Accuracy improvement of various short-term load forecasting models by a novel and unified statistical data-filtering method

References

• Alexander, E., V. Marcus, and A. Sahin. 2019. Short-term probabilistic load forecasting at low aggregation levels using convolutional neural networks. In the 2019 IEEE Milan PowerTech conference, Milan, Italy. doi:10.1109/PTC.2019.8810811.
   Google Scholar
• Al-Hamadi, H.-M., and S.-A. Soliman. 2006. Fuzzy short-term electric load forecasting using Kalman filter. IEE Proceedings - Generation, Transmission and Distribution 153 (2):217–27, 16 March. doi:10.1049/ip-gtd:20050088.
   Google Scholar
• Al-Qahtani, F.-H., and S.-F. Crone. 2013. Multivariate k-nearest neighbour regression for time series data - A novel algorithm for forecasting UK electricity demand. In the 2013 International Joint Conference on Neural Networks (IJCNN), 1-8, Dallas, TX, USA. doi:10.1109/IJCNN.2013.6706742.
   Google Scholar
• Amjady, N., and F. Keynia. 2009. Short-term load forecasting of power systems by combination of wavelet transform and neuro-evolutionary algorithm. Energy 34 (1):46–57. doi:10.1016/j.energy.2008.09.020.
   Web of Science ®Google Scholar
• Bengio, Y., P. Simard, and P. Frasconi. 1994. Learning long-term dependencies with gradient descent is difficult. IEEE Transactions on Neural Networks 5:157–66. doi:10.1109/72.279181.
   PubMed Web of Science ®Google Scholar
• Box, G.-E., and G.-M. Jenkins. 1976. Time Series Analysis: Forecasting and Control. The revised ed. Holden-Day, San Francisco, USA.
   Google Scholar
• Cao, X., S. Dong, Z. Wu, and Y. Jing. 2015. A data-driven hybrid optimization model for short-term residential load forecasting. In 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing (CIT/IUCC/DASC/PICOM), Liverpool, UK, 283–87. doi:10.1109/CIT/IUCC/DASC/PICOM.2015.41.
   Google Scholar
• Chen Y., Z. Lin, X. Zhao, G. Wang, and Y. Gu. 2014. Deep learning-based classification of hyperspectral data. Observ. RemoteSens. 7 (6), 2094-2107.
   Google Scholar
• Chontzopoulos, -I.-I. 2018. Very short-term probabilistic demand forecasting at high aggregation level for the mitigation of balancing costs. Master thesis, ETH Zürich.
   Google Scholar
• Connor, J. T. 1996. A robust neural network filter for electricity demand prediction. Journal of Forecasting 15:437–58. doi:doi:10.1002/(SICI)1099-131X(199611)15:6<437::aid-for634>3.0.CO;2-H
   Google Scholar
• Du, S., T. Li, X. Gong, Y. Yang, and S.-J. Horng. 2017. Traffic flow forecasting based on hybrid deep learning framework. In Proceedings of the 12th International Conference on Intelligent Systems and Knowledge Engineering, Nanjing, China, 1–6.
   Google Scholar
• Ester, M., H.-P. Kriegel, J. Sander, and X. Xu. 1996. A density-based algorithm for discovering clusters in large spatial databases with noise. Proceedings of the Second International Conference on Knowledge Discovery and Data Mining 226–31. https://dl.acm.org/doi/10.5555/3001460.3001507
   Google Scholar
• García, M.-L., S. Valero, C. Senabre, and A.-G. Marín. 2013. Short-term predictability of load series: Characterization of load data bases. IEEE Transactions on Power Systems 28 (3):2466–74. doi:10.1109/TPWRS.2013.2250317.
   Web of Science ®Google Scholar
• Gastaldi, M., R. Lamedica, A. Nardecchia, and A. Prudenzi. 2004. Short-term forecasting of municipal load through a Kalman filtering based approach. In The IEEE PES Power Systems Conference and Exposition, New York 2004, New York, NY, USA. doi:10.1109/PSCE.2004.1397538.
   Google Scholar
• Ghofrani, M., M. Hassanzadeh, M. Etezadi-Amoli, and M.-S. Fadali. 2011. Smart meter based short-term load forecasting for residential customers. In the 2011 North American Power Symposium, Boston, MA, 1–5, doi:10.1109/NAPS.2011.6025124.
   Google Scholar
• Guan, C., P.-B. Luh, L.-D. Michel, Y. Wang, and P.-B. Friedland. 2012. Very short-term load forecasting: Wavelet neural networks with data pre-filtering. IEEE Transactions on Power Systems 28 (1):30–41. doi:10.1109/TPWRS.2012.2197639.
   Web of Science ®Google Scholar
• Guan, C., P.-B. Luh, L.-D. Michel, and Z. Chi. 2013. Hybrid kalman filters for very short-term load forecasting and prediction interval estimation. IEEE Transactions on Power Systems 28 (4):3806–17, November. doi:10.1109/TPWRS.2013.2264488.
