Construction of Electricity Load Forecasting Model Based on Electricity Data Analysis

[1] Ahmad, N., Ghadi, Y., Adnan, M., & Ali, M. (2022). Load forecasting techniques for power system: Research challenges and survey. IEEE Access, 10, 71054-71090. Search in Google Scholar

[2] He, W. (2017). Load forecasting via deep neural networks. Procedia Computer Science, 122, 308-314. Search in Google Scholar

[3] Kuster, C., Rezgui, Y., & Mourshed, M. (2017). Electrical load forecasting models: A critical systematic review. Sustainable cities and society, 35, 257-270. Search in Google Scholar

[4] Nti, I. K., Teimeh, M., Nyarko-Boateng, O., & Adekoya, A. F. (2020). Electricity load forecasting: a systematic review. Journal of Electrical Systems and Information Technology, 7, 1-19. Search in Google Scholar

[5] Fallah, S. N., Deo, R. C., Shojafar, M., Conti, M., & Shamshirband, S. (2018). Computational intelligence approaches for energy load forecasting in smart energy management grids: state of the art, future challenges, and research directions. Energies, 11(3), 596. Search in Google Scholar

[6] Peyghami, S., Davari, P., Fotuhi-Firuzabad, M., & Blaabjerg, F. (2019). Standard test systems for modern power system analysis: An overview. IEEE industrial electronics magazine, 13(4), 86-105. Search in Google Scholar

[7] Peyghami, S., Palensky, P., & Blaabjerg, F. (2020). An overview on the reliability of modern power electronic based power systems. IEEE Open Journal of Power Electronics, 1, 34-50. Search in Google Scholar

[8] Momoh, J. A. (2017). Electric power system applications of optimization. CRC press. Search in Google Scholar

[9] Hammad, M. A., Jereb, B., Rosi, B., & Dragan, D. (2020). Methods and models for electric load forecasting: a comprehensive review. Logist. Sustain. Transp, 11(1), 51-76. Search in Google Scholar

[10] Qian, K., Wang, X., & Yuan, Y. (2021). Research on regional short-term power load forecasting model and case analysis. Processes, 9(9), 1617. Search in Google Scholar

[11] Yang, W., Wang, J., & Wang, R. (2017). Research and application of a novel hybrid model based on data selection and artificial intelligence algorithm for short term load forecasting. Entropy, 19(2), 52. Search in Google Scholar

[12] Guan, W., Zhang, D., Yu, H., Peng, B., Wu, Y., Yu, T., & Wang, K. (2021). Customer load forecasting method based on the industry electricity consumption behavior portrait. Frontiers in Energy Research, 9, 742993. Search in Google Scholar

[13] Shen, Y., Ma, Y., Deng, S., Huang, C. J., & Kuo, P. H. (2021). An ensemble model based on deep learning and data preprocessing for short-term electrical load forecasting. Sustainability, 13(4), 1694. Search in Google Scholar

[14] Wang, R., Wang, J., & Xu, Y. (2019). A novel combined model based on hybrid optimization algorithm for electrical load forecasting. Applied Soft Computing, 82, 105548. Search in Google Scholar

[15] Ni, K., Wang, J., Tang, G., & Wei, D. (2019). Research and application of a novel hybrid model based on a deep neural network for electricity load forecasting: a case study in Australia. Energies, 12(13), 2467. Search in Google Scholar

[16] Javed, U., Ijaz, K., Jawad, M., Ansari, E. A., Shabbir, N., Kütt, L., & Husev, O. (2021). Exploratory data analysis based short-term electrical load forecasting: A comprehensive analysis. Energies, 14(17), 5510. Search in Google Scholar

[17] Dong, X., Deng, S., & Wang, D. (2022). A short-term power load forecasting method based on k-means and SVM. Journal of Ambient Intelligence and Humanized Computing, 13(11), 5253-5267. Search in Google Scholar

[18] Nie, Y., Jiang, P., & Zhang, H. (2020). A novel hybrid model based on combined preprocessing method and advanced optimization algorithm for power load forecasting. Applied Soft Computing, 97, 106809. Search in Google Scholar

[19] Lin, Y., Luo, H., Wang, D., Guo, H., & Zhu, K. (2017). An ensemble model based on machine learning methods and data preprocessing for short-term electric load forecasting. Energies, 10(8), 1186. Search in Google Scholar

[20] Liu, Z., Sun, X., Wang, S., Pan, M., Zhang, Y., & Ji, Z. (2019). Midterm power load forecasting model based on kernel principal component analysis and back propagation neural network with particle swarm optimization. Big data, 7(2), 130-138. Search in Google Scholar

[21] Deng, S., Chen, F., Wu, D., He, Y., Ge, H., & Ge, Y. (2022). Quantitative combination load forecasting model based on forecasting error optimization. Computers and Electrical Engineering, 101, 108125. Search in Google Scholar