Short-term Traffic Flow and Hourly Electric Load Forecasting Algorithm based on Echo State Neural Networks

Qingsong Song; Xibin Liu; Xiangmo Zhao
March 2012
International Journal of Digital Content Technology & its Applic;Mar2012, Vol. 6 Issue 4, p166
Academic Journal
An algorithm for short-term traffic flow and hourly electric load forecasting based on echo state neural networks (ESN) is proposed in this paper. ESN is a new paradigm for using recurrent neural networks (RNNs) with a simpler training method. While the prediction, traffic flows and load patterns are treated as time series signals; no further information is used than the past data records, such as weather, seasonal variations. The relation between key parameter of the ESN and the predicting performance is discussed; ESN and feedforward neural network (FNN) are compared with the same tasks also. Simulation experiment results demonstrate that the proposed ESN algorithm is valid and can obtain more accurate predicting results than the FNNs for these short-term traffic flow and hourly electric load prediction problems.


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