Abstract
In the modern electronic power system, energy management and load forecasting are important tasks. Energy management systems are designed concerning monitoring and optimizing the energy requirement in smart systems. This research work is divided into two parts. The first part will contain load forecasting and energy management in a smart grid. Load forecasting in the smart grid can be divided into three parts long-term, mid-term, and short-term load forecasting. The second part will describe energy usage optimization for the electric vehicle. Here we will show grids to vehicle energy demand management and optimization. This chapter will first introduce different deep learning techniques and then discuss their applications related to smart-grid and smart vehicle.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Clifford, E., Clancy, M.: Impact of wind generation on wholesale electricity costs in 2011. Sustainable Energy Authority of Ireland, Ireland (2011)
Agrawal, P.: Overview of DOE microgrid activities. In: Proceedings of Symposium on Microgrid, Montreal, vol. 23 (2006)
Logenthiran, T., Srinivasan, D., Shun, T.Z.: Demand side management in smart grid using heuristic optimization. IEEE Trans. Smart Grid 3(3), 1244–1252 (2012)
Wang, Z., Srinivasan, R.S.: A review of artificial intelligence based building energy use prediction: contrasting the capabilities of single and ensemble prediction models. Renew. Sustain. Energy Rev. 75, 796–808 (2017)
Ghalehkhondabi, I., Ardjmand, E., Weckman, G.R., Young, W.A.: An overview of energy demand forecasting methods. Energy Syst. 8(2), 411–447 (2017)
Ryu, S., Noh, J., Kim, H.: Deep neural network based demand side short term load forecasting. Energies 10(1), 3 (2017)
Bouktif, S., Fiaz, A., Ouni, A., Serhani, M.A.: Optimal deep learning lstm model for electric load forecasting using feature selection and genetic algorithm: comparison with machine learning approaches. Energies 11(7), 1636 (2018)
Dong, X., Qian, L., Huang, L.: Short-term load forecasting in smart grid: a combined CNN and K-means clustering approach. In: Proceedings of IEEE International Conference on Big Data and Smart Computing, pp. 119–125 (2017)
Wang, Y., Liu, M., Bao, Z., Zhang, S.: Short-term load forecasting with multi-source data using gated recurrent unit neural networks. Energies 11(5), 1138 (2018)
Agrawal, R.K., Muchahary, F., Tripathi, M.M.: Long term load forecasting with hourly predictions based on long-short-term-memory networks. In: Proceedings of IEEE Texas Power and Energy Conference, pp. 1–6 (2018)
Barger, D.J.: MGE experience with INSITE spatial load forecasting. In: Proceedings of IEEE Power and Energy Society General Meeting, pp. 1–3 (2011)
López, K.L., Gagné, C., Gardner, M.A.: Demand-side management using deep learning for smart charging of electric vehicles. IEEE Trans. Smart Grid 10(3), 2683–2691 (2018)
Xiong, Y., Wang, B., Chu, C.C., Gadh, R.: Electric vehicle driver clustering using statistical model and machine learning. In: Proceedings of IEEE Power & Energy Society General Meeting, pp. 1–5 (2018)
Mocanu, E., Mocanu, D.C., Nguyen, P.H., Liotta, A., Webber, M.E., Gibescu, M., Slootweg, J.G.: On-line building energy optimization using deep reinforcement learning. IEEE Trans. Smart Grid 10(4), 3698–3708 (2018)
Kombrink, S., Mikolov, T., Karafiát, M., Burget, L.: Recurrent neural network based language modeling in meeting recognition. Interspeech 2011, 2877–2880 (2011)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv:1412.3555 (2014)
Shayeghi, H., Shayanfar, H.A., Azimi, G.: Intelligent neural network based STLF. Int. J. Comput. Syst. Sci. Eng. 4(1), 17–27 (2009)
Sallab, A.E., Abdou, M., Perot, E., Yogamani, S.: Deep reinforcement learning framework for autonomous driving. Electron. Imaging 2017(19), 70–76 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Banerjee, S., Ghosh, S., Mishra, B.K. (2022). Application of Deep Learning for Energy Management in Smart Grid. In: Acharjya, D.P., Mitra, A., Zaman, N. (eds) Deep Learning in Data Analytics. Studies in Big Data, vol 91. Springer, Cham. https://doi.org/10.1007/978-3-030-75855-4_13
Download citation
DOI: https://doi.org/10.1007/978-3-030-75855-4_13
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-75854-7
Online ISBN: 978-3-030-75855-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)