Research Article

BiGRU-CNN neural network applied to short-term electric load forecasting

Lucas Duarte Soares; Edgar Manuel Carreño Franco

Downloads: 2
Views: 223


Paper aims: This study analyzed the feasibility of the BiGRU-CNN artificial neural network as a forecasting tool for short-term electric load. This forecasting model can serve as a support tool related to decision-making by companies in the energy sector.

Originality: Despite a large amount of scientific research in this area, the literature still searches for more assertive forecasting models regarding short-term electric load. Thus, the BiGRU-CNN model, based on layers of BiGRU and CNN architecture networks was tested. This model was already proposed and used for other similar tasks, however, it has not been used on load forecasting.

Research method: The code was programmed in Python using the keras package. The forecasts of all networks were carried out 10 times until an acceptable statistical sample was reached so that future electric load values are as close as possible to reality.

Main findings: The best forecasting model was the proposed BiGRU-CNN network when compared to classical and some hybrid networks.

Implications for theory and practice: This methodology can be applied to short-term electric load forecasting problems. There is evidence that the combination of different layers of neural networks can provide more efficient forecasting results than classical networks with only one architecture.


Time series forecasting, Recurrent neural networks, Artificial intelligence, Machine learning


Albawi, S., Mohammed, T. A., & Al-Zawi, S. (2017, Aug. 21-23). Understanding of a convolutional neural network. In International Conference on Engineering and Technology - ICET (pp. 1–6). Antalya, Turkey: IEEE.

Alberg, D., & Last, M. (2018). Short-term load forecasting in smart meters with sliding window-based ARIMA algorithms. Vietnam Journal of Computer Science, 5(3–4), 241-249.

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.), 9th Annual Information Technology, Electromechanical Engineering and Microelectronics Conference - IEMECON (pp. 205–210). Jaipur, India: IEEE.

Ayifu, M., Wushouer, S., & Palidan, M. (2019). Multilingual named entity recognition based on the BiGRU-CNN-CRF hybrid model. International Journal of Information and Communication Technology, 15(3), 223-242.

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. IEEE Access: Practical Innovations, Open Solutions, 9, 36719-36729.

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 International Conference on Artificial Intelligence in Information and Communication, ICAIIC 2020 (pp. 587-589). Fukuoka, Japan: IEEE

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.), Proceedings of Sixth Brazilian Symposium on Neural Networks: Vol. 1 (pp. 120-124). Rio de Janeiro, Brazil: IEEE.

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. IEEE Transactions on Industrial Electronics, 65(9), 7406-7415.

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.), International Symposium on Advanced Intelligent Informatics: Revolutionize Intelligent Informatics Spectrum for Humanity - SAIN (pp. 43-48). Yogyakarta, Indonesia: IEEE.

Chapagain, K., Kittipiyakul, S., & Kulthanavit, P. (2020). Short-term electricity demand forecasting: impact analysis of temperature for Thailand. Energies, 13(10), 1-29.

Charytoniuk, W., & Chen, M. S. (2000). Very short-term load forecasting using artificial. IEEE Transactions on Power Systems, 15(1), 263-268.

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. Applied Energy, 195, 659-670.

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. Applied Energy, 169, 567-584.

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 Proceedings of the 14th IEEE Conference on Industrial Electronics and Applications - ICIEA 2019 (pp. 591–595). Xi'an, China: IEEE.

Dhaval, B., & Deshpande, A. (2020). Short-term load forecasting with using multiple linear regression. Iranian Journal of Electrical and Computer Engineering, 10(4), 3911-3917.

Dudek, G. (2016). Pattern-based local linear regression models for short-term load forecasting. Electric Power Systems Research, 130, 139-147.

Dudek, G. (2020). Multilayer perceptron for short-term load forecasting: from global to local approach. Neural Computing & Applications, 32(8), 3695-3707.

Fallah, S. N., Ganjkhani, M., Shamshirband, S., & Chau, K. (2019). Computational intelligence on short-term load forecasting: a methodological overview. Energies, 12(3), 393.

