Mixed data-driven decision-making in demand response management: An empirical evidence from dynamic time-warping based nonparametric-matching DID
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DOI: 10.1016/j.omega.2020.102233
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- Li, Tong & Wang, Zhaohua & Zhao, Wenhui, 2022. "Comparison and application potential analysis of autoencoder-based electricity pattern mining algorithms for large-scale demand response," Technological Forecasting and Social Change, Elsevier, vol. 177(C).
- Wenhui Zhao & Tong Li & Danyang Xu & Zhaohua Wang, 2024. "A global forecasting method of heterogeneous household short-term load based on pre-trained autoencoder and deep-LSTM model," Annals of Operations Research, Springer, vol. 339(1), pages 227-259, August.
- D’Inverno, Giovanna & Vidoli, Francesco & De Witte, Kristof, 2023. "Sustainable budgeting and financial balance: Which lever will you pull?," European Journal of Operational Research, Elsevier, vol. 309(2), pages 857-871.
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Keywords
Demand response management; Data-driven algorithm; Mixed frequency data; Dynamic time warping; Nonparametric matching;All these keywords.
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