Author:
Sun I.-Fu,Ting Lo Pang-Yun,Su Ko-Wei,Chuang Kun-Ta
Publisher
Springer Nature Singapore
Reference18 articles.
1. Huang, S., Guo, Y., Liu, D., Zha, S., Fang, W.: A two-stage transfer learning based deep learning approach for production progress prediction in IoT-enabled manufacturing. IEEE Internet Things J. 6(6), 10627–10638 (2019)
2. Wu, Z., Mu, Y., Deng, S., Li, Y.: Spatial–temporal short-term load forecasting framework via K-shape time series clustering method and graph convolutional networks. Energy Rep. 8, 8752–8766 (2022)
3. Alberg, D., Last, M.: Short-term load forecasting in smart meters with sliding window-based ARIMA algorithms. Vietnam J. Comput. Sci. 5(3–4), 241–249 (2018)
4. Devlin, M.A., Hayes, B.P.: Non-intrusive load monitoring and classification of activities of daily living using residential smart meter data. IEEE Trans. Consum. Electron. 65(3), 339–348 (2019)
5. Ye, L., Keogh, E.: Time series shapelets: a novel technique that allows accurate, interpretable and fast classification. Data Min. Knowl. Disc. 22(1–2), 149–182 (2010)