   Web of Science ®Google Scholar
• He, K., X. Zhang, S. Ren, and J. Sun. 2016. Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 770–78.
   Google Scholar
• Hinton, G.-E., and -R.-R. Salakhutdinov. 2006. Reducing the dimensionality of data with neural networks. Science 313 (1):504–07. doi:10.1126/science.1127647.
   PubMedGoogle Scholar
• Hippert, H.-S., C.-E. Pedreira, and R.-C. Souza. 2001. Neural networks for short-term load forecasting: A review and evaluation. IEEE Transactions on Power Systems 16 (1):44–55. doi:10.1109/59.910780.
   Web of Science ®Google Scholar
• Huang, C.-M., and Y.-C. Huang. 2002. Combined wavelet-based networks and game-theoretical decision approach for real-time power dispatch. IEEE Power Engineering Review 22 (7):633–39. doi:10.1109/MPER.2002.4312389.
   Google Scholar
• Khashei, M., and M.-A. Bijari. 2011. Novel hybridization of artificial neural networks and ARIMA models for time series forecasting. Applied Soft Computing 11 (2):2664–75. doi:10.1016/j.asoc.2010.10.015.
   Web of Science ®Google Scholar
• Kim, C.-I., I.-K. Yu, and Y.-H. Song. 2002. Kohonen neural networks and wavelet transform based approach to short-term load forecasting. Electric Power Systems Research 63 (3):169–76. doi:10.1016/S0378-7796(02)00097-4.
   Web of Science ®Google Scholar
• Kong, W., Z.-Y. Dong, Y. Jia, D.-J. Hill, Y. Xu, and Y. Zhang. 2017. Short-term residential load forecasting based on LSTM recurrent neural network. IEEE Transactions on Smart Grid 10 (1):841–51. doi:10.1109/TSG.2017.2753802.
   Web of Science ®Google Scholar
• Li, H., Y. Zhao, Z. Zhang, and X. Hu. 2015. Short-term load forecasting based on the grid method and the time series fuzzy load forecasting method. In International Conference on Renewable Power Generation (RPG 2015), 1–6, Beijing, China. doi:10.1049/cp.2015.0382.
   Google Scholar
• Melero, -J.-J., -J.-J. Guerrero, J. Beltran, and C. Pueyo. 2012. Efficient data filtering for wind energy assessment. IET Renewable Power Generation 6 (6):446–54. doi:10.1049/iet-rpg.2011.0288.
   Web of Science ®Google Scholar
• Mocanu, E., P.-H. Nguyen, M. Gibescu, and W.-L. Kling. 2016. Deep learning for estimating building energy consumption. Sustainable Energy, Grids and Networks 6:91–99. doi:10.1016/j.segan.2016.02.005.
   Web of Science ®Google Scholar
• Nakama, T. 2009. Theoretical analysis of batch and on-line training for gradient descent learning in neural networks. Neurocomputing 73 (1):151–59. doi:10.1016/j.neucom.2009.05.017.
   Web of Science ®Google Scholar
• Oonsivilai, A., and M.-E. El-Hawary. 1999. Wavelet neural network based short term load forecasting of electric power system commercial load. In Proceedings of IEEE Canadian Conf. Elect. Comput. Eng., Edmonton, Canada, 1223–28. doi:10.1109/CCECE.1999.804865.
   Google Scholar
• Oord, A. V.-D., et al. 2016. A generative model for raw audio. arXiv arXiv:1609.03499.
   Google Scholar
• Pandey, A.-S., D. Singh, and S.-K. Sinha. 2010. Intelligent hybrid wavelet models for short-term load forecasting. IEEE Transactions on Power Systems 25 (3):1266–73. doi:10.1109/TPWRS.2010.2042471.
   Web of Science ®Google Scholar
• Pramono, S.-H., M. Rohmatillah, E. Maulana, R.-N. Hasanah, and F. Hario. 2019. Deep learning-based short-term load forecasting for supporting demand response program in hybrid energy system. Energies 12:3359–75. doi:10.3390/en12173359.
   Web of Science ®Google Scholar
• Qiang, S., and Y. Pu. 2019. Short-term power load forecasting based on support vector machine and particle swarm optimization. Journal of Algorithms & Computational Technology 13:1–8. doi:10.1177/1748301818797061.
   Web of Science ®Google Scholar
• Qingle, P., and Z. Min. 2010. Very short-term load forecasting based on neural network and rough set. In Intelligent Computation Technology and Automation (ICICTA), 2010 International Conference on, Changsha, Chin, 1132–35. doi:10.1109/ICICTA.2010.38.
   Google Scholar
• Reis, A. J.-R., and -A. P.-A. da Silva. 2005. Feature extraction via multiresolution analysis for short-term load forecasting. IEEE Transactions on Power Systems 20 (1):189–98. doi:10.1109/TPWRS.2004.840380.
