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Zhao Y, Zhao H, Li B, Wu B, Guo S. Point and interval forecasting for carbon trading price: a case of 8 carbon trading markets in China. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:49075-49096. [PMID: 36763267 DOI: 10.1007/s11356-023-25151-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Accepted: 01/02/2023] [Indexed: 04/16/2023]
Abstract
Carbon trading price (CTP) prediction accuracy is critical for both market participants and policymakers. As things stand, most previous studies have only focused on one or a few carbon trading markets, implying that the models' universality is insufficient to be validated. By employing a case study of all carbon trading markets in China, this study proposes a hybrid point and interval CTP forecasting model. First, the Pearson correlation method is used to identify the key influencing factors of CTP. The original CTP data is then decomposed into multiple series using complete ensemble empirical mode decomposition with adaptive noise. Following that, the sample entropy method is used to reconstruct the series to reduce computational time and avoid overdecomposition. Following that, a long short-term memory method optimized by the Adam algorithm is established to achieve the point forecasting of CTP. Finally, the kernel density estimation method is used to predict CTP intervals. On the one hand, the results demonstrate the proposed model's validity and superiority. The interval prediction model, on the other hand, reflects the uncertainty of market participants' behavior, which is more practical in the operation of carbon trading markets.
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Affiliation(s)
- Yihang Zhao
- School of Economics and Management, North China Electric Power University, Beijing, 102206, China
- Beijing Key Laboratory of New Energy and Low-Carbon Development, North China Electric Power University, Changping, Beijing, 102206, China
| | - Huiru Zhao
- School of Economics and Management, North China Electric Power University, Beijing, 102206, China
- Beijing Key Laboratory of New Energy and Low-Carbon Development, North China Electric Power University, Changping, Beijing, 102206, China
| | - Bingkang Li
- Department of Economic Management, North China Electric Power University, Baoding, 071003, Hebei Province, China
| | - Boxiang Wu
- State Grid Chaoyang Electric Power Company, Beijing, 100031, China
| | - Sen Guo
- School of Economics and Management, North China Electric Power University, Beijing, 102206, China.
- Beijing Key Laboratory of New Energy and Low-Carbon Development, North China Electric Power University, Changping, Beijing, 102206, China.
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Xu H, Chang Y, Zhao Y, Wang F. A novel hybrid wind speed interval prediction model based on mode decomposition and gated recursive neural network. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:87097-87113. [PMID: 35804229 DOI: 10.1007/s11356-022-21904-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 07/04/2022] [Indexed: 06/15/2023]
Abstract
Wind energy has become one of the most efficient renewable energy sources. However, the wind has the characteristics of intermittence and uncontrollability, so it is challenging to predict wind speed accurately. Considering the shortcomings of traditional wind power point predictions, a new hybrid model comprised three main modules used for data preprocessing, deterministic point prediction, and interval prediction is proposed to predict the wind speed interval. The first module, the data preprocessing module, uses variational mode decomposition (VMD), sample entropy (SE), and singular spectrum analysis (SSA) to extract the different frequency components of the initial wind speed series. The second module, the deterministic point prediction module, uses extreme learning machines (ELM), and a gated recursive unit (GRU) model to perform point prediction on the wind speed series. The third module, the interval prediction module, uses the nonparametric kernel density estimation method to construct the upper and lower bounds of the wind speed interval. In addition, the final wind speed prediction interval is obtained by integrating the prediction results of multiple interval prediction results to improve the robustness and generalization of the wind speed interval prediction. Finally, the effectiveness of the prediction performance of the proposed hybrid model is verified based on the data of two actual wind farms. The experimental results show that the proposed hybrid model can obtain the appropriate wind speed interval with high confidence and quality with different confidence levels of 95%, 90%, and 85%.
