Parameter auto-selection for hemispherical resonator gyroscope's long-term prediction model based on cooperative game theory

AbstractAdvanced grid technology represented by smart grid and energy internet is the core feature of the next-generation power grid. The next-generation power grid will be a large-scale cyber-physical system (CPS), which will have a higher level of risk management due to its flexibility in sensing and control. This paper explains the methods and results of a study on grid CPS’s behavior after risk. Firstly, a behavior model based on hybrid automata is built to simulate grid CPS’s risk decisions. Then, a GCPS risk transfer model based on cooperative game theory is built. The model allows decisions to ignore complex network structures. On this basis, a modified applicant-proposing algorithm to achieve risk optimum is proposed. The risk management model proposed in this paper can provide references for power generation and transmission decision after risk as well as risk aversion, an empirical study in north China verifies its validity.

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Input Selection for Long-Term Prediction of Time Series

Computational Intelligence and Bioinspired Systems - Lecture Notes in Computer Science ◽

10.1007/11494669_123 ◽

2005 ◽

pp. 1002-1009 ◽

Cited By ~ 4

Author(s):

Jarkko Tikka ◽

Jaakko Hollmén ◽

Amaury Lendasse

Keyword(s):

Time Series ◽

Input Selection ◽

Term Prediction ◽

Selection For ◽

Long Term Prediction

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A long-term prediction model for total phosphorus concentrations in Lake Kinneret

Water Research ◽

10.1016/0043-1354(89)90062-6 ◽

1989 ◽

Vol 23 (1) ◽

pp. 61-66 ◽

Cited By ~ 13

Author(s):

G. Herman ◽

A. Nishri ◽

T. Berman

Keyword(s):

Prediction Model ◽

Total Phosphorus ◽

Lake Kinneret ◽

Term Prediction ◽

Long Term Prediction

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A Long-Term Prediction Model of Beijing Haze Episodes Using Time Series Analysis

Computational Intelligence and Neuroscience ◽

10.1155/2016/6459873 ◽

2016 ◽

Vol 2016 ◽

pp. 1-7 ◽

Cited By ~ 2

Author(s):

Xiaoping Yang ◽

Zhongxia Zhang ◽

Zhongqiu Zhang ◽

Liren Sun ◽

Cui Xu ◽

...

Keyword(s):

Time Series ◽

Time Series Analysis ◽

Prediction Model ◽

Industrial Development ◽

Measurement Model ◽

Vector Autoregressive ◽

Series Analysis ◽

Term Prediction ◽

Long Term Prediction

The rapid industrial development has led to the intermittent outbreak of pm2.5 or haze in developing countries, which has brought about great environmental issues, especially in big cities such as Beijing and New Delhi. We investigated the factors and mechanisms of haze change and present a long-term prediction model of Beijing haze episodes using time series analysis. We construct a dynamic structural measurement model of daily haze increment and reduce the model to a vector autoregressive model. Typical case studies on 886 continuous days indicate that our model performs very well on next day’s Air Quality Index (AQI) prediction, and in severely polluted cases (AQI ≥ 300) the accuracy rate of AQI prediction even reaches up to 87.8%. The experiment of one-week prediction shows that our model has excellent sensitivity when a sudden haze burst or dissipation happens, which results in good long-term stability on the accuracy of the next 3–7 days’ AQI prediction.

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