Prediction of hydroelectric power generation in Japan
| Autor: | Guo-Dong Li, Masatake Nagai, Shiro Masuda |
|---|---|
| Rok vydání: | 2016 |
| Předmět: |
Engineering
021103 operations research Markov chain Operations research business.industry 020209 energy General Chemical Engineering 0211 other engineering and technologies Energy Engineering and Power Technology Regression analysis 02 engineering and technology Residual Original data Reliability engineering Electric power system Fuel Technology Hydroelectricity 0202 electrical engineering electronic engineering information engineering business Randomness |
| Zdroj: | Energy Sources, Part B: Economics, Planning, and Policy. 11:288-294 |
| ISSN: | 1556-7257 1556-7249 |
| DOI: | 10.1080/15567249.2012.708097 |
| Popis: | The prediction model for power systems based on grey model has been studied by many researches. However, the prediction accuracy of grey model is unsatisfying when original data shows great randomness. In this article, in order to improve the prediction capability of grey model, the regression model is first integrated into GM(1, 1) through compensation for the residual error series. Then Markov chain model is applied for achieving the high prediction accuracy. A real case of hydroelectric power generation in Japan is used to validate the effectiveness of the proposed model. Based on our prediction results for hydroelectric power generation in Japan by year from 2010 to 2015, the growth of hydroelectric power generation has not seen a very big change. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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