Abstract
Short-term power load forecasting is quite vital in maintaining the balance between power production and power consumption of the power grid. Prediction accuracy not only affects the power grid construction, but also influences the economic development of the power grid. This paper proposes a short-term load forecasting based on Convolutional Neural Networks and Bidirectional Long Short-Term Memory (CNN-BiLSTM) with Bayesian Optimization (BO) and Attention Mechanism (AM). The BiLSTM is good at time series forecasting, and the Attention Mechanism can help the model to focus on the important part of the BiLSTM output. In order to make the forecasting performance of the model as good as possible, the Bayesian Optimization is used to tune the hyperparameters of the model. The input of the model is history load, time slot, and meteorological factors. In order to eliminate the seasonal influence, the data set is divided into four subsets with respect to four seasons. The performance of the proposed model is compared with other forecasting models by MAE, RMSE, MAPE, and \(R^2\) score. The experiment results show that the proposed model fits the actual values best and has the best forecasting performance among the contrast models.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
He, F., Zhou, J., Feng, Z., Liu, G., Yang, Y.: A hybrid short-term load forecasting model based on variational mode decomposition and long short-term memory networks considering relevant factors with Bayesian optimization algorithm. Appl. Energy 237, 103–116 (2019)
Torkzadeh, R., Mirzaei, A., Mirjalili, M.M., Anaraki, A.S., Sehhati, M.R., Behdad, F.: Medium term load forecasting in distribution systems based on multi linear regression and principal component analysis: a novel approach. In: 19th Conference on Electrical Power Distribution Networks (EPDC), pp. 66–70. IEEE, Tehran (2014)
Hagan, M.T., Behr, S.M.: The time series approach to short term load forecasting. IEEE Trans. Power Syst. 2(3), 785–791 (1987)
Huang, S.-J., Shih, K.-R.: Short-term load forecasting via ARMA model identification including non-Gaussian process considerations. IEEE Trans. Power Syst. 18(2), 673–679 (2003)
Vermaak, J., Botha, E.C.: Recurrent neural networks for short-term load forecasting. IEEE Trans. Power Syst. 13(1), 126–132 (1998)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Kong, W., Dong, Z.Y., Jia, Y., Hill, D.J., Xu, Y., Zhang, Y.: Short-term residential load forecasting based on LSTM recurrent neural network. IEEE Trans. Smart Grid 10(1), 841–851 (2019)
Hu, P., Tong, J., Wang, J., et al.: A hybrid model based on CNN and Bi-LSTM for urban water demand prediction. In: Congress on Evolutionary Computation (CEC), Wellington, New Zealand, pp. 1088–1094. IEEE (2019)
Alhussein, M., Aurangzeb, K., Haider, S.I.: Hybrid CNN-LSTM model for short-term individual household load forecasting. IEEE Access 8, 180544–180557 (2020)
Pan, C., Tan, J., Feng, D., Li, Y.: Very short-term solar generation forecasting based on LSTM with temporal attention mechanism. In: 5th International Conference on Computer and Communications (ICCC), Chengdu, China, pp. 267–271. IEEE (2019)
Wang, M., Cheng, J., Zhai, H.: Life prediction for machinery components based on CNN-BiLSTM network and attention model. In: 5th Information Technology and Mechatronics Engineering Conference (ITOEC), Chongqing, China, pp. 851–855. IEEE (2020)
Kim, T., Cho, S.: Particle swarm optimization-based CNN-LSTM networks for forecasting energy consumption. In: Congress on Evolutionary Computation (CEC), Wellington, New Zealand, pp. 1510–1516. IEEE (2019)
Lu, J., Zhang, Q., Yang, Z., et al.: A hybrid model based on convolutional neural network and long short-term memory for short-term load forecasting. In: Power and Energy Society General Meeting (PESGM), Atlanta, GA, USA, pp. 1–5. IEEE (2019)
Niu, D.X., Shi, H.F., Wu, D.D.: Short-term load forecasting using Bayesian neural networks learned by Hybrid Monte Carlo algorithm. Appl. Soft Comput. 12(6), 1822–1827 (2012)
He, Y.J., Zhu, Y.C., Gu, J.C., et al.: Similar day selecting based neural network model and its application in short-term load forecasting. In: International Conference on Machine Learning and Cybernetics, Guangzhou, China, pp. 4760–4763, IEEE (2005)
Pelikan, M., Goldberg, D.E., Cantu-Paz, E.: BOA: The Bayesian optimization algorithm. In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 1999), pp. 525–532 (1999)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 Springer Nature Switzerland AG
About this paper
Cite this paper
Miao, K., Hua, Q., Shi, H. (2021). Short-Term Load Forecasting Based on CNN-BiLSTM with Bayesian Optimization and Attention Mechanism. In: Zhang, Y., Xu, Y., Tian, H. (eds) Parallel and Distributed Computing, Applications and Technologies. PDCAT 2020. Lecture Notes in Computer Science(), vol 12606. Springer, Cham. https://doi.org/10.1007/978-3-030-69244-5_10
Download citation
DOI: https://doi.org/10.1007/978-3-030-69244-5_10
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-69243-8
Online ISBN: 978-3-030-69244-5
eBook Packages: Computer ScienceComputer Science (R0)