BiGRU-CNN neural network applied to short-term electric load forecasting
Lucas Duarte Soares; Edgar Manuel Carreño Franco
Abstract
Keywords
References
Albawi, S., Mohammed, T. A., & Al-Zawi, S. (2017, Aug. 21-23). Understanding of a convolutional neural network. In
Alberg, D., & Last, M. (2018). Short-term load forecasting in smart meters with sliding window-based ARIMA algorithms.
Amin, M. A. A., & Hoque, M. A. (2019, March 13-15). Comparison of ARIMA and SVM for short-term load forecasting. In S. Chakrabarti, & A. Mukherjee (Eds.),
Ayifu, M., Wushouer, S., & Palidan, M. (2019). Multilingual named entity recognition based on the BiGRU-CNN-CRF hybrid model.
Boubaker, S., Benghanem, M., Mellit, A., Lefza, A., Kahouli, O., & Kolsi, L. (2021). Deep neural networks for predicting solar radiation at Hail Region, Saudi Arabia.
Bui, V., Nguyen, V. H., Pham, T. L., Kim, J., & Jang, Y. M. (2020, Feb. 19-21). RNN-based deep learning for one-hour ahead load forecasting. In
Carpinteiro, O. A. S., & Silva, A. P. A. (2000, Nov. 25). A hierarchical neural model in short-term load forecasting. In C. H. C. Ribeiro, & F. M. G. França (Eds.),
Cerne, G., Dovzan, D., & Skrjanc, I. (2018). Short-term load forecasting by separating daily profiles and using a single fuzzy model across the entire domain.
Chandramitasari, W., Kurniawan, B., & Fujimura, S. (2018, Aug. 29-30). Building deep neural network model for short term electricity consumption forecasting. In A. Pranolo, A. Prahara, A. Azhari, & A. Aktawan (Eds.),
Chapagain, K., Kittipiyakul, S., & Kulthanavit, P. (2020). Short-term electricity demand forecasting: impact analysis of temperature for Thailand.
Charytoniuk, W., & Chen, M. S. (2000). Very short-term load forecasting using artificial.
Chen, Y., Xu, P., Chu, Y., Li, W., Wu, Y., Ni, L., Bao, Y., & Wang, K. (2017). Short-term electrical load forecasting using the Support Vector Regression (SVR) model to calculate the demand response baseline for office buildings.
Coelho, V. N., Coelho, I. M., Coelho, B. N., Reis, A. J. R., Enayatifar, R., Souza, M. J. F., & Guimarães, F. G. (2016). A self-adaptive evolutionary fuzzy model for load forecasting problems on smart grid environment.
Deng, Y., Jia, H., Li, P., Tong, X., Qiu, X., & Li, F. (2019, June 19-21). A deep learning methodology based on bidirectional gated recurrent unit for wind power prediction. In
Dhaval, B., & Deshpande, A. (2020). Short-term load forecasting with using multiple linear regression.
Dudek, G. (2016). Pattern-based local linear regression models for short-term load forecasting.
Dudek, G. (2020). Multilayer perceptron for short-term load forecasting: from global to local approach.
Fallah, S. N., Ganjkhani, M., Shamshirband, S., & Chau, K. (2019). Computational intelligence on short-term load forecasting: a methodological overview.
Gao, X., Li, X., Zhao, B., Ji, W., Jing, X., & He, Y. (2019). Short-term electricity load forecasting model based on EMD-GRU with feature selection.
Ghalehkhondabi, I., Ardjmand, E., Weckman, G. R., & Young, W. A. (2017). An overview of energy demand forecasting methods published in 2005–2015.
Hadi, K. A., Lasri, R., & Abderrahmani, A. E. (2019). Social data analytics for forecasting electoral outcomes.
Hagan, M. T., Demuth, H. B., & Beale, M. H. (2014).
Hahn, H., Meyer-Nieberg, S., & Pickl, S. (2009). Electric load forecasting methods: tools for decision making.
