Higher Order Wavelet Neural Networks with Kalman learning for wind speed forecasting

Ricalde, Luis J.; Catzin, G. A.; Alanis, Alma Y.; Sánchez, Edgar N.

doi:10.1109/ciasg.2011.5953332

Cited by 20 publications

(9 citation statements)

References 17 publications

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“…The first-week data are used for training the neural network [22], and the secondweek data are used for testing. We use the information from the second week in Figs.…”

Section: Simulation Resultsmentioning

confidence: 99%

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Electrical Microgrid Optimization via a New Recurrent Neural Network

Urias

Sánchez

Ricalde

2015

IEEE Systems Journal

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This paper presents the development and implementation of a new recurrent neural network for optimization as applied to optimal operation of an electrical microgrid, which is interconnected to the utility grid; moreover, it incorporates batteries, for energy storing and supplying, and an electric car. The proposed neural network determines the optimal amount of power over a time horizon of one week for wind, solar, and battery systems, including that of the electric car, in order to minimize the power acquired from the utility grid and to maximize the power supplied by the renewable energy sources. Simulation results illustrate that generation levels for each energy source over a time horizon can be reached in an optimal form.

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“…The first-week data are used for training the neural network [22], and the secondweek data are used for testing. We use the information from the second week in Figs.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…Such process is based on the application of the neural network shown in [22] and [23], which can predict time series. The neural network uses wavelet functions on its structure, as shown in Fig.…”

Section: Prediction Processmentioning

confidence: 99%

Electrical Microgrid Optimization via a New Recurrent Neural Network

Urias

Sánchez

Ricalde

2015

IEEE Systems Journal

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“…Due to the local properties of wavelets and the ability of adapting the wavelet shape according to the training data set instead of adapting the parameters of the fixed shape activation function, WNNs offer higher generalization capability compared to the classical feed forward ANNs [40]. Recently, a WNN using Mexican hat mother wavelet function is proposed for wind speed forecast [38]. However, Morlet wavelet has vanishing mean oscillatory behavior with more diverse oscillations with respect to Mexican hat wavelet, which can be seen from Fig.…”

Section: The Developed Wavelet Neural Networkmentioning

confidence: 98%

“…Wavelet can also be applied in a more efficient structure called wavelet neural network, in which wavelet functions are used as the activation functions of the neurons in neural networks. In [38], wavelet has been used in the form of WNN for wind speed prediction and it is trained by extended Kalman filter. Since such a model consists of many scaled and shifted wavelets of the utilized mother wavelet, it requires a powerful training algorithm to efficiently train the model and not to be trapped in local optima while finding the best input/output mapping function of the model.…”

Section: Literature Reviewmentioning

confidence: 99%

Wind power forecast using wavelet neural network trained by improved Clonal selection algorithm

Chitsaz

Amjady

Zareipour

2015

Energy Conversion and Management

215

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“…Due to their nonlinear modeling characteristics, neural networks have been successfully applied in control systems, pattern classification, pattern recognition, and time series forecasting problems. There are several previous works that use artificial neural networks to predict wind time series [2,4,11]. The best well-known training approach for recurrent neural networks (RNN) is the back propagation through time [12].…”

Section: Introductionmentioning

confidence: 99%

Neural Model with Particle Swarm Optimization Kalman Learning for Forecasting in Smart Grids

Alanis

Ricalde²,

Simetti

et al. 2013

Mathematical Problems in Engineering

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This paper discusses a novel training algorithm for a neural network architecture applied to time series prediction with smart grids applications. The proposed training algorithm is based on an extended Kalman filter (EKF) improved using particle swarm optimization (PSO) to compute the design parameters. The EKF-PSO-based algorithm is employed to update the synaptic weights of the neural network. The size of the regression vector is determined by means of the Cao methodology. The proposed structure captures more efficiently the complex nature of the wind speed, energy generation, and electrical load demand time series that are constantly monitorated in a smart grid benchmark. The proposed model is trained and tested using real data values in order to show the applicability of the proposed scheme.

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Higher Order Wavelet Neural Networks with Kalman learning for wind speed forecasting

Cited by 20 publications

References 17 publications

Electrical Microgrid Optimization via a New Recurrent Neural Network

Electrical Microgrid Optimization via a New Recurrent Neural Network

Wind power forecast using wavelet neural network trained by improved Clonal selection algorithm

Neural Model with Particle Swarm Optimization Kalman Learning for Forecasting in Smart Grids

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