Intelligence based Accurate Medium and Long Term Load Forecasting System

References

• Abdulnabi, A. H., G. Wang, J. Lu, and K. Jia. November 2015. Multi-task CNN model for attribute prediction. IEEE Transactions on Multimedia 17(11):1949–2089. doi: 10.1109/TMM.2015.2477680.
   Web of Science ®Google Scholar
• Al-Musaylh, M. S., R. C. Deo, J. F. Adamowski, and Y. Li. 2018. Short-term electricity demand forecasting with mars, svr and arima models using aggregated demand data in Queensland, Australia. Advanced Engineering Informatics 35 (January):1–16. https://linkinghub.elsevier.com/retrieve/pii/S1474034617301477.
   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(January):46–57. no. 1. 10.1016/j.energy.2008.09.020.
   Google Scholar
• Bair, E., T. Hastie, D. Paul, and R. Tibshirani. March 2006. Prediction by supervised principal components. Journal of the American Statistical Association 101(473):119–37. doi: 10.1198/016214505000000628.
   Web of Science ®Google Scholar
• Bengio, Y. 2009. Learning deep architectures for AI. Foundations and Trends® in Machine Learning 2 (1):1–127. http://www.nowpublishers.com/article/Details/MAL-006.
   Google Scholar
• Bengio, Y. 2013. Deep Learning of Representations: Looking Forward. 7978 LNAI. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 1–37.
   Google Scholar
• Bozchalui, M. C., S. A. Hashmi, H. Hassen, C. A. Canizares, and K. Bhattacharya. December, 2012. Optimal operation of residential energy hubs in smart grids. IEEE Transactions on Smart Grid 3 (4):1755–66. doi:10.1109/TSG.2012.2212032
   Web of Science ®Google Scholar
• Burnham, K. P., and D. R. Anderson, eds. 2004. Model selection and multimodel inference. New York: Springer New York.
   Google Scholar
• Cai, M., M. Pipattanasomporn, and S. Rahman. 2019. Day-ahead building-level load forecasts using deep learning vs. traditional time-series techniques. Applied Energy 236 (February):1078–88. doi:10.1016/j.apenergy.2018.12.042.
   Google Scholar
• Canizo, M., I. Triguero, A. Conde, and E. Onieva. 2019. Multi-head CNN–RNN for multi-time series anomaly detection: an industrial case study. Neurocomputing 363 (October):246–60. doi:10.1016/j.neucom.2019.07.034.
   Google Scholar
• Cao, C., F. Liu, H. Tan, D. Song, W. Shu, W. Li, Y. Zhou, X. Bo, and Z. Xie. 2018. Deep learning and its applications in biomedicine. Genomics, Proteomics & Bioinformatics 16 (1):17–32. doi:10.1016/j.gpb.2017.07.003.
   PubMed Web of Science ®Google Scholar
• Ceperic, E., V. Ceperic, and A. Baric. November, 2013. A strategy for short-term load forecasting by support vector regression machines. IEEE Transactions on Power Systems 28 (4):4356–64. doi:10.1109/TPWRS.2013.2269803
   Web of Science ®Google Scholar
• Chan, S. C., K. M. Tsui, H. C. Wu, Y. Hou, Y.-C. Wu, and F. Wu. September 2012. Load/price forecasting and managing demand response for smart grids: methodologies and challenges. IEEE Signal Processing Magazine 29(5):68–85. doi: 10.1109/MSP.2012.2186531.
   Web of Science ®Google Scholar
• Chen, H., D. Ni, J. Qin, S. Li, X. Yang, T. Wang, and P. A. Heng. 2015. Standard plane localization in fetal ultrasound via domain transferred deep neural networks. IEEE Journal of Biomedical and Health Informatics 19 (5):1627–36. doi:10.1109/JBHI.2015.2425041.
