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Forecasting Electric Energy Demand using a predictor model based on Liquid State Machine
Neusa Grando, Tania Mezzadri Centeno, Sílvia Silva da Costa Botelho, Felipe Michels Fontoura
Pages - 40 - 53     |    Revised - 30-06-2010     |    Published - 10-08-2010
Volume - 1   Issue - 2    |    Publication Date - July 2010  Table of Contents
Liquid State Machine, Pulsed Neural Networks, Prediction, Electric Energy Demand
Electricity demand forecasts are required by companies who need to predict their customers’ demand, and by those wishing to trade electricity as a commodity on financial markets. It is hard to find the right prediction method for a given application if not a prediction expert. Recent works show that Liquid State Machines (LSM’s) can be applied to the prediction of time series. The main advantage of the LSM is that it projects the input data in a high-dimensional dynamical space and therefore simple learning methods can be used to train the readout. In this paper we present an experimental investigation of an approach for the computation of time series prediction by employing Liquid State Machines (LSM) in the modeling of a predictor in a case study for short-term and long-term electricity demand forecasting. Results of this investigation are promising, considering the error to stop training the readout, the number of iterations of training of the readout and that no strategy of seasonal adjustment or preprocessing of data was achieved to extract non-correlated data out of the time series.
CITED BY (4)  
1 George, J. B., Abraham, G. M., Singh, K., Ankolekar, S. M., Amrutur, B., & Sikdar, S. K. (2014). Input coding for neuro-electronic hybrid systems. Biosystems, 126, 1-11.
2 Usman, O. L., & Alaba, O. B. (2014). Predicting Electricity Consumption Using Radial Basis Function (RBF) Network. International Journal of Computer Science and Artificial Intelligence, 4(2), 54.
3 Uma, S., Chitra, A., & Suganthi, J. (2013). Design of a non-linear time series prediction model for daily electricity demand forecasting. International Journal of Business Innovation and Research, 7(3), 298-317.
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Miss Neusa Grando
UTFPR - Brazil
Dr. Tania Mezzadri Centeno
UTFPR - Brazil
Professor Sílvia Silva da Costa Botelho
FURG - Brazil
Mr. Felipe Michels Fontoura
UTFPR - Brazil

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