Abstract
The prediction of electricity consumption has become an important part of managing the smart grid. Smart grid management involves energy production (from traditional and renewable sources), transportation and measurements (smart meters). Storing large amounts of electrical energy is not possible, therefore it is necessary to precisely predict energy consumption. Nowadays deep learning approaches are successfully used in different artificial intelligence areas. Deep neural network architecture called WaveNet was designed for text to speech task, improving speech quality over currently used approaches. In this paper, we present modification of the WaveNet architecture from speech (sound waves) generation to energy load prediction.
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
Hernández, L., Baladrón, C., Aguiar, J., Carro, B., Sánchez-Esguevillas, A., Lloret, J.: Artificial neural networks for short-term load forecasting in microgrids environment. Energy 75, 252–264 (2014)
Jurado, S., Nebot, Á., Mugica, F., Avellana, N.: Hybrid methodologies for electricity load forecasting: entropy-based feature selection with machine learning and soft computing techniques. Energy 86, 276–291 (2015)
Ghelardoni, L., Ghio, A., Anguita, D.: Energy load forecasting using empirical mode decomposition and support vector regression. IEEE Trans. Smart Grid 4, 549–556 (2013)
Bahrami, S., Hooshmand, R.-A., Parastegari, M.: Short term electric load forecasting by wavelet transform and grey model improved by PSO (particle swarm optimization) algorithm. Energy 72, 434–442 (2014)
Feinberg, E.A., Genethliou, D.: Load forecasting. In: Chow, J.H., Wu, F.F., Momoh, J. (eds.) Applied Mathematics for Restructured Electric Power Systems. PEPS, pp. 269–285. Springer, Boston (2005). doi:10.1007/0-387-23471-3_12
van den Oord, A., et al.: WaveNet: A Generative Model for Raw Audio. CoRR abs/1609.03499 (2016)
Zhao, H., Magoulés, F.: A review on the prediction of building energy consumption. Renew. Sustain. Energy Rev. 16, 3586–3592 (2012)
Grmanová, G., Laurinec, P., Rozinajová, V., Ezzeddine, A.B., Lucká, M., Lacko, P., Vrablecová, P., Návrat, P.: Incremental ensemble learning for electricity load forecasting. Acta Polytech. Hung. 13(2) (2016)
Čerňanský, M., Makula, M., Trebatický, P., Lacko, P.: Text correction using approaches based on Markovian architectural bias. In: Proceedings of the 10th International Conference on Engineering Applications of Neural Networks, Publishing Centre Alexander Technological Educational Institute of Thessaloniki, EANN 2007, pp. 221–228 (2007)
Kavousi-Fard, A., Samet, H., Marzbani, F.: A new hybrid modified firefly algorithm and support vector regression model for accurate short term load forecasting. Expert Syst. Appl. 41(13), 6047–6056 (2014)
Ko, C.N., Lee, C.M.: Short-term load forecasting using SVR (support vector regression)-based radial basis function neural network with dual extended Kalman filter. Energy 49, 413–422 (2013)
Chaturvedi, D.K., Sinha, A.P., Malik, O.P.: Short term load forecast using fuzzy logic and wavelet transform integrated generalized neural network. Int. J. Electr. Power Energy Syst. 67, 230–237 (2015)
Vardakas, J.S., Zenginis, I.: A survey on short-term electricity price prediction models for smart grid applications. In: Mumtaz, S., Rodriguez, J., Katz, M., Wang, C., Nascimento, A. (eds.) WICON 2014. LNICSSITE, vol. 146, pp. 60–69. Springer, Cham (2015). doi:10.1007/978-3-319-18802-7_9
Acknowledgments
This work was partially supported by the Scientific Grant Agency of the Slovak Republic, grant No. VG 1/0752/14 and with the support of the Research and Development Operational Programme for the project International centre of excellence for research of intelligent and secure information-communication technologies and systems, ITMS 26240120039, co-funded by the European Regional Development Fund.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Farkas, M., Lacko, P. (2017). Using Advanced Audio Generating Techniques to Model Electrical Energy Load. In: Boracchi, G., Iliadis, L., Jayne, C., Likas, A. (eds) Engineering Applications of Neural Networks. EANN 2017. Communications in Computer and Information Science, vol 744. Springer, Cham. https://doi.org/10.1007/978-3-319-65172-9_4
Download citation
DOI: https://doi.org/10.1007/978-3-319-65172-9_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-65171-2
Online ISBN: 978-3-319-65172-9
eBook Packages: Computer ScienceComputer Science (R0)