   PubMed Web of Science ®Google Scholar
• Chen, L.-C., G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille. 2018. DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Transactions on Pattern Analysis and Machine Intelligence no. 4. 40 (4):834–48. doi:10.1109/TPAMI.2017.2699184.
   PubMed Web of Science ®Google Scholar
• Cho, K., and Y. Kim. 2022. Improving streamflow prediction in the WRF-Hydro model with LSTM networks. Journal of Hydrology 605 (February):127297. https://linkinghub.elsevier.com/retrieve/pii/S0022169421013470.
   Google Scholar
• Ertugrul, Ö. F. 2016. Forecasting electricity load by a novel recurrent extreme learning machines approach. International Journal of Electrical Power & Energy Systems 78 (June):429–35. doi:10.1016/j.ijepes.2015.12.006.
   Google Scholar
• Fan, G.-F., -L.-L. Peng, and W.-C. Hong. 2018. Short term load forecasting based on phase space reconstruction algorithm and bi-square kernel regression model. Applied Energy. 224(August):13–33. no. 224. 10.1016/j.apenergy.2018.04.075.
   Google Scholar
• Faria, P., and Z. Vale. August 2011. Demand response in electrical energy supply: an optimal real time pricing approach. Energy 36(8):5374–84. doi: 10.1016/j.energy.2011.06.049.
   Web of Science ®Google Scholar
• Fukushima, K., and S. Miyake. 1982. Neocognitron: A self-organizing neural network model for a mechanism of visual pattern recognition. 267–85.
   Google Scholar
• Ghahramani, Z. 2004. Unsupervised Learning. 72–112.
   Google Scholar
• Ghofrani, M., M. Ghayekhloo, A. Arabali, and A. Ghayekhloo. 2015. A hybrid short-term load forecasting with a new input selection framework. Energy no. 81. 81 (March):777–86. doi:10.1016/j.energy.2015.01.028.
   Google Scholar
• Girshick, R., J. Donahue, T. Darrell, and J. Malik. 2014. Rich feature hierarchies for accurate object detection and semantic segmentation. In 2014 IEEE Conference on Computer Vision and Pattern Recognition, 580–87. IEEE.
   Google Scholar
• Glorot, X., A. Bordes, and Y. Bengio. 2011. Deep sparse rectifier neural networks. AISTATS ’11: Proceedings of the 14th International Conference on Artificial Intelligence and Statistics 15: 315–23.
   Google Scholar
• Goodfellow, I., Y. Bengio, and C.A. 2016. Deep Learning.
   Google Scholar
• Günther, J., P. M. Pilarski, G. Helfrich, H. Shen, and K. Diepold. 2014. First steps towards an intelligent laser welding architecture using deep neural networks and reinforcement learning. Procedia Technology 15:474–83. doi:10.1016/j.protcy.2014.09.007.
   Google Scholar
• Guo, Y., Y. Liu, A. Oerlemans, S. Lao, S. Wu, Y. G. L.m, Y. Liu, A. Oerlemans, S. Lao, S. Wu, et al. 2016. Deep learning for visual understanding: a review. Neurocomputing no. 187. 187 (April):27–48. doi:10.1016/j.neucom.2015.09.116.
   Google Scholar
• Gupta, A., M. S. Ayhan, and A. S. Maida. 2013. Natural image bases to represent neuroimaging data. Journal of Machine Learning Research: Workshop and Conference Proceedings 28 (3):977–84.
   Google Scholar
• Gupta, S., P. Arbeláez, R. Girshick, and J. Malik. 2015. Indoor scene understanding with RGB-D images: bottom-up segmentation, object detection and semantic segmentation. International Journal of Computer Vision 112 (2):133–49. doi:10.1007/s11263-014-0777-6.
   Web of Science ®Google Scholar
• Gupta, S., R. Girshick, P. Arbeláez, and J. Malik. 2014. Learning rich features from RGB-D images for object detection and segmentation. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 8695 LNCS (PART 7):345–60.
