. Dong, Y. Jia, D. J. Hill, Y. Xu, and Y. Zhang, “Short-term residential load forecasting based on lstm recurrent neural network,” IEEE Trans. Smart Grid , vol. 10, no. 1, pp. 841–851, 2017
2017
Earlier work this paper cites.
A. Kendall and Y. Gal, “What uncertainties do we need in bayesian deep learning for computer vision?” Advances in neural information processing systems , vol. 30, 2017
2017
Earlier work this paper cites.
H. Nosair and F. Bouffard, “Economic dispatch under uncertainty: The probabilistic envelopes approach,” IEEE Transactions on Power Systems , vol. 32, no. 3, pp. 1701–1710, 2017
2017
Earlier work this paper cites.
B. Lakshminarayanan, A. Pritzel, and C. Blundell, “Simple and scalable predictive uncertainty estimation using deep ensembles,” Advances in neural information processing systems , vol. 30, 2017
2017
Earlier work this paper cites.
D. M. Blei, A. Kucukelbir, and J. D. McAuliffe, “Variational inference: A review for statisticians,” Journal of the American statistical Association , vol. 112, no. 518, pp. 859–877, 2017
2017
Earlier work this paper cites.
K. B. Lindberg, S. J. Bakker, and I. Sartori, “Modelling electric and heat load profiles of non-residential buildings for use in long-term aggregate load forecasts,” Utilities Policy , vol. 58, pp. 63–88, 2019
2019
Earlier work this paper cites.
Y. Wang, D. Gan, M. Sun, N. Zhang, Z. Lu, and C. Kang, “Probabilistic individual load forecasting using pinball loss guided lstm,” Applied Energy , vol. 235, pp. 10–20, 2019
2019
Earlier work this paper cites.
Y. Yang, W. Hong, and S. Li, “Deep ensemble learning based probabilistic load forecasting in smart grids,” Energy , vol. 189, p. 116324, 2019
2019
Earlier work this paper cites.
L. Xu, S. Wang, and R. Tang, “Probabilistic load forecasting for buildings considering weather forecasting uncertainty and uncertain peak load,” Applied energy , vol. 237, pp. 180–195, 2019
2019
Cited alongside, same era.
M. Sun, T. Zhang, Y. Wang, G. Strbac, and C. Kang, “Using bayesian deep learning to capture uncertainty for residential net load forecasting,” IEEE Trans. Power Systems , vol. 35, no. 1, pp. 188–201, 2019
2019
Cited alongside, same era.
Y. Yang, W. Li, T. A. Gulliver, and S. Li, “Bayesian deep learning-based probabilistic load forecasting in smart grids,” IEEE Trans. Industrial Informatics , vol. 16, no. 7, pp. 4703–4713, 2019
2019
Cited alongside, same era.
X. Li, S. Chen, X. Hu, and J. Yang, “Understanding the disharmony between dropout and batch normalization by variance shift,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2019, pp. 2682–2690
2019
Cited alongside, same era.
G. Papamakarios, E. Nalisnick, D. J. Rezende, S. Mohamed, and B. Lakshminarayanan, “Normalizing flows for probabilistic modeling and inference,” The Journal of Machine Learning Research , vol. 22, no. 1, pp. 2617–2680, 2021
2021
Later among the works it cites.
2021
Later among the works it cites.
L. Li, J. Yan, Q. Wen, Y. Jin, and X. Yang, “Learning robust deep state space for unsupervised anomaly detection in contaminated time-series,” IEEE Trans. Knowledge and Data Engineering , 2022
2022
Later among the works it cites.
M. Farrokhabadi, J. Browell, Y. Wang, S. Makonin, W. Su, and H. Zareipour, “Day-ahead electricity demand forecasting competition: Post-covid paradigm,” IEEE Open Access Journal of Power and Energy , vol. 9, pp. 185–191, 2022
2022
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
B. Dai and D. Wipf, “Diagnosing and enhancing vae models,” arXiv preprint arXiv:1903.05789 , 2019
2019
Cited alongside, same era.
N. Tagasovska and D. Lopez-Paz, “Single-model uncertainties for deep learning,” Advances in Neural Information Processing Systems , vol. 32, 2019
2019
Cited alongside, same era.
2019
Cited alongside, same era.
