Abstract
ESN (Echo state network) is a novel recurrent neural network and it is also acknowledged as a powerful temporal processing method, especially in real-valued, time-series forecasting fields. The current research results believe that the connection structure of the reservoir has significant effect for ESN’s forecasting performance. However, the randomly generated reservoir is hard to establish a optimal reservoir structure for a given task. Optimizing the connection structure of reservoir can be considered as a feature selection issue and this issue can be solved by binary optimization algorithm. SOS (Symbiotic organisms search) is a recently proposed heuristic algorithm and its superior performance is confirmed via many mathematical benchmark functions and engineering design problems. It’s worth noting that the original SOS is only suitable for continuous numerical optimization problems. In this paper, a binary SOS, called BSOS, is employed to optimize the connection structure of reservoir of the standard ESN. To verify the effectiveness of the proposed model, a real electric load series derived from New South Wales in Australia is adopted as benchmark dataset. The experimental results demonstrate that the proposed model can significantly improve the forecasting accuracy and it is a hopeful forecasting model.
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
Similar content being viewed by others
References
Research and application of neural network and its combination model in time series forecasting. Ph.D. thesis, LanZhou University (2018)
Cheng, M.Y., Prayogo, D.: Symbiotic organisms search: a new metaheuristic optimization algorithm. Comput. Struct. 139, 98–112 (2014)
Deihimi, A., Orang, O., Showkati, H.: Short-term electric load and temperature forecasting using wavelet echo state networks with neural reconstruction. Energy 57, 382–401 (2013)
Dutoit, X., Schrauwen, B., Van Campenhout, J., Stroobandt, D., Van Brussel, H., Nuttin, M.: Pruning and regularization in reservoir computing. Neurocomputing 72(7), 1534–1546 (2009)
Jaeger, H.: The “echo state” approach to analysing and training recurrent neural networks-with an erratum note. Bonn, Germany: German Natl. Res. Center Inf. Technol. GMD Tech. Rep. 148, 34 (2001)
Nastos, P., Paliatsos, A., Koukouletsos, K., Larissi, I., Moustris, K.: Artificial neural networks modeling for forecasting the maximum daily total precipitation at Athens, Greece. Atmosph. Res. 144, 141–150 (2014)
Ozturk, M.C., Xu, D., Príncipe, J.C.: Analysis and design of echo state networks. Neural Comput. 19(1), 111–138 (2007)
Song, Q., Feng, Z.: Effects of connectivity structure of complex echo state network on its prediction performance for nonlinear time series. Neurocomputing 73(10), 2177–2185 (2010)
Wang, H., Yan, X.: Optimizing the echo state network with a binary particle swarm optimization algorithm. Knowl.-Based Syst. 86, 182–193 (2015)
Zhang, X., Wang, J., Zhang, K.: Short-term electric load forecasting based on singular spectrum analysis and support vector machine optimized by cuckoo search algorithm. Electric Power Syst. Res. 146(2), 270–285 (2017)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Pan, L., Zhang, B. (2020). Optimizing the Reservoir Connection Structure Using Binary Symbiotic Organisms Search Algorithm: A Case Study on Electric Load Forecasting. In: Tian, Y., Ma, T., Khan, M. (eds) Big Data and Security. ICBDS 2019. Communications in Computer and Information Science, vol 1210. Springer, Singapore. https://doi.org/10.1007/978-981-15-7530-3_21
Download citation
DOI: https://doi.org/10.1007/978-981-15-7530-3_21
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-7529-7
Online ISBN: 978-981-15-7530-3
eBook Packages: Computer ScienceComputer Science (R0)