Application of echo state networks in short-term electric load forecasting

Deihimi, Ali; Showkati, Hemen

doi:10.1016/j.energy.2012.01.007

Cited by 112 publications

(61 citation statements)

References 41 publications

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“…In recent years, a number of applications of ESN in streamflow forecasting [7][8][9] for hydropower plant and load forecasting [10][11][12] for power system have been revealed in the literature. The results indicate that ESN not only benefits from some feedbacks like other RNNs that enable them to model any complex dynamic behavior, but also gains a sparsely interconnected reservoir of neurons leading to a very fast and simple training procedure, unlike the complicated and time consuming training process of other RNNs without reservoir.…”

Section: Introductionmentioning

confidence: 99%

Echo State Network with Bayesian Regularization for Forecasting Short-Term Power Production of Small Hydropower Plants

et al. 2015

Energies

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Abstract:As a novel recurrent neural network (RNN), an echo state network (ESN) that utilizes a reservoir with many randomly connected internal units and only trains the readout, avoids increased complexity of training procedures faced by traditional RNN. The ESN can cope with complex nonlinear systems because of its dynamical properties and has been applied in hydrological forecasting and load forecasting. Due to the linear regression algorithm usually adopted by generic ESN to train the output weights, an ill-conditioned solution might occur, degrading the generalization ability of the ESN. In this study, the ESN with Bayesian regularization (BESN) is proposed for short-term power production forecasting of small hydropower (SHP) plants. According to the Bayesian theory, the weights distribution in space is considered and the optimal output weights are obtained by maximizing the posterior probabilistic distribution. The evidence procedure is employed to gain optimal hyperparameters for the BESN model. The recorded data obtained from the SHP plants in two different counties, located in Yunnan Province, China, are utilized to validate the proposed model. For comparison, the feed-forward neural networks with Levenberg-Marquardt algorithm (LM-FNN) and the generic ESN are also employed. The results indicate that BESN outperforms both LM-FNN and ESN.

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Section: Introductionmentioning

confidence: 99%

Echo State Network with Bayesian Regularization for Forecasting Short-Term Power Production of Small Hydropower Plants

et al. 2015

Energies

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“…As example researches, Cai et al [65] have used distributed adaptive resonance theory (dART) and hyper-spherical ARTMAP (HS-ARTMAP) neural networks. Deihimi and Showkati [66] have used echo state network (ESN), as the state-ofthe-art RNN, for STLF.…”

Section: Related Workmentioning

confidence: 99%

Time series prediction using PSO-optimized neural network and hybrid feature selection algorithm for IEEE load data

Sheikhan

Mohammadi

2012

Neural Comput & Applic

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Artificial neural networks have been widely used in time series prediction. In this paper, multi-layer feedforward neural networks with optimized structures, using particle swarm optimization (PSO) algorithm, are used for hourly load prediction based on load time series of IEEE Reliability Test System. To have a small and appropriate feature subset, a hybrid method is used for feature selection in this paper. This hybrid method uses the combination of genetic algorithm (GA) and ant colony optimization (ACO) algorithm. The season, day of the week, time of the day and history load are considered as load influencing factors in this study based on the mentioned standard load dataset. The optimized number of neurons in the hidden layers of multi-layer perceptron (MLP) is determined using PSO algorithm. Experimental results show that the proposed hybrid model offers superior performance, in terms of mean absolute percentage error (MAPE), in time series prediction as compared to some recent researches in this field.

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“…As one of the forecasting tools, the application of fuzzy logic and ANN to predict the load demand in the short-term category was researched by Badri et al [36]. Deihimi and Showkati [37] adopted the echo state network as one of the recurrent NN (neural network) paradigms for forecasting the load, which belongs to an electric utility in North America. Another type of neural network (NN), named Kohonen's self-organizing maps, was used by L opez et al [38] for short-term load forecasting of the Spanish electricity market.…”

Section: Introductionmentioning

confidence: 99%

A combined model based on data pre-analysis and weight coefficients optimization for electrical load forecasting

Xiao

Wang

Hou

et al. 2015

Energy

129

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Application of echo state networks in short-term electric load forecasting

Cited by 112 publications

References 41 publications

Echo State Network with Bayesian Regularization for Forecasting Short-Term Power Production of Small Hydropower Plants

Echo State Network with Bayesian Regularization for Forecasting Short-Term Power Production of Small Hydropower Plants

Time series prediction using PSO-optimized neural network and hybrid feature selection algorithm for IEEE load data

A combined model based on data pre-analysis and weight coefficients optimization for electrical load forecasting

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