Carbon pricing prediction based on wavelet transform and K-ELM optimized by bat optimization algorithm in China ETS: the case of Shanghai and Hubei carbon markets
Carbon pricing is regarded as a crucial enabler for an accelerated low-carbon energy economy transformation to achieve temperature control targets. This paper studies carbon price forecasting considering historical carbon price series as an influencing factor. A hybrid model of a kernel-based extrem...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2018-11-01
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| Series: | Carbon Management |
| Subjects: | |
| Online Access: | http://dx.doi.org/10.1080/17583004.2018.1522095 |
| _version_ | 1797678414868512768 |
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| author | Sun Wei Zhang Chongchong Sun Cuiping |
| author_facet | Sun Wei Zhang Chongchong Sun Cuiping |
| author_sort | Sun Wei |
| collection | DOAJ |
| description | Carbon pricing is regarded as a crucial enabler for an accelerated low-carbon energy economy transformation to achieve temperature control targets. This paper studies carbon price forecasting considering historical carbon price series as an influencing factor. A hybrid model of a kernel-based extreme learning machine (KELM) optimized by the bat optimization algorithm based on wavelet transform is proposed. Firstly, the wavelet transform is used to eliminate the high-frequency components of the previous day’s carbon price data to improve the accuracy of prediction. Then, the partial auto-correlation function (PACF) is applied to analyse the correlation among historical carbon prices to select the inputs for the forecasting model. Additionally, adding a kernel function improves to some extent the fitting accuracy and stability of the traditional extreme learning machine. Finally, the parameters of the KELM model are optimized by the bat optimization algorithm. Two types of carbon prices in the China ETS were used to examine the forecasting ability of the proposed hybrid methodology. The empirical results show that the proposed hybrid methodology is more robust than other comparison models for carbon price forecasting. |
| first_indexed | 2024-03-11T22:59:26Z |
| format | Article |
| id | doaj.art-3b80281e42a24b4dba440ef807d90070 |
| institution | Directory Open Access Journal |
| issn | 1758-3004 1758-3012 |
| language | English |
| last_indexed | 2024-03-11T22:59:26Z |
| publishDate | 2018-11-01 |
| publisher | Taylor & Francis Group |
| record_format | Article |
| series | Carbon Management |
| spelling | doaj.art-3b80281e42a24b4dba440ef807d900702023-09-21T15:09:05ZengTaylor & Francis GroupCarbon Management1758-30041758-30122018-11-019660561710.1080/17583004.2018.15220951522095Carbon pricing prediction based on wavelet transform and K-ELM optimized by bat optimization algorithm in China ETS: the case of Shanghai and Hubei carbon marketsSun Wei0Zhang Chongchong1Sun Cuiping2North China Electric Power UniversityNorth China Electric Power UniversityNorth China Electric Power UniversityCarbon pricing is regarded as a crucial enabler for an accelerated low-carbon energy economy transformation to achieve temperature control targets. This paper studies carbon price forecasting considering historical carbon price series as an influencing factor. A hybrid model of a kernel-based extreme learning machine (KELM) optimized by the bat optimization algorithm based on wavelet transform is proposed. Firstly, the wavelet transform is used to eliminate the high-frequency components of the previous day’s carbon price data to improve the accuracy of prediction. Then, the partial auto-correlation function (PACF) is applied to analyse the correlation among historical carbon prices to select the inputs for the forecasting model. Additionally, adding a kernel function improves to some extent the fitting accuracy and stability of the traditional extreme learning machine. Finally, the parameters of the KELM model are optimized by the bat optimization algorithm. Two types of carbon prices in the China ETS were used to examine the forecasting ability of the proposed hybrid methodology. The empirical results show that the proposed hybrid methodology is more robust than other comparison models for carbon price forecasting.http://dx.doi.org/10.1080/17583004.2018.1522095wavelet transformbat algorithmkernel-based extreme learning machinecarbon price forecasting |
| spellingShingle | Sun Wei Zhang Chongchong Sun Cuiping Carbon pricing prediction based on wavelet transform and K-ELM optimized by bat optimization algorithm in China ETS: the case of Shanghai and Hubei carbon markets Carbon Management wavelet transform bat algorithm kernel-based extreme learning machine carbon price forecasting |
| title | Carbon pricing prediction based on wavelet transform and K-ELM optimized by bat optimization algorithm in China ETS: the case of Shanghai and Hubei carbon markets |
| title_full | Carbon pricing prediction based on wavelet transform and K-ELM optimized by bat optimization algorithm in China ETS: the case of Shanghai and Hubei carbon markets |
| title_fullStr | Carbon pricing prediction based on wavelet transform and K-ELM optimized by bat optimization algorithm in China ETS: the case of Shanghai and Hubei carbon markets |
| title_full_unstemmed | Carbon pricing prediction based on wavelet transform and K-ELM optimized by bat optimization algorithm in China ETS: the case of Shanghai and Hubei carbon markets |
| title_short | Carbon pricing prediction based on wavelet transform and K-ELM optimized by bat optimization algorithm in China ETS: the case of Shanghai and Hubei carbon markets |
| title_sort | carbon pricing prediction based on wavelet transform and k elm optimized by bat optimization algorithm in china ets the case of shanghai and hubei carbon markets |
| topic | wavelet transform bat algorithm kernel-based extreme learning machine carbon price forecasting |
| url | http://dx.doi.org/10.1080/17583004.2018.1522095 |
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