Smart Short-Term Load Forecasting through Coordination of LSTM-Based Models and Feature Engineering Methods during the COVID-19 Pandemic

References

• R. Madurai Elavarasan et al., “COVID-19: Impact analysis and recommendations for power sector operation,” Appl. Energy, vol. 279, pp. 115739, 2020. DOI: 10.1016/j.apenergy.2020.115739.
   PubMed Web of Science ®Google Scholar
• K. Otoufat-Shamsi, S. Ghane, R. Ghaemmaghami, H. Hedayati, and A. Abdalisousan, “Effect of Corona virus on electricity consumption in Iran and the world,” J. Intell. Procedures Elect. Technol., vol. 12, no. 45, pp. 49–62, 2021.
   Google Scholar
• M. H. Asgharinejad Keisami, S. Azad, R. Mohammadi Chabanloo, M. Nazari-Heris and S. Asadi, “Machine learning and power system planning: Opportunities and challenges,” in Application of Machine Learning and Deep Learning Methods to Power System Problems. Power Systems, M. Nazari-Heris, S. Asadi, B. Mohammadi-Ivatloo, M. Abdar, H. Jebelli, and M. Sadat-Mohammadi, Eds. Cham: Springer. DOI: 10.1007/978-3-030-77696-1_3.
   Google Scholar
• A. Y. Alani and I. O. Osunmakinde, “Short-term multiple forecasting of electric energy loads for sustainable demand planning in smart grids for smart homes,” Sustainability, vol. 9, no. 11, pp. 1972, 2017. DOI: 10.3390/su9111972.
   Web of Science ®Google Scholar
• W. Xuewei, C. Ruofa, Y. Hongchao, and L. Caiyan, “Support vector machine short term load forecasting based on similar days,” Comput. Measur. Contrl., vol. 26, no. 01, pp. 47–50, 2018.
   Google Scholar
• K. Wu et al., “An attention‐based CNN‐LSTM‐BiLSTM model for short‐term electric load forecasting in integrated energy system,” Int. Trans. Electr. Energ. Syst., vol. 31, no. 1, pp. e12637, 2021. DOI: 10.1002/2050-7038.12637.
   Web of Science ®Google Scholar
• W. Zhang et al., “Short-term power load forecasting using integrated methods based on long short-term memory,” Sci. China Technol. Sci., vol. 63, no. 4, pp. 614–624, 2020. DOI: 10.1007/s11431-019-9547-4.
   Web of Science ®Google Scholar
• B. S. Kwon, R. J. Park, and K. B. Song, “Short-term load forecasting based on deep neural networks using LSTM layer,” J. Electr. Eng. Technol., vol. 15, no. 4, pp. 1501–1509, 2020. DOI: 10.1007/s42835-020-00424-7.
   Web of Science ®Google Scholar
• M. Alhussein, K. Aurangzeb, and S. I. Haider, “Hybrid CNN-LSTM model for short-term individual household load forecasting,” IEEE Access, vol. 8, pp. 180544–180557, 2020. DOI: 10.1109/ACCESS.2020.3028281.
   Web of Science ®Google Scholar
• B. Farsi, M. Amayri, N. Bouguila, and U. Eicker, “On short-term load forecasting using machine learning techniques and a novel parallel deep LSTM-CNN approach,” IEEE Access, vol. 9, pp. 31191–31212, 2021. DOI: 10.1109/ACCESS.2021.3060290.
   Google Scholar
• S. H. Rafi, S. R. Deeba, E. Hossain, and Nahid-Al-Masood, “A short-term load forecasting method using integrated CNN and LSTM network,” IEEE Access, vol. 9, pp. 32436–32448, 2021. DOI: 10.1109/ACCESS.2021.3060654.
   Google Scholar
• M. S. Hossain and H. Mahmood, “Short-term load forecasting using an LSTM neural network,” 2020 IEEE Power and Energy Conference at Illinois (PECI), Champaign, IL, USA, 2020, pp. 1–6. DOI: 10.1109/PECI48348.2020.9064654.
   Google Scholar
• A. Moradzadeh, S. Zakeri, M. Shoaran, B. Mohammadi-Ivatloo, and F. Mohammadi, “Short-term load forecasting of microgrid via hybrid support vector regression and long short-term memory algorithms,” Sustainability, vol. 12, no. 17, pp. 7076, 2020. DOI: 10.3390/su12177076.
   Web of Science ®Google Scholar
• C. Cai, Y. Tao, T. Zhu, and Z. Deng, “Short-term load forecasting based on deep learning bidirectional LSTM neural network,” Appl. Sci., vol. 11, no. 17, pp. 8129, 2021. DOI: 10.3390/app11178129.
   Google Scholar
• N. Mughees, S. A. Mohsin, A. Mughees, and A. Mughees, “Deep sequence to sequence Bi-LSTM neural networks for day-ahead peak load forecasting,” Exp. Syst. Appl., vol. 175, pp. 114844, 2021. DOI: 10.1016/j.eswa.2021.114844.
   Web of Science ®Google Scholar
• Y. Wang et al., “Probabilistic individual load forecasting using pinball loss guided LSTM,” Appl. Energy, vol. 235, pp. 10–20, 2019. DOI: 10.1016/j.apenergy.2018.10.078.
