Author:
Andronikos Achilleas,Tzelepi Maria,Tefas Anastasios
Publisher
Springer Nature Switzerland
Reference19 articles.
1. Abeysingha, A., Sritharan, A.S., Valluvan, R., Ahilan, K., Jayasinghe, D.: Electricity load/demand forecasting in sri lanka using deep learning techniques. In: 2021 10th International Conference on Information and Automation for Sustainability (ICIAfS), pp. 293–298 (2021). https://doi.org/10.1109/ICIAfS52090.2021.9606057
2. Amarasinghe, K., Marino, D.L., Manic, M.: Deep neural networks for energy load forecasting. In: Proceedings of the IEEE 26th International Symposium on Industrial Electronics (ISIE), pp. 1483–1488 (2017)
3. Andriopoulos, N., et al.: Short term electric load forecasting based on data transformation and statistical machine learning. Appl. Sci. 11(1), 158 (2021)
4. Cheng, Y.Y., Chan, P.P., Qiu, Z.W.: Random forest based ensemble system for short term load forecasting. In: Proceedings of the 2012 International Conference on Machine Learning and Cybernetics, vol. 1, pp. 52–56 (2012)
5. Emmanouilidis, G., Tzelepi, M., Tefas, A.: Short-term electric load demand forecasting on greek energy market using deep learning: a comparative study. In: 2022 Panhellenic Conference on Electronics & Telecommunications (PACET), pp. 1–4 (2022). https://doi.org/10.1109/PACET56979.2022.9976351