EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 394 (2023) E3S Web Conf., 394 (2023) 01002 References
Open Access
Issue
E3S Web Conf.
Volume 394, 2023
6th International Symposium on Resource Exploration and Environmental Science (REES 2023)
Article Number 01002
Number of page(s) 9
DOI https://doi.org/10.1051/e3sconf/202339401002
Published online 02 June 2023
  1. Almeshaiei E, Soltan H. A methodology for electric power load forecasting[J]. Alexandria Engineering Journal, 2011, 50(2): 137-144. [CrossRef] [Google Scholar]
  2. Jahan I S, Snasel V, Misak S. Intelligent systems for power load forecasting: A study review[J]. Energies, 2020, 13(22):6105. [CrossRef] [Google Scholar]
  3. Jin M, Zhou X, Zhang Z M, et al. Short-term power load forecasting using grey correlation contest modeling[J]. Expert Systems with Applications, 2012, 39(1): 773-779. [CrossRef] [Google Scholar]
  4. Nepal B, Yamaha M, Yokoe A, et al. Electricity load forecasting using clustering and ARIMA model for energy management in buildings[J]. Japan Architectural Review, 2020, 3(1): 62-76. [CrossRef] [Google Scholar]
  5. Singh A K, Khatoon S, Muazzam M, et al. Load forecasting techniques and methodologies: A review[C]//2012 2nd International Conference on Power, Control and Embedded Systems. IEEE, 2012: [Google Scholar]
  6. Yang A, Li W, Yang X. Short-term electricity load forecasting based on feature selection and Least Squares Support Vector Machines[J]. KnowledgeBased Systems, 2019, 163: 159-173. [Google Scholar]
  7. Bakirtzis A G, Petridis V, Kiartzis S J, et al. A neural network short term load forecasting model for the Greek power system[J]. IEEE Transactions on power systems, 1996, 11(2): 858-863. [CrossRef] [Google Scholar]
  8. Imani M. Electrical load-temperature CNN for residential load forecasting[J]. Energy, 2021, 227:120480. [CrossRef] [Google Scholar]
  9. Muzaffar S, Afshari A. Short-term load forecasts using LSTM networks[J]. Energy Procedia, 2019, 158: 2922-2927. [CrossRef] [Google Scholar]
  10. Zhu J, Yang Z, Guo Y, et al. Short-term load forecasting for electric vehicle charging stations based on deep learning approaches[J]. Applied sciences, 2019, 9(9):1723. [CrossRef] [Google Scholar]
  11. Tang X, Dai Y, Wang T, et al. Short‐term power load forecasting based on multi‐layer bidirectional recurrent neural network[J]. IET Generation, Transmission & Distribution, 2019, 13(17):38473854. [Google Scholar]
  12. Guo X, Zhao Q, Zheng D, et al. A short-term load forecasting model of multi-scale CNN-LSTM hybrid neural network considering the real-time electricity price[J]. Energy Reports, 2020, 6: 1046-1053. [CrossRef] [Google Scholar]
  13. Wu L, Kong C, Hao X, et al. A short-term load forecasting method based on GRU-CNN hybrid neural network model[J]. Mathematical Problems in Engineering, 2020, 2020. [Google Scholar]
  14. Xie K, Yi H, Hu G, et al. Short-term power load forecasting based on Elman neural network with particle swarm optimization[J]. Neurocomputing, 2020, 416: 136-142. [CrossRef] [Google Scholar]
  15. Nie Y, Jiang P, Zhang H. A novel hybrid model based on combined preprocessing method and advanced optimization algorithm for power load forecasting[J]. Applied Soft Computing, 2020, 97:106809. [CrossRef] [Google Scholar]
  16. Dai Y, Zhao P. A hybrid load forecasting model based on support vector machine with intelligent methods for feature selection and parameter optimization[J]. Applied energy, 2020, 279:115332. [CrossRef] [Google Scholar]