   Google Scholar
• Han, L., Y. Peng, Y. Li, B. Yong, Q. Zhou, and L. Shu. 2019. Enhanced deep networks for short-term and medium-term load forecasting. IEEE Access 7:4045–55. doi:10.1109/ACCESS.2018.2888978.
   Web of Science ®Google Scholar
• He, K., X. Zhang, S. Ren, and J. Sun 2016a. Deep residual learning for image recognition. In IEEE Conference on Computer Vision and Pattern Recognition. Vol. 64.
   Google Scholar
• He, K., X. Zhang, S. Ren, and J. Sun. 2016b. Deep residual learning for image recognition.
   Google Scholar
• He, Y., Q. Xu, J. Wan, and S. Yang. 2016. Short-term power load probability density forecasting based on quantile regression neural network and triangle kernel function. Energy. 114(November):498–512. no. 114. 10.1016/j.energy.2016.08.023.
   Google Scholar
• Hinton, G. 2009. Deep belief networks. Scholarpedia 4 (5):5947. http://www.scholarpedia.org/article/Deep_belief_networks.
   Google Scholar
• Hinton, G., L. Deng, D. Yu, G. E. Dahl, A. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. Sainath, et al. 2012. Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. IEEE Signal Processing Magazine. 29(6):82–97. doi:10.1109/MSP.2012.2205597.
   Web of Science ®Google Scholar
• Hinton, G. E., S. Osindero, and Y.-W. Teh. July 2006. A Fast Learning Algorithm for Deep Belief Nets. Neural Computation 18(7):1527–54. doi: 10.1162/neco.2006.18.7.1527.
   PubMed Web of Science ®Google Scholar
• Hochreiter, S. S., Schmidhuber, and J. J. 1997. Long short-term memory. Neural Computation 9 (8):1735–80. doi:10.1162/neco.1997.9.8.1735.
   PubMed Web of Science ®Google Scholar
• Huo, J., T. Shi, and J. Chang. 2016. Comparison of random forest and svm for electrical short-term load forecast with different data sources. In 2016 7th IEEE International Conference on Software Engineering and Service Science (ICSESS), Beijing, China, 1077–80. IEEE. http://ieeexplore.ieee.org/document/7883252/.
   Google Scholar
• Hussain, L., S. Saeed, A. Idris, I. A. I. A. Awan, S. A. S. A. Shah, A. Majid, B. Ahmed, and Q.-A. Q.-A. Chaudhary. 2019. Regression analysis for detecting epileptic seizure with different feature extracting strategies. Biomedical Engineering/Biomedizinische Technik 64 (6) no. 6December18: 619–42. 10.1515/bmt-2018-0012
   PubMed Web of Science ®Google Scholar
• Karpathy, A., and L. Fei-Fei. 2015. Deep visual-semantic alignments for generating image descriptions. In 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 3128–37. Boston, MA, USA: IEEE.
   Google Scholar
• Karpathy, A., G. Toderici, S. Shetty, T. Leung, R. Sukthankar, and L. Fei-Fei. 2014. Large-scale video classification with convolutional neural networks. Computer Vision and Pattern Recognition (CVPR), 2014 IEEE Conference On: 1725–32.
   Google Scholar
• khantach, E., A. M. Hamlich, and N. Eddine Belbounaguia. 2019. Short-term load forecasting using machine learning and periodicity decomposition. AIMS Energy 7 (3):382–94. http://www.aimspress.com/article/10.3934/energy.2019.3.382.
   Web of Science ®Google Scholar
• Khator, S. K., and L. C. Leung. 1997. “Power distribution planning: a review of models and issues. ” {IEEE} {Trans}. {Power} {Syst} no. 3. 12 (3):1151–59. doi:10.1109/59.630455.