   Web of Science ®Google Scholar
• Rosca, R.-E. 2011. Stationary and non-stationary time series. The USV Annals of Economics and Public Administration 10:177–86.
   Google Scholar
• Ryu, S., J. Noh, and H. Kim. 2016. Deep neural network based demand side short term load forecasting. In the 2016 IEEE International Conference on Smart Grid Communications (SmartGridComm), Sydney, NSW, Australia, 308–13. doi:10.1109/SmartGridComm.2016.7778779.
   Google Scholar
• Sehgal, S., H. Singh, M. Agarwal, V. Bhasker, and Shantanu. 2014. Data analysis using principal component analysis. In the 2014 International Conference on Medical Imaging, m-Health and Emerging Communication Systems (MedCom), Greater Noida, India. doi:10.1109/MedCom.2014.7005973.
   Google Scholar
• Tai, N.-L., S. Jürgen, and H.-X. Wu. 2006. Techniques of applying wavelet transform into combined model for short-term load forecasting. Electric Power Systems Research 76 (6–7):525–33, 0378–7796. doi:10.1016/j.epsr.2005.07.003.
   Web of Science ®Google Scholar
• Tian, C., J. Ma, C. Zhang, and P.-A. Zhan. 2018. Deep neural network model for short-term load forecast based on long short-term memory network and convolutional neural network. Energies 11 (12):3493. doi:10.3390/en11123493.
   Web of Science ®Google Scholar
• Uyar, M., S. Yildirim, and M.-T. Gencoglu. 2008. An effective wavelet-based feature extraction method for classification of power quality disturbance signals. Electric Power Systems Research 78:1747–55. doi:10.1016/j.epsr.2008.03.002.
   Web of Science ®Google Scholar
• Wang, J., J.-Z. Wang, Y. Li, S. Zhu, and J. Zhao. 2014. Techniques of applying wavelet de-noising into a combined model for short-term load forecasting. International Journal of Electrical Power & Energy Systems 62:816–24. doi:10.1016/j.ijepes.2014.05.038.
   Web of Science ®Google Scholar
• Yuan, C., S. Liu, and Z. Fang. 2016. Comparison of China’s primary energy consumption forecasting by using ARIMA (the autoregressive integrated moving average) model and GM (1, 1) model. Energy 100:384–90. doi:10.1016/j.energy.2016.02.001.
   Web of Science ®Google Scholar
• Yun, Z., Z. Quan, S. Caixin, L. Shaolan, L. Yuming, and S. Yang. 2008. RBF neural network and ANFIS-based short-term load forecasting approach in real-time price environment. IEEE Transactions on Power Systems 23 (3):853–58. doi:10.1109/TPWRS.2008.922249.
   Web of Science ®Google Scholar
• Zeiler, M.-D., M. Ranzato, and R. Monga. 2013. On rectified linear units for speech processing. In the 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 3517–21.
   Google Scholar
• Zhang, B.-L., R. Coggins, M.-A. Jabri, and D. Dersch. 2001. Multiresolution fore- casting for futures trading using wavelet decompositions. IEEE Transactions on Neural Networks 12 (4):765–75. doi:10.1109/72.935090.
   PubMedGoogle Scholar
• Zhang, B.-L., and Z.-Y. Dong. 2001. An adaptive neural-wavelet model for short term load forecasting. Electric Power Systems Research 59 (2):121–29. doi:10.1016/S0378-7796(01)00138-9.
   Web of Science ®Google Scholar
• Zhang, R., Y. Xu, Z.-Y. Dong, and W. Kong. 2016. A composite k-Nearest neighbor model for day-ahead load forecasting with limited temperature forecasts. In the 2016 IEEE Power and Energy Society General Meeting (PESGM), Boston, MA, USA. doi:10.1109/PESGM.2016.7741097.
   Google Scholar
• Zhang, R., Z.-Y. Dong, Y. Xu, K. Meng, and K.-P. Wong. 2013. Short-term load forecasting of Australian national electricity market by an ensemble model of extreme learning machine. IET Generation, Transmission & Distribution 7 (4):391–97. doi:10.1049/iet-gtd.2012.0541.
   Web of Science ®Google Scholar
• Zhang, X.-B., J.-Z. Wang, and K.-Q. Zhang. 2017. Short-term electric load forecasting based on singular spectrum analysis and support vector machine optimized by Cuckoo search algorithm. Electric Power Systems Research 146:270–285, 0378–7796. doi:10.1016/j.epsr.2017.01.035.
   Web of Science ®Google Scholar
• Zhao, H., H. Liu, W. Hu, and X. Yan. 2018. Anomaly detection and fault analysis of wind turbine components based on deep learning network. Renewable Energy 127:825–34. doi:10.1016/j.renene.2018.05.024.
   Web of Science ®Google Scholar