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Forecasting Electric Energy Demand using a predictor model based on Liquid State Machine
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International Journal of Artificial Intelligence and Expert Systems (IJAE)
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Volume:  1    Issue:  2
Pages:  7-53
Publication Date:   July 2010
ISSN (Online): 2180-124X
40 - 53
Published Date   
CSC Journals, Kuala Lumpur, Malaysia
Keywords   Abstract   References   Cited by   Related Articles   Collaborative Colleague
KEYWORDS:   Liquid State Machine, Pulsed Neural Networks, Prediction, Electric Energy Demand 
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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. 
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Neusa Grando : Colleagues
Tania Mezzadri Centeno : Colleagues
Sílvia Silva da Costa Botelho : Colleagues
Felipe Michels Fontoura : Colleagues  
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