   Web of Science ®Google Scholar
• Khotanzad, A., R. Afkhami-Rohani, and D. Maratukulam. 1998a. “ANNSTLF-{artificial} {neural} {network} {short}-{term} {load} {forecaster}- generation three. ” {IEEE} {Trans}. {Power} {Syst} 13(4):1413–22. no. 4. doi:10.1109/59.736285.
   Web of Science ®Google Scholar
• Khotanzad, A., R. Afkhami-Rohani, and D. Maratukulam. 1998b. ANNSTLF-Artificial Neural Network Short-Term Load Forecaster- generation three. IEEE Transactions on Power Systems 13 (4):1413–22. doi:10.1109/59.736285.
   Web of Science ®Google Scholar
• Kollia, I., and S. Kollias. 2018. A deep learning approach for load demand forecasting of power systems. In 2018 IEEE Symposium Series on Computational Intelligence (SSCI), 912–19. IEEE.
   Google Scholar
• Kong, W., Z. Y. Dong, Y. Jia, D. J. Hill, Y. Xu, and Y. Zhang. 2019. Short-term residential load forecasting based on lstm recurrent neural network. IEEE Transactions on Smart Grid no. 1. 10 (January):841–51. doi:10.1109/TSG.2017.2753802.
   Google Scholar
• Krizhevsky, A., I. Sutskever, and G. E. Hinton. 2012. Imagenet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst 25:1106–14.
   Google Scholar
• LeCun, Y., and Y. Bengio. 1995. Convolutional networks for images,speech,and time series. Handb. BrainTheoryNeuralNetw.
   Google Scholar
• LeCun, Y., Y. Bengio, and G. Hinton. 2015. Deep Learning. Nature 521 (7553): 436–44. May 28. 10.1038/nature14539.
   PubMed Web of Science ®Google Scholar
• LeCun, Y., P. Haffner, L. Bottou, and Y. Bengio. 1999. Object recognition with gradient-based learning. Springer Berlin Heidelberg 319–45.
   Google Scholar
• Levi, G., and T. Hassncer. 2015. Age and gender classification using convolutional neural networks. In 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 34–42. IEEE.
   Google Scholar
• Litjens, G., T. Kooi, B. E. Bejnordi, A. A. A. Setio, F. Ciompi, M. Ghafoorian, J. A. W. M. W. M. van der Laak, B. van Ginneken, and C. I. Sánchez. December, 2017. A survey on deep learning in medical image analysis. Medical Image Analysis 42 (1995):60–88. doi:10.1016/j.media.2017.07.005
   PubMedGoogle Scholar
• Mamlook, R., O. Badran, and E. Abdulhadi. April, 2009. A fuzzy inference model for short-term load forecasting. Energy Policy 37 (4):1239–48. doi:10.1016/j.enpol.2008.10.051
   Web of Science ®Google Scholar
• Mehmood Butt, F., L. Hussain, A. Mahmood, and K. Javed Lone. 2021. Artificial intelligence based accurately load forecasting system to forecast short and medium-term load demands. Mathematical Biosciences and Engineering 18 (1):400–25. http://www.aimspress.com/article/doi/10.3934/mbe.2021022.
   Web of Science ®Google Scholar
• Metaxiotis, K., A. Kagiannas, D. Askounis, and J. Psarras. June, 2003. Artificial intelligence in short term electric load forecasting: a state-of-the-art survey for the researcher. Energy Conversion and Management 44 (9):1525–34. doi:10.1016/S0196-8904(02)00148-6
   Web of Science ®Google Scholar
• Moslehi, K., and R. Kumar. 2010. A reliability perspective of the smart grid. IEEE Transactions on Smart Grid no. 1. 1 (June):57–64. doi:10.1109/TSG.2010.2046346.
   Google Scholar
• Murthy Balijepalli, V. S. K., V. Pradhan, S. A. Khaparde, and R. M. Shereef. 2011. Review of demand response under smart grid paradigm. In ISGT2011-India, 236–43. IEEE.
