Symbiotic Structure Learning Algorithm for Feedforward Neural-Network-Aided Grey Model and Prediction Applications

Yang, Shih-Hung; Huang, Wun-Jhu; Tsai, Jian-Feng; Chen, Yon‐Ping

doi:10.1109/access.2017.2702340

Cited by 5 publications

(3 citation statements)

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“…The GP model or GM(1,1) was first proposed to deal with the data in grey system. It is able to analyze system that includes insufficient information and unapparent relationship [25][26][27]. Hence, the GP model is often used in predicting data in non-linear system based on limited information.…”

Section: Grey Prediction Model For Non-linear Behavior Forecastingmentioning

confidence: 99%

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A hybrid model for short term real-time electricity price forecasting in smart grid

2018

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Background: With the prominent growth of power market, real-time electricity price has become a trend in smart grid as it enables moderation of power consumption of customers. Accurate forecast of real-time price (RTP) has much influence on customers' behaviors, such as better scheduling operating time of domestic appliances in order to maximize benefit. In this paper, an innovative hybrid RTP forecasting model considering linear and non-linear behaviors within input data, is proposed to forecast the short-term electricity prices in smart grid. Results: The effectiveness of the proposed hybrid forecasting model is verified by numerical results in terms of forecasting performance evaluations. The results clearly demonstrate that our approach is effective in RTP forecasting with a high accuracy. The mean absolute percentage error (MAPE) is approximate to 3.5% and it also significantly outperforms the existing models. Conclusion: Based on the achieved results, we can conclude that the proposed hybrid model is an accurate and efficient tool in short-term RTP forecasting and it is potentially effective to a variety of forecasting tasks.

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Section: Grey Prediction Model For Non-linear Behavior Forecastingmentioning

confidence: 99%

“…α is the learning ratio and 0 < α < 1. β is the momentum factor. • Adjust the connection weights W ir and the bias of the layer LB nodes T r : (26) T r = T r + α • e r + β • T r (27) where W ir and T r are the adjusting values of the previous learning loop.…”

Section: Artificial Neural Network For Error Optimizationmentioning

confidence: 99%

A hybrid model for short term real-time electricity price forecasting in smart grid

2018

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“…Before the implementation of GM, the original dataset is preprocessed into an equal interval sequence, in order to improve the effectiveness of GM. Yang et al (Yang et al, 2017) combine the conventional GM with a feed-forward neural network. This hybrid method can adaptively update the weights of the neural network in the case of limited training data and can accurately predict the angular rate of a rehabilitation system.…”

Section: Introductionmentioning

confidence: 99%

Forecast of urban traffic carbon emission and analysis of influencing factors

2021

Energy Efficiency

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An extended neural ordinary differential equation network with grey system and its applications

Zhang,

Xiao,

Gao

2024

Neurocomputing

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Symbiotic Structure Learning Algorithm for Feedforward Neural-Network-Aided Grey Model and Prediction Applications

Cited by 5 publications

References 26 publications

A hybrid model for short term real-time electricity price forecasting in smart grid

A hybrid model for short term real-time electricity price forecasting in smart grid

Forecast of urban traffic carbon emission and analysis of influencing factors

An extended neural ordinary differential equation network with grey system and its applications

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