An enhanced radial basis function network for short-term electricity price forecasting

Lin, Whei-Min; Gow, Hong-Jey; Tsai, Ming-Tang

doi:10.1016/j.apenergy.2010.04.006

Cited by 75 publications

(20 citation statements)

References 19 publications

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“…According to equation (6), the estimates of parameter vector b(t) can be transformed to solve the following optimization problem: (7) in which N is the sample size,…”

Section: Quantile Regressionmentioning

confidence: 99%

“…However, it is difficult to improve the prediction accuracy of STLF due to the nonlinear and random-like behaviors of system load, weather conditions, variations of social and economic environments, and so on [2]. In order to improve the prediction accuracy of load forecasting model, various STLF methods have been introduced in the past years, including artificial neural network (ANN) [3], grey Bernoulli model [4], wavelet transform combined with neuroevolutionary algorithm [5], radial basis function (RBF) [6], particle swarm optimization [7], support vector regression (SVR) [8], combining sister forecasts [9], ensemble Kalman filter [10], combined model [11], and hybrid methods [12,13].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Short-term power load probability density forecasting based on quantile regression neural network and triangle kernel function

You

Wan

et al. 2016

Energy

101

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“…According to equation (6), the estimates of parameter vector b(t) can be transformed to solve the following optimization problem: (7) in which N is the sample size,…”

Section: Quantile Regressionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Short-term power load probability density forecasting based on quantile regression neural network and triangle kernel function

You

Wan

et al. 2016

Energy

101

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“…Since radial basis ANN use radial functions as activation functions, they can be utilized successfully for the solution of high-dimensional nonlinear problems. There are several studies in the literature that involve the use of radial basis functions for the solution of forecasting problem, namely Rivas et al [12], Aslanargun et al [2], Lin et al [9], Shen et al [16], Li-xia et al [10], Coelho and Santos [4] and Wu and Liu [20]. In recent years, different types of ANN such as multiplicative neuron model artificial neural networks (MNM-ANN) have been used for the time series forecasting problems.…”

Section: Introductionmentioning

confidence: 99%

Multiplicative neuron model artificial neural network based on Gaussian activation function

Gündoğdu

Eğrioğlu

Aladağ

et al. 2015

Neural Comput & Applic

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Multiplicative neuron model-based artificial neural networks are one of the artificial neural network types which have been proposed recently and have produced successful forecasting results. Sigmoid activation function was used in multiplicative neuron model-based artificial neural networks in the previous studies. Although artificial neural networks which involve the use of radial basis activation function produce more successful forecasting results, Gaussian activation function has not been used for multiplicative neuron model yet. In this study, rather than using a sigmoid activation function, Gaussian activation function was used in multiplicative neuron model artificial neural network. The weights of artificial neural network and parameters of activation functions were optimized by guaranteed convergence particle swarm optimization. Two major contributions of this study are as follows: the use of Gaussian activation function in multiplicative neuron model for the first time and the optimizing of central and propagation parameters of activation function with the weights of artificial neural network in a single optimization process. The superior forecasting performance of the proposed Gaussian activation function-based multiplicative neuron model artificial neural network was proved by applying it to real-life time series.

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“…The ANN is a simple, powerful and flexible tool for forecasting, providing better solution to model complex non-linear relationships. 8 The considered hard computing models are linear predictors, 13 while electricity price is generally a nonlinear. The proposed ANN soft computing approach works on complex nonlinear relationships than the traditional linear models.…”

Section: Introductionmentioning

confidence: 99%

Day-Ahead Price Forecasting in Asia's First Liberalized Electricity Market using Artificial Neural Networks

Anbazhagan

Kumarappan²

2011

International Journal of Computational Intelligence Systems

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This paper proposes a comparative model for the day-ahead electricity price forecasting that could be realized using multi-layer neural network (MLNN) with levenberg-marquardt (LM) algorithm, generalized regression neural network (GRNN) and cascade-forward neural network (CFNN). In this work applications of various models were applied to national electricity market of Singapore (NEMS), i.e. Asia's first liberalized electricity market. The individual price of year 2006 is very volatile with a very wide range. Therefore, accurate forecasting models are required for Singapore electricity market company (EMC) to maximize their profits and for consumers to maximize their utilities. Hence the year 2006 has been taken for forecasting the uniform Singapore electricity price (USEP). The mean absolute percentage error (MAPE) results show that the proposed CFNN model possess better forecasting abilities than the other models and its performance was least affected by the volatility.Keywords: Price forecasting, Levenberg-marquardt (LM) algorithm, Generalized regression neural network (GRNN), Cascade-forward neural network (CFNN), National electricity market of singapore (NEMS), Uniform singapore energy price (USEP).

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An enhanced radial basis function network for short-term electricity price forecasting

Cited by 75 publications

References 19 publications

Short-term power load probability density forecasting based on quantile regression neural network and triangle kernel function

Short-term power load probability density forecasting based on quantile regression neural network and triangle kernel function

Multiplicative neuron model artificial neural network based on Gaussian activation function

Day-Ahead Price Forecasting in Asia's First Liberalized Electricity Market using Artificial Neural Networks

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