   Google Scholar
• Oord, A. V. D., S. Dieleman, H. Zen, K. Simonyan, O. Vinyals, A. Graves, N. Kalchbrenner, A. Senior, and K. Kavukcuoglu. 2016. Wavenet: A generative model for raw audio.
   Google Scholar
• Park, K., S. Yoon, and E. Hwang. 2019. Hybrid load forecasting for mixed-use complex based on the characteristic load decomposition by pilot signals. IEEE Access 7:12297–306. doi:10.1109/ACCESS.2019.2892475.
   Web of Science ®Google Scholar
• Qiu, X., L. Zhang, Y. Ren, P. Suganthan, and G. Amaratunga. 2014. Ensemble deep learning for regression and time series forecasting. In 2014 IEEE Symposium on Computational Intelligence in Ensemble Learning (CIEL), 1–6. IEEE.
   Google Scholar
• Ranaweera, D. K., N. F. Hubele, and G. G. Karady. May, 1996. Fuzzy Logic for Short Term Load Forecasting. International Journal of Electrical Power & Energy Systems 18 (4):215–22. doi:10.1016/0142-0615(95)00060-7
   Web of Science ®Google Scholar
• Rodrigues Moreno, S., R. Gomes da Silva, V. Cocco Mariani, and L. Dos Santos Coelho. 2020. Multi-step wind speed forecasting based on hybrid multi-stage decomposition model and long short-term memory neural network. Energy Conversion and Management 213 (June):112869. https://linkinghub.elsevier.com/retrieve/pii/S0196890420304076.
   Google Scholar
• Sermanet, P., D. Eigen, X. Zhang, M. Mathieu, and Y. L. Rob Fergus. 2013. In-Tegrated Recognition. Localization and Detection Using Convolutional Networks 4:16.
   Google Scholar
• Shelhamer, E., J. Long, and T. Darrell. 2017. Fully convolutional networks for semantic segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence 39 (4):640–51. doi:10.1109/TPAMI.2016.2572683.
   PubMed Web of Science ®Google Scholar
• Shin, H. C., H. R. Roth, M. Gao, L. Lu, Z. Xu, I. Nogues, J. Yao, D. Mollura, and R. M. Summers. 2016. Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Transactions on Medical Imaging 35 (5):1285–98. doi:10.1109/TMI.2016.2528162.
   PubMed Web of Science ®Google Scholar
• Shin, H.-C., L. Lu, and R.m.s 2015. Interleaved text/image deep mining on a large-scale radiology database. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 17: 1090–99.
   Google Scholar
• Shivarama Krishna, K., and K. Sathish Kumar. 2015. A review on hybrid renewable energy systems. Renewable and Sustainable Energy Reviews 52 (December):907–16. doi:10.1016/j.rser.2015.07.187.
   Google Scholar
• Singh, P., and P. Dwivedi. 2018. Integration of new evolutionary approach with artificial neural network for solving short term load forecast problem. Applied Energy 217 (May):537–49. doi:10.1016/j.apenergy.2018.02.131.
   Google Scholar
• Stefenon, S. F., M. H. Dal Molin Ribeiro, A. Nied, V. C. Mariani, L. Dos S. Coelho, D. F. Menegat da Rocha, R. B. Grebogi, and A. E. de B. Ruano. 2020. Wavelet group method of data handling for fault prediction in electrical power insulators. International Journal of Electrical Power & Energy Systems 123 (December):106269. https://linkinghub.elsevier.com/retrieve/pii/S0142061520310711.
   Google Scholar
• Sundermeyer, M., H. Ney, and R. Schluter. 2015. From feedforward to recurrent LSTM neural networks for language modeling. IEEE/ACM Transactions on Audio, Speech, and Language Processing no. 3. 23 (March):517–29. doi:10.1109/TASLP.2015.2400218.