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. Energies, 12(6), 1-18.

Ghalehkhondabi, I., Ardjmand, E., Weckman, G. R., & Young, W. A. (2017). An overview of energy demand forecasting methods published in 2005–2015. Energy Systems, 8, 411-447.

Hadi, K. A., Lasri, R., & Abderrahmani, A. E. (2019). Social data analytics for forecasting electoral outcomes. International Journal of Innovative Technology and Exploring Engineering, 8(8), 2468-2471.

Hagan, M. T., Demuth, H. B., & Beale, M. H. (2014). Neural network design (2nd ed.). Oklahoma: OSU.

Hahn, H., Meyer-Nieberg, S., & Pickl, S. (2009). Electric load forecasting methods: tools for decision making. European Journal of Operational Research, 199(3), 902-907.

Huang, C., & Yang, H. (1995, Nov. 21-23). A time series approach to short term load forecasting through evolutionary programming structures. In Proceedings of the International Conference on Energy Management and Power Delivery - EMPD (Vol. 2, pp. 583–588). Singapore: IEEE.

Islam, M. A., Che, H. S., Hasanuzzaman, M., & Rahim, N. A. (2019). Energy demand forecasting. In M. Hasanuzzaman & N. A. Rahim (Eds.), Energy for sustainable development: demand, supply, conversion and management. London: Academic Press/Elsevier.

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. IEEE Transactions on Smart Grid, 9(4), 3331-3350.

Johannesen, N. J., Kolhe, M., & Goodwin, M. (2019). Relative evaluation of regression tools for urban area electrical energy demand forecasting. Journal of Cleaner Production, 218, 555-564.

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. IEEE Transactions on Power Systems, 17(2), 491-496.

Kingma, D. P., & Ba, J. L. (2015, May. 7-9). Adam: a method for stochastic optimization. In Y. Bengio & Y. LeCun (Eds.), 3rd International Conference on Learning Representations - ICLR 2015 - Conference Track Proceedings (pp. 1–15). San Diego:

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.), IEEE Conference on Energy Internet and Energy System Integration - EI2 (pp. 1–5). Beijing, China: IEEE.

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. ISPRS International Journal of Geo-Information, 9(11), 635.

Li, Y., Che, J., & Yang, Y. (2018). Subsampled support vector regression ensemble for short term electric load forecasting. Energy, 164, 160-170.

Liu, J., Yang, Y., Lv, S., Wang, J., & Chen, H. (2019). Attention-based BiGRU-CNN for Chinese question classification. Journal of Ambient Intelligence and Humanized Computing, 10(13), 1-12.

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. IEEE Access: Practical Innovations, Open Solutions, 6, 5705-5715.

Lv, P., Liu, S., Yu, W., Zheng, S., & Lv, J. (2020). EGA-STLF: a hybrid short-term load forecasting model. IEEE Access: Practical Innovations, Open Solutions, 8, 31742-31752.

Markovié, M. L., & Fraissler, W. F. (1993). Short‐term load forecast by plausibility checking of announced demand: An expert‐system approach. European Transactions on Electrical Power, 3(5), 353-358.

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. Energies, 13(20), 1-29.

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 IECON - The 46th Annual Conference of the IEEE Industrial Electronics Society (pp. 2565-2570). Singapore: IEEE.

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. Energy, 214, 118874.

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.), IEEE Latin American Conference on Computational Intelligence - LA-CCI. Cartagena, Colombia: IEEE.

Medsker, L. R., & Jain, L. C. (2000). Recurrent neural networks: design and applications. Boca Raton: CRC Press.

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 IEEE Manchester PowerTech. Manchester, UK: IEEE.

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 7th International Conference on Power Systems - ICPS (pp. 812–819). Pune, India: IEEE.

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. Applied Mathematical Modelling, 40(5-6), 4079-4093.