D. Tran, M. Dusenberry, M. Van Der Wilk, and D. Hafner, “Bayesian layers: A module for neural network uncertainty,” Advances in neural information processing systems , vol. 32, 2019
2019
Cited alongside, same era.
M. A. Hammad, B. Jereb, B. Rosi, D. Dragan et al. , “Methods and models for electric load forecasting: a comprehensive review,” Logist. Sustain. Transp , vol. 11, no. 1, pp. 51–76, 2020
2020
Cited alongside, same era.
D. Salinas, V. Flunkert, J. Gasthaus, and T. Januschowski, “Deepar: Probabilistic forecasting with autoregressive recurrent networks,” International Journal of Forecasting , vol. 36, no. 3, pp. 1181–1191, 2020
2020
Cited alongside, same era.
J. Ho, A. Jain, and P. Abbeel, “Denoising diffusion probabilistic models,” Advances in Neural Information Processing Systems , vol. 33, pp. 6840–6851, 2020
2020
Cited alongside, same era.
C. Miller, A. Kathirgamanathan, B. Picchetti, P. Arjunan, J. Y. Park, Z. Nagy, P. Raftery, B. W. Hobson, Z. Shi, and F. Meggers, “The building data genome project 2, energy meter data from the ashrae great energy predictor iii competition,” Scientific data , vol. 7, no. 1, p. 368, 2020
2020
Cited alongside, same era.
C. Wang, D. Qin, Q. Wen, T. Zhou, L. Sun, and Y. Wang, “Adaptive probabilistic load forecasting for individual buildings,” iEnergy , vol. 1, no. 3, pp. 341–350, 2022
2022
Later among the works it cites.
J. Postels, F. Ferroni, H. Coskun, N. Navab, and F. Tombari, “Sampling-free epistemic uncertainty estimation using approximated variance propagation,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2019, pp. 2931–2940
2022
Later among the works it cites.
Z. Zhu, W. Chen, R. Xia, T. Zhou, P. Niu, B. Peng, W. Wang, H. Liu, Z. Ma, X. Gu, J. Wang, Q. Chen, L. Yang, Q. Wen, and L. Sun, “Energy forecasting with robust, flexible, and explainable machine learning algorithms,” AI Magazine , 2023
2023
Closest in time.
Y. Zhou, Z. Ding, Q. Wen, and Y. Wang, “Robust load forecasting towards adversarial attacks via bayesian learning,” IEEE Trans. Power Systems , vol. 38, no. 2, pp. 1445–1459, 2023
2023
Closest in time.
M. Grabner, Y. Wang, Q. Wen, B. Blažič, and V. Štruc, “A global modeling framework for load forecasting in distribution networks,” IEEE Trans. Smart Grid , 2023
2023
Closest in time.
2023
Closest in time.
K. Rawal and A. Ahmad, “Load uncertainty-aware economic dispatch,” in 2023 IEEE PES 15th Asia-Pacific Power and Energy Engineering Conference (APPEEC) . IEEE, 2023, pp. 1–6
2023
Closest in time.
P. Zhao, D. Cao, Y. Wang, Z. Chen, and W. Hu, “Gaussian process-aided transfer learning for probabilistic load forecasting against anomalous events,” IEEE transactions on power systems , vol. 38, no. 3, pp. 2962–2965, 2023
2023
Closest in time.
K. G. Olivares, C. Challu, G. Marcjasz, R. Weron, and A. Dubrawski, “Neural basis expansion analysis with exogenous variables: Forecasting electricity prices with nbeatsx,” International Journal of Forecasting , vol. 39, no. 2, pp. 884–900, 2023
2023
Closest in time.
A. Zeng, M. Chen, L. Zhang, and Q. Xu, “Are transformers effective for time series forecasting?” in Proceedings of the AAAI conference on artificial intelligence , vol. 37, no. 9, 2023, pp. 11 121–11 128
2023
Closest in time.
X. Liang, Z. Liu, J. Wang, X. Jin, and Z. Du, “Uncertainty quantification-based robust deep learning for building energy systems considering distribution shift problem,” Applied Energy , vol. 337, p. 120889, 2023
2023
Closest in time.
H. Cheng, Q. Wen, Y. Liu, and L. Sun, “RobustTSF: Towards theory and design of robust time series forecasting with anomalies,” in The Twelfth International Conference on Learning Representations , 2024. [Online]. Available: https://openreview.net/forum?id=ltZ9ianMth
2024
Closest in time.