  17. Lv L, Wu Z, Zhang J, et al. A VMD and LSTM based hybrid model of load forecasting for power grid security[J]. IEEE Transactions on Industrial Informatics, 2021, 18(9): 6474-6482. [Google Scholar]
  18. Wang Y, Chen J, Chen X, et al. Short-term load forecasting for industrial customers based on TCNLightGBM[J]. IEEE Transactions on Power Systems, 2020, 36(3): 1984-1997. [Google Scholar]
  19. Chen Z, Jin T, Zheng X, et al. An innovative methodbased CEEMDAN–IGWO–GRU hybrid algorithm for short-term load forecasting[J]. Electrical Engineering, 2022, 104(5): 3137-3156. [CrossRef] [Google Scholar]
  20. Huang S, Zhang J, He Y, et al. Short-Term Load Forecasting Based on the CEEMDAN-Sample Entropy-BPNN-Transformer[J]. Energies, 2022, 15(10):3659. [CrossRef] [Google Scholar]
  21. Chen X, Chen W, Dinavahi V, et al. Short-Term Load Forecasting and Associated Weather Variables Prediction Using ResNet-LSTM Based Deep Learning[J]. IEEE Access, 2023. [PubMed] [Google Scholar]
  22. Liu M, Qin H, Cao R, et al. Short-Term Load Forecasting Based on Improved TCN and DenseNet[J]. IEEE Access, 2022, 10:115945115957. [Google Scholar]
  23. Su J, Han X, Hong Y. Short Term Power Load Forecasting Based on PSVMD-CGA Model[J]. Sustainability, 2023, 15(4):2941. [CrossRef] [Google Scholar]
  24. Cai C, Li Y, Su Z, et al. Short-Term Electrical Load Forecasting Based on VMD and GRU-TCN Hybrid Network[J]. Applied Sciences, 2022, 12(13):6647. [CrossRef] [Google Scholar]
  25. Yi S, Liu H, Chen T, et al. A deep LSTM‐CNN based on self‐attention mechanism with input data reduction for short‐term load forecasting[J]. IET Generation, Transmission & Distribution, 2023. [Google Scholar]
  26. Ahmed M S, Cook A R. Analysis of freeway traffic time-series data by using Box-Jenkins techniques[M]. 1979. [Google Scholar]
  27. MacQueen J. Classification and analysis of multivariate observations[C]//5th Berkeley Symp. Math. Statist. Probability. 1967: 281-297. [Google Scholar]
  28. LeCun Y, Boser B, Denker J S, et al. Backpropagation applied to handwritten zip code recognition[J]. Neural computation, 1989, 1(4):541551. [CrossRef] [Google Scholar]
  29. Rafi S H, Deeba S R, Hossain E. A short-term load forecasting method using integrated CNN and LSTM network[J]. IEEE Access, 2021, 9: 32436-32448. [CrossRef] [Google Scholar]
  30. Hochreiter S, Schmidhuber J. Long short-term memory[J]. Neural computation, 1997, 9(8):17351780. [Google Scholar]
  31. Elman J L. Finding structure in time[J]. Cognitive science, 1990, 14(2): 179-211. [CrossRef] [Google Scholar]
  32. He K, Zhang X, Ren S, et al. Deep residual learning for image recognition[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 770-778. [Google Scholar]
  33. Ioffe S, Szegedy C. Batch normalization: Accelerating deep network training by reducing internal covariate shift[C]//International conference on machine learning. pmlr, 2015: 448-456. [Google Scholar]
  34. Jin Y, Guo H, Wang J, et al. A hybrid system based on LSTM for short-term power load forecasting[J]. Energies, 2020, 13(23):6241. [CrossRef] [Google Scholar]
  35. Alhussein M, Aurangzeb K, Haider S I. Hybrid CNNLSTM model for short-term individual household load forecasting[J]. Ieee Access, 2020, 8:180544180557. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.