Author:
Chen Xi,Peng Jiao,He Yue,Zhang Bo,Jiang Dan,Lin Peng
Publisher
Springer Nature Singapore
Reference9 articles.
1. Hu, Y., Qu, B., Wang, J., et al.: Short-term load forecasting using multimodal evolutionary algorithm and random vector functional link network-based ensemble learning. Appl. Energy 285 (2021)
2. Shixiong, F., Lixin, L., Songyan, W., et al.: Application of artificial intelligence technology in power grid control. Power Syst. Technol. 44(2), 401–411 (2020)
3. Liu, Y., Li, Z., Bai, K., et al.: Short-term power-forecasting method of distributed PV power system for consideration of its effects on load forecasting. J. Eng. 13, 865–869 (2017)
4. Kaytez, F., Taplamacioglu, M.C., Cam, E., et al.: Forecasting electricity consumption: a comparison of regression analysis, neural networks and least squares support vector machines. Int. J. Electr. Power Energy Syst. 67(1), 431–438 (2015)
5. Hermias, J.P., Teknomo, K., Monje, J.C.: Short-term stochastic load forecasting using autoregressive integrated moving average models and hidden markov model. In: International Conference on Information and Communication Technologies. IEEE (2017)