Short-term forecasting of renewable energy consumption: Augmentation of a modified grey model with a Kalman filter
© 2019 Elsevier B.V. One of the important global trends in near future is to replace fossil-fuel energy with sustainable energy. The accurate predictions of the renewable energy consumption are seemingly crucial in both national and international levels. In the context of a limited number of histori...
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| Main Authors: | , |
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| Format: | Journal |
| Published: |
2020
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| Subjects: | |
| Online Access: | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85076676427&origin=inward http://cmuir.cmu.ac.th/jspui/handle/6653943832/68327 |
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| Institution: | Chiang Mai University |
| Summary: | © 2019 Elsevier B.V. One of the important global trends in near future is to replace fossil-fuel energy with sustainable energy. The accurate predictions of the renewable energy consumption are seemingly crucial in both national and international levels. In the context of a limited number of historical data, grey prediction system of single variable is one of primary choices for such prediction. Nonetheless, this seems rather sceptical when the dynamics of a system relies on solely one variable. This paper presents a novel approach based on a modification of multivariable grey prediction model whereby the influences of exogenous variables are taken into account. Furthermore, instead of employing the least square method for parameter estimation, states and parameters in our proposed method are sequentially estimated by means of the traditional Kalman filtering. The genetic algorithm is additionally supplemented in the Kalman filter step in order to justify some unknown noise statistics. To validate the effectiveness of the proposed scheme, it is employed to estimate and predict the renewable energy consumption in Thailand along with its associated factors using the data from 1990 to 2015. Compared with the multivariable grey model using the least square method for estimation of model parameters, the results show that the hybrid approach provides a better estimation and prediction performance. |
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