Load Forecasting Based on Data Mining and Improved Stacking Ensemble Learning Under Load Aggregator

Author:

Zhang Zhishuo,Du Xinhui,Zhang Wenxuan,Chang Kun,Zhang Rixin

Publisher

Springer Nature Singapore

Reference14 articles.

1. Buddhadeva, S., Sangram, K.R., Pravat, K.R.: Application of mathematical morphology for power quality improvement in microgrid. Int. Trans. Electr. Energy Syst. 30(5), 12329 (2020)

2. Yang, J., Li, Q., Zhang, Y., et al.: Peak shaving optimization modeling for demand response of multiple EV aggregators considering matching degree of power grid demand. Electr. Power Autom. Equip. 41(8), 125–134 (2021)

3. Master-slave game based optimal pricing strategy for LA in day-ahead electricity market. Autom. Electric Power Syst. 45(1), 159−167 (2021). (in Chinese)

4. Li, G., Liu, Z., Jin, G., et al.: Ultra short-term power load forecasting based on randomly distributive embedded framework and BP neural network. Power Syst. Technol. 44(2), 437–445 (2020). (in Chinese)

5. Li, Y., Jia, Y., Li, L.: Short term power load forecasting based on a stochastic forest algorithm. Power Syst. Protect. Control 48(21), 117–124 (2020). (in Chinese)

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