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Transformer-based few-shot learning for modeling Electricity Consumption Profiles with minimal data across thousands of domains
Xia, W., Peng, G., Wang, C., Palensky, P., Pauwels, E. & Vergara, P. P., 2026, In: International Journal of Electrical Power and Energy Systems. 175, 18 p., 111575.Research output: Contribution to journal › Article › Scientific › peer-review
Open AccessFile -
A flow-based model for conditional and probabilistic electricity consumption profile generation
Xia, W., Wang, C., Palensky, P. & Vergara, P. P., 2025, In: Energy and AI. 21, 17 p., 100586.Research output: Contribution to journal › Article › Scientific › peer-review
Open AccessFile3 Downloads (Pure) -
Comparative Analysis of Zero-Shot Capability of Time-Series Foundation Models in Short-Term Load Prediction
Lin, N., Yun, D., Xia, W., Palensky, P. & Vergara, P. P., 2025, 2025 IEEE Kiel PowerTech, PowerTech 2025. IEEE, 6 p. (2025 IEEE Kiel PowerTech, PowerTech 2025).Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review
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RL-ADN: A high-performance Deep Reinforcement Learning environment for optimal Energy Storage Systems dispatch in active distribution networks
Hou, S., Gao, S., Xia, W., Salazar Duque, E. M., Palensky, P. & Vergara, P. P., 2025, In: Energy and AI. 19, 12 p., 100457.Research output: Contribution to journal › Article › Scientific › peer-review
Open AccessFile4 Link opens in a new tab Citations (SciVal)97 Downloads (Pure) -
Synthetic Data Generation for Wind Energy Forecasting: Comparison Between Statistical and Deep Learning Models
Klyagina, O., Xia, W., Andrade, J. R., Vergara, P. P. & Bessa, R. J., 2025, Proceedings of the 2025 IEEE International Conference on Systems, Man, and Cybernetics (SMC). IEEE, p. 6544-6549 6 p.Research output: Chapter in Book/Conference proceedings/Edited volume › Conference contribution › Scientific › peer-review