Traffic Indexes Prediction Based on Grey Prediction Model

Peng, Hui; Qu, Wenqi; Tang, Junjian; Chen, Jin

doi:10.1109/iscid.2013.68

Cited by 2 publications

(1 citation statement)

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“…It transforms the forms of the irregular discrete sequences and displays the potential regularities within the sequences. Transforming the forms of the sequences can make the properties of stochastic and randomness get weaker thereby turning irregular sequences to regular ones [28][29][30]. Since only a few non-linear data proceeded from LS fitting model are used, it is quite appropriate to employ the GP model to estimate the non-linear behavior within the input data on this stage.…”

Section: Grey Prediction Model For Non-linear Behavior Forecastingmentioning

confidence: 99%

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%