Pan, X., & Lee, B. (2012). A comparison of support vector machines and artificial neural networks for mid-term load forecasting. In IEEE International Conference on Industrial Technology, ICIT (pp. 95–101). Athens, Greece: IEEE.

Rahman, S., & Hazim, O. (1996). Load forecasting for multiple sites: development of an expert system-based technique. Electric Power Systems Research, 39(3), 161-169.

Rendon-Sanchez, J. F., & Menezes, L. M. (2019). Structural combination of seasonal exponential smoothing forecasts applied to load forecasting. European Journal of Operational Research, 275(3), 916-924.

Saber, A. Y., & Alam, A. K. M. R. (2018). Short term load forecasting using multiple linear regression for big data. In IEEE Symposium Series on Computational Intelligence - SSCI (pp. 1–6). Honolulu, HI, USA: IEEE.

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. IEEE Access : Practical Innovations, Open Solutions, 8, 143759-143768.

Setiawan, A., Koprinska, I., & Agelidis, V. G. (2009). Very short-term electricity load demand forecasting using support vector regression. In International Joint Conference on Neural Networks (pp. 2888–2894). Atlanta, GA, USA: IEEE.

Shahidehpour, M., Yamin, H., & Li, Z. (2002). Market operations in electric power systems: forecasting, scheduling, and risk management (1st ed.). Hoboken: Wiley.

Sindhu, C., Som, B., & Singh, S. P. (2021a). Aspect based opinion mining leveraging weighted bigru and CNN module in parallel. In International Conference on Intelligent Technologies - CONIT (pp. 1-7). Hubli, India: IEEE.

Sindhu, C., Som, B., & Singh, S. P. (2021b). Aspect-oriented sentiment classification using BiGRU-CNN model. In 5th International Conference on Computing Methodologies and Communication - ICCMC (pp. 984-989). Erode, India: IEEE..

Singh, A. K., & Khatoon, S. (2013). An overview of electricity demand forecasting techniques. National Conference on Emerging Trends in Electrical, Instrumentation &. Communications Engineer, 3(3), 38-48.

Soliman, S. A., & Al-Kandari, A. M. (2010). Electrical load forecasting: modeling and model construction (1st ed.). Oxford: Butterworth-Heinemann.

Talathi, S. S., & Vartak, A. (2015). Improving performance of recurrent neural network with relu nonlinearity. Neural and Evolutionary Computing, 1, ArXiv:1511.03771. Retrieved in 2021 November 04, from

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. Energies, 11(12), 3493.

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 55th International Universities Power Engineering Conference - UPEC. Turin, Italy: IEEE.

Upadhaya, D., Thakur, R., & Singh, N. K. (2019). A systematic review on the methods of short term load forecasting. In 2nd International Conference on Power Energy Environment and Intelligent Control - PEEIC (pp. 6-11). Greater Noida, India: IEEE.

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. Alexandria Engineering Journal, 59(5), 3111-3118.

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. International Transactions on Electrical Energy Systems, 31(1), 1-15.

Wu, L., Kong, C., Hao, X., & Chen, W. (2020b). A short-term load forecasting method based on GRU-CNN hybrid neural network model. Mathematical Problems in Engineering, 2020, 1-10.

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 2nd IEEE Conference on Energy Internet and Energy System Integration - EI2 (pp. 1–4). Beijing, China: IEEE

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. IEEE Access : Practical Innovations, Open Solutions, 9, 69002-69009.

Yan, K., Li, W., Ji, Z., Qi, M., & Du, Y. (2019). A hybrid LSTM neural network for energy consumption forecasting of individual households. IEEE Access : Practical Innovations, Open Solutions, 7, 157633-157642.

Yang, H., Huang, C., & Huang, C. (1996). Identification of ARMAX model for short term load forecasting: an evolutionary programming approach. IEEE Transactions on Power Systems, 11(1), 403-408.

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. IEEE Access: Practical Innovations, Open Solutions, 6(8), 73750-73759.

Submitted date:

Accepted date:

61afb36fa95395450b695886 production Articles
Links & Downloads


Share this page
Page Sections