   Web of Science ®Google Scholar
• Y. Bengio, P. Simard, and P. Frasconi, “Learning long-term dependencies with gradient descent is difficult,” IEEE Trans. Neural Netw., vol. 5, no. 2, pp. 157–166, 1994. DOI: 10.1109/72.279181.
   PubMed Web of Science ®Google Scholar
• F. A. Gers, J. Schmidhuber, and F. Cummins, “Learning to forget: Continual prediction with LSTM,” Neural Comput., vol. 12, no. 10, pp. 2451–2471, 2000. DOI: 10.1162/089976600300015015.
   PubMed Web of Science ®Google Scholar
• A. Karpathy and L. Fei-Fei, 2015. “Deep visual-semantic alignments for generating image descriptions,” Presented at the Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3128–3137.
   Google Scholar
• J. Benesty, J. Chen, Y. Huang, and I. Cohen, Noise Reduction in Speech Processing, vol. 2. Springer Topics in Signal Processing, Berlin Heidelberg: Springer, 2009, pp. 115–120.
   Google Scholar
• S. M. Piryonesi and T. E. El-Diraby, “Data analytics in asset management: Cost-effective prediction of the pavement condition index,” J. Infrastruct. Syst., vol. 26, no. 1, pp. 04019036, 2020. DOI: 10.1061/(ASCE)IS.1943-555X.0000512.
   Web of Science ®Google Scholar
• S. M. Piryonesi and T. E. El-Diraby, “Role of data analytics in infrastructure asset management: Overcoming data size and quality problems,” J. Transp. Eng. B: Pavements, vol. 146, no. 2, pp. 04020022, 2020.
   Web of Science ®Google Scholar
• J. Friedman, T. Hastie, and R. Tibshirani, The Elements of Statistical Learning, vol. 1, no. 10. New York: Springer Series in Statistics, 2001.
   Google Scholar
• M. L. McHugh, “The chi-square test of independence,” Biochem. Med. (Zagreb), vol. 23, no. 2, pp. 143–149, 2013. DOI: 10.11613/bm.2013.018.
   PubMed Web of Science ®Google Scholar
• D. J. Olive, “Multiple linear regression,” in Linear Regression. Cham: Springer, 2017, pp. 17–83.
   Google Scholar
• H. Abdi and L. J. Williams, “Principal component analysis,” WIREs Comput. Stat., vol. 2, no. 4, pp. 433–459, 2010. DOI: 10.1002/wics.101.
   Google Scholar
• M. E. Wall, A. Rechtsteiner, and L. M. Rocha, “Singular value decomposition and principal component analysis,” in A practical Approach to Microarray Data Analysis. Boston, MA: Springer, 2003, pp. 91–109.
   Google Scholar
• A. Graves, A. Mohamed, and G. E. Hinton. “Speech recognition with deep recurrent neural networks,” Presented at the 2013 IEEE International Conference on Acoustics, Speech and Signal Processing, 2013, pp. 6645–6649.
   Google Scholar
• K. Zhang, Y. Jiang, D. Liu, and H. Song, “Spatio-Temporal Data Mining for Aviation Delay Prediction,” Presented at the 2020 IEEE 39th International Performance Computing and Communications Conference (IPCCC), Austin, TX, USA, 2020, pp. 1–7. DOI: 10.1109/IPCCC50635.2020.9391561.
   Google Scholar
• G. Ruan et al., “A cross-domain approach to analyzing the short-run impact of COVID-19 on the US electricity sector,” Joule, vol. 4, no. 11, pp. 2322–2337, 2020. DOI: 10.1016/j.joule.2020.08.017.
   PubMed Web of Science ®Google Scholar
• G. Ruan, J. Wu, H. Zhong, Q. Xia, and L. Xie, “Quantitative assessment of US bulk power systems and market operations during the COVID-19 pandemic,” Appl. Energy, vol. 286, pp. 116354, 2021. DOI: 10.1016/j.apenergy.2020.116354.
   PubMed Web of Science ®Google Scholar
• H. Zhong, Z. Tan, Y. He, L. Xie, and C. Kang, “Implications of COVID-19 for the electricity industry: A comprehensive review,” CSEE J. Power Energy Syst., vol. 6, no. 3, pp. 489–495, 2020.
   Web of Science ®Google Scholar
• P. P. Phyo and C. Jeenanunta, “Daily load forecasting based on a combination of classification and regression tree and deep belief network,” IEEE Access, vol. 9, pp. 152226–152242, 2021. DOI: 10.1109/ACCESS.2021.3127211.
   Web of Science ®Google Scholar
• P. P. Phyo, C. Jeenanunta, and K. Hashimoto, “Electricity load forecasting in Thailand using deep learning models,” Int. J. Elect. Electron. Eng. Telecommun., vol. 8, no. 4, pp. 221–225, 2019.
   Google Scholar
• R. Jiao, T. Zhang, Y. Jiang, and H. He, “Short-term non-residential load forecasting based on multiple sequences LSTM recurrent neural network,” IEEE Access, vol. 6, pp. 59438–59448, 2018. DOI: 10.1109/ACCESS.2018.2873712.
   Web of Science ®Google Scholar