Huang, C., & Yang, H. (1995, Nov. 21-23). A time series approach to short term load forecasting through evolutionary programming structures. In
Islam, M. A., Che, H. S., Hasanuzzaman, M., & Rahim, N. A. (2019). Energy demand forecasting. In M. Hasanuzzaman & N. A. Rahim (Eds.),
Jiang, H., Zhang, Y., Muljadi, E., Zhang, J. J., & Gao, D. W. (2018). A short-term and high-resolution distribution system load forecasting approach using support vector regression with hybrid parameters optimization.
Johannesen, N. J., Kolhe, M., & Goodwin, M. (2019). Relative evaluation of regression tools for urban area electrical energy demand forecasting.
Kandil, M. S., El-Debeiky, S. M., & Hasanien, N. E. (2002). Long-term load forecasting for fast developing utility using a knowledge-based expert system.
Kingma, D. P., & Ba, J. L. (2015, May. 7-9). Adam: a method for stochastic optimization. In Y. Bengio & Y. LeCun (Eds.),
Kuan, L., Yan, Z., Xin, W., Yan, C., Xiangkun, P., Wenxue, S., Zhe, J., Yong, Z., Nan, X., & Xin, Z. (2017, Nov. 26-28). Short-term electricity load forecasting method based on multilayered self-normalizing GRU network. In F. Gao (Ed.),
Li, P., Luo, A., Liu, J., Wang, Y., Zhu, J., Deng, Y., & Zhang, J. (2020). Bidirectional gated recurrent unit neural network for Chinese address element segmentation.
Li, Y., Che, J., & Yang, Y. (2018). Subsampled support vector regression ensemble for short term electric load forecasting.
Liu, J., Yang, Y., Lv, S., Wang, J., & Chen, H. (2019). Attention-based BiGRU-CNN for Chinese question classification.
Luo, X., Zhou, W., Wang, W., Zhu, Y., & Deng, J. (2018). Attention-based relation extraction with bidirectional gated recurrent unit and highway network in the analysis of geological data.
Lv, P., Liu, S., Yu, W., Zheng, S., & Lv, J. (2020). EGA-STLF: a hybrid short-term load forecasting model.
Markovié, M. L., & Fraissler, W. F. (1993). Short‐term load forecast by plausibility checking of announced demand: An expert‐system approach.
Massaoudi, M., Refaat, S. S., Abu-Rub, H., Chihi, I., & Oueslati, F. S. (2020a). PLS-CNN-BiLSTM: an end-to-end algorithm-based savitzky-golay smoothing and evolution strategy for load forecasting.
Massaoudi, M., Refaat, S. S., Chihi, I., Trabelsi, M., Abu-Rub, H., & Oueslati, F. S. (2020b). Short-term electric load forecasting based on data-driven deep learning techniques. In
Massaoudi, M., Refaat, S. S., Chihi, I., Trabelsi, M., Oueslati, F. S., & Abu-Rub, H. (2021). A novel stacked generalization ensemble-based hybrid LGBM-XGB-MLP model for short-term load forecasting.
Mayrink, V., & Hippert, H. S. (2016). A hybrid method using exponential smoothing and gradient boosting for electrical short-term load forecasting. In C. Rodríguez, & J. B. Gómez (Eds.),
Medsker, L. R., & Jain, L. C. (2000).
Mohammed, J., Bahadoorsingh, S., Ramsamooj, N., & Sharma, C. (2017, June 18-22). Performance of exponential smoothing, a neural network and a hybrid algorithm to the short term load forecasting of batch and continuous loads. In
Morettin, P. A., & Toloi, C. M. C (2006). Análise de séries temporais (2. ed.). São Paulo, Brazil Blucher.
