RBF Neural Network and ANFIS-Based Short-Term Load Forecasting Approach in Real-Time Price Environment

Zhang, Yun; Quan, Zhou; Sun, Caixin; Lei, Shaolan; Liu, Yuming; Yang, Song

doi:10.1109/tpwrs.2008.922249

Cited by 357 publications

(44 citation statements)

References 15 publications

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“…Artificial neural network (ANN) is combined with wavelet transform called wavelet neural network is applied for LF. Zhang et al [15] presented a model to forecast shortterm load by combining the radial basis function (RBF) neural network with the adaptive neural fuzzy inference system (ANFIS). Xiao et al [16] presented an approach of back propagation neural network with rough set (RSBP) for complicated STLF with dynamic and non-linear factors to develop the accuracy of predictions.…”

Section: Introductionmentioning

confidence: 99%

Study on a Novel Short-Term Load Forecasting Method Based on Improved PSO and FRBFNN

Liu¹

2016

IJSIP

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Section: Introductionmentioning

confidence: 99%

Study on a Novel Short-Term Load Forecasting Method Based on Improved PSO and FRBFNN

Liu¹

2016

IJSIP

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“…In Ref. [15], a model which can forecast the short-term electricity load was established via the radial basis function neural network and ANFIS. The failure detection of turning tool by using the ANFIS was discussed in Ref.…”

Section: Introductionmentioning

confidence: 99%

Cycle-based ammonia-coverage-ratio reference generator design for Diesel engine two-cell selective catalytic reduction systems via a fuzzy approach

Zhang

Wang

2015

Fuel

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“…These networks work best when a large amount of training data is available. RBF neural networks have been employed for functional approximation in time-series modeling because of their nonlinear approximation properties [6,15,19,21,22]. However, these studies did not achieve a precision of less than 1.4% for the mean absolute percent error (MAPE).…”

Section: Introductionmentioning

confidence: 99%

Daily Electric Load Forecasting Based on RBF Neural Network Models

Hwang

2013

International Journal of Fuzzy Logic and Intelligent Systems

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This paper presents a method of improving the performance of a day-ahead 24-h load curve and peak load forecasting. The next-day load curve is forecasted using radial basis function (RBF) neural network models built using the best design parameters. To improve the forecasting accuracy, the load curve forecasted using the RBF network models is corrected by the weighted sum of both the error of the current prediction and the change in the errors between the current and the previous prediction. The optimal weights (called "gains" in the error correction) are identified by differential evolution. The peak load forecasted by the RBF network models is also corrected by combining the load curve outputs of the RBF models by linear addition with 24 coefficients. The optimal coefficients for reducing both the forecasting mean absolute percent error (MAPE) and the sum of errors are also identified using differential evolution. The proposed models are trained and tested using four years of hourly load data obtained from the Korea Power Exchange. Simulation results reveal satisfactory forecasts: 1.230% MAPE for daily peak load and 1.128% MAPE for daily load curve.

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RBF Neural Network and ANFIS-Based Short-Term Load Forecasting Approach in Real-Time Price Environment

Cited by 357 publications

References 15 publications

Study on a Novel Short-Term Load Forecasting Method Based on Improved PSO and FRBFNN

Study on a Novel Short-Term Load Forecasting Method Based on Improved PSO and FRBFNN

Cycle-based ammonia-coverage-ratio reference generator design for Diesel engine two-cell selective catalytic reduction systems via a fuzzy approach

Daily Electric Load Forecasting Based on RBF Neural Network Models

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