   Google Scholar
• Szegedy, C., W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich 2015. Going deeper with convolutions. C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich. eds. Going Deeper with Convolutions. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston
   Google Scholar
• Taigman, Y., M. Yang, M. Ranzato, and L. Wolf. 2014. DeepFace: closing the gap to human-level performance in face verification. In Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1701–08.
   Google Scholar
• Taylor, J. W. August 2003. Short-term electricity demand forecasting using double seasonal exponential smoothing. Journal of the Operational Research Society 54(8):799–805. doi: 10.1057/palgrave.jors.2601589.
   Web of Science ®Google Scholar
• Tian, C., J. Ma, C. Zhang, and P. Zhan. 2018. A deep neural network model for short-term load forecast based on long short-term memory network and convolutional neural network. Energies 11 (12): 3493. December 14. 10.3390/en11123493.
   Web of Science ®Google Scholar
• Wen, T. H., M. Gasic, N. Mrksic, P. H. Su, D. Vandyke, and S. Young. 2015. Semantically conditioned LSTM-based natural language generation for spoken dialogue systems. Computer Science > Computation and Language.
   Google Scholar
• Xie, L., J. Wang, Z. Wei, M. Wang, and Q. Tian. 2016. DisturbLabel: regularizing CNN on the Loss Layer. In 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 4753–62. IEEE.
   Google Scholar
• Xu, Y., C. Hu, Q. Wu, S. Jian, Z. Li, Y. Chen, G. Zhang, Z. Zhang, and S. Wang. 2022. Research on particle swarm optimization in lstm neural networks for rainfall-runoff simulation. Journal of Hydrology 608 (May):127553. https://linkinghub.elsevier.com/retrieve/pii/S0022169422001287.
   Google Scholar
• Yang, J., M. N. Nguyen, P. P. San, X. L. Li, and S. Krishnaswamy 2015a. Deep Convolutional Neural Networks on Multi channel Time Series for Human Activity Recognition. In In Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence.
   Google Scholar
• Yang, L., and H. Yang. 2019. Analysis of different neural networks and a new architecture for short-term load forecasting. Energies 12 (8): 1433. April 14. 10.3390/en12081433.
   Web of Science ®Google Scholar
• Yang, S., J. Nguyen, M. N. San, P. P. Li, and X. L. Krishnaswamy. 2015b. “Deep convolutional neural networks on {multichannelTimeSeriesforHumanActivityRecognition}. ” in {InProceedingsoftheTwenty}-FourthInternational {JointConferenceonArtificialIntelligence}.
   Google Scholar
• Young, T., D. Hazarika, S. Poria, and E. Cambria. 2018. Recent trends in deep learning based natural language processing [review article]. IEEE Computational Intelligence Magazine. 13(August):55–75. no. 3. 10.1109/MCI.2018.2840738.
   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 (April):384–90. doi:10.1016/j.energy.2016.02.001.
   Google Scholar
• Zeng, Z., H. Xiao, and X. Zhang. October 2016. Self CNN-based time series stream forecasting. Electronics Letters 52(22):1857–58. doi: 10.1049/el.2016.2626.
   Web of Science ®Google Scholar
• ZHANG, Z., L. GUO, Y. Dai, X. DONG, and P. WANG. 2018. A short-term user load forecasting with missing data. DEStech Transactions on Engineering and Technology Researchno icmeit (June).
   Google Scholar
• Zheng, H., J. Yuan, and L. Chen. August 2017. Short-term load forecasting using EMD-LSTM neural networks with a xgboost algorithm for feature importance evaluation. Energies 10(8):1168. doi: 10.3390/en10081168.
   Web of Science ®Google Scholar
• Zheng, Y., Q. Liu, E. Chen, Y. Ge, and J. Zhao. 2014. Time series classification using multi-channels deep convolutional neural networks. In International Conference on Web-Age Information Management; Springer: Cham, Switzerland.
   Google Scholar