Mukhopadhyay, P., Mitra, G., Banerjee, S., & Mukherjee, G. (2018, Dec. 21-23). Electricity load forecasting using fuzzy logic: Short term load forecasting factoring weather parameter. In
Niu, M., Sun, S., Wu, J., Yu, L., & Wang, J. (2016). An innovative integrated model using the singular spectrum analysis and nonlinear multi-layer perceptron network optimized by hybrid intelligent algorithm for short-term load forecasting.
Pan, X., & Lee, B. (2012). A comparison of support vector machines and artificial neural networks for mid-term load forecasting. In
Rahman, S., & Hazim, O. (1996). Load forecasting for multiple sites: development of an expert system-based technique.
Rendon-Sanchez, J. F., & Menezes, L. M. (2019). Structural combination of seasonal exponential smoothing forecasts applied to load forecasting.
Saber, A. Y., & Alam, A. K. M. R. (2018). Short term load forecasting using multiple linear regression for big data. In
Sajjad, M., Khan, Z. A., Ullah, A., Hussain, T., Ullah, W., Lee, M. Y., & Baik, S. W. (2020). A novel CNN-GRU-based hybrid approach for short-term residential load forecasting.
Setiawan, A., Koprinska, I., & Agelidis, V. G. (2009). Very short-term electricity load demand forecasting using support vector regression. In
Shahidehpour, M., Yamin, H., & Li, Z. (2002).
Sindhu, C., Som, B., & Singh, S. P. (2021a). Aspect based opinion mining leveraging weighted bigru and CNN module in parallel. In
Sindhu, C., Som, B., & Singh, S. P. (2021b). Aspect-oriented sentiment classification using BiGRU-CNN model. In
Singh, A. K., & Khatoon, S. (2013). An overview of electricity demand forecasting techniques.
Soliman, S. A., & Al-Kandari, A. M. (2010).
Talathi, S. S., & Vartak, A. (2015). Improving performance of recurrent neural network with relu nonlinearity.
Tian, C., Ma, J., Zhang, C., & Zhan, P. (2018). A deep neural network model for short-term load forecast based on long short-term memory network and convolutional neural network.
Tudose, A. M., Sidea, D. O., Picioroaga, I. I., Boicea, V. A., & Bulac, C. (2020). A CNN based model for short-term load forecasting: a real case study on the Romanian power system. In
Upadhaya, D., Thakur, R., & Singh, N. K. (2019). A systematic review on the methods of short term load forecasting. In
Wang, Y., Liao, W., & Chang, Y. (2018). Gated recurrent unit network-based short-term photovoltaic forecasting. Energies, 11(8), 2163.
Wu, F., Cattani, C., Song, W., & Zio, E. (2020a). Fractional ARIMA with an improved cuckoo search optimization for the efficient short-term power load forecasting.
Wu, K., Wu, J., Feng, L., Yang, B., Liang, R., Yang, S., & Zhao, R. (2021). An attention-based CNN-LSTM-BiLSTM model for short-term electric load forecasting in integrated energy system.
Wu, L., Kong, C., Hao, X., & Chen, W. (2020b). A short-term load forecasting method based on GRU-CNN hybrid neural network model.
Xiuyun, G., Ying, W., Yang, G., Chengzhi, S., Wen, X., & Yimiao, Y. (2018). Short-term load forecasting model of gru network based on deep learning framework. In
Xuan, Y., Si, W., Zhu, J., Sun, Z., Zhao, J., Xu, M., & Xu, S. (2021). Multi-model fusion short-term load forecasting based on random forest feature selection and hybrid neural network.
Yan, K., Li, W., Ji, Z., Qi, M., & Du, Y. (2019). A hybrid LSTM neural network for energy consumption forecasting of individual households.
Yang, H., Huang, C., & Huang, C. (1996). Identification of ARMAX model for short term load forecasting: an evolutionary programming approach.
Zhang, D., Tian, L., Hong, M., Han, F., Ren, Y., & Chen, Y. (2018). Combining convolution neural network and bidirectional gated recurrent unit for sentence semantic classification.
Submitted date:
07/12/2021
Accepted date:
10/27/2021