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Affiliation(s)
- Haiyan Xu
- College of Information Science and Engineering, Northeastern University, Shenyang, 110819, China
| | - Yuqing Chang
- College of Information Science and Engineering, Northeastern University, Shenyang, 110819, China.
| | - Yong Zhao
- College of Resources and Civil Engineering, Northeastern University, Shenyang, 110819, China
| | - Fuli Wang
- College of Information Science and Engineering, Northeastern University, Shenyang, 110819, China
- State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang, 110819, China
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Yang W, Hao M, Hao Y. Innovative ensemble system based on mixed frequency modeling for wind speed point and interval forecasting. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2022.11.145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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Hajirahimi Z, Khashei M. Hybridization of hybrid structures for time series forecasting: a review. Artif Intell Rev 2022. [DOI: 10.1007/s10462-022-10199-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
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Wind Speed Forecasting Using Attention-Based Causal Convolutional Network and Wind Energy Conversion. ENERGIES 2022. [DOI: 10.3390/en15082881] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
As one of the effective renewable energy sources, wind energy has received attention because it is sustainable energy. Accurate wind speed forecasting can pave the way to the goal of sustainable development. However, current methods ignore the temporal characteristics of wind speed, which leads to inaccurate forecasting results. In this paper, we propose a novel SSA-CCN-ATT model to forecast the wind speed. Specifically, singular spectrum analysis (SSA) is first applied to decompose the original wind speed into several sub-signals. Secondly, we build a new deep learning CNN-ATT model that combines causal convolutional network (CNN) and attention mechanism (ATT). The causal convolutional network is used to extract the information in the wind speed time series. After that, the attention mechanism is employed to focus on the important information. Finally, a fully connected neural network layer is employed to get wind speed forecasting results. Three experiments on four datasets show that the proposed model performs better than other comparative models. Compared with different comparative models, the maximum improvement percentages of MAPE reaches up to 26.279%, and the minimum is 5.7210%. Moreover, a wind energy conversion curve was established by simulating historical wind speed data.
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Yang Y, Zhou H, Wu J, Ding Z, Wang YG. Robustified extreme learning machine regression with applications in outlier-blended wind-speed forecasting. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.108814] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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Application of a Novel Optimized Fractional Grey Holt-Winters Model in Energy Forecasting. SUSTAINABILITY 2022. [DOI: 10.3390/su14053118] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
It is of great significance to be able to accurately predict the time series of energy data. In this paper, based on the seasonal and nonlinear characteristics of monthly and quarterly energy time series, a new optimized fractional grey Holt–Winters model (NOFGHW) is proposed to improve the identification of the model by integrating the processing methods of the two characteristics. The model consists of three parts. Firstly, a new fractional periodic accumulation operator is proposed, which preserves the periodic fluctuation of data after accumulation. Secondly, the new operator is introduced into the Holt–Winters model to describe the seasonality of the sequence. Finally, the LBFGS algorithm is used to optimize the parameters of the model, which can deal with nonlinear characteristics in the sequence. Furthermore, in order to verify the superiority of the model in energy prediction, the new model is applied to two cases with different seasonal, different cycle, and different energy types, namely monthly crude oil production and quarterly industrial electricity consumption. The experimental results show that the new model can be used to predict monthly and quarterly energy time series, which is better than the OGHW, SNGBM, SARIMA, LSSVR, and BPNN models. Based on this, the new model demonstrates reliability in energy prediction.
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Goh HH, He R, Zhang D, Liu H, Dai W, Lim CS, Kurniawan TA, Teo KTK, Goh KC. A multimodal approach to chaotic renewable energy prediction using meteorological and historical information. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.108487] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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Metro passenger flow forecasting though multi-source time-series fusion: An ensemble deep learning approach. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.108644] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
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Wang J, Zhang L, Wang C, Liu Z. A regional pretraining-classification-selection forecasting system for wind power point forecasting and interval forecasting. Appl Soft Comput 2021. [DOI: 10.1016/j.asoc.2021.107941] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Li Q, Wang J, Zhang H. A wind speed interval forecasting system based on constrained lower upper bound estimation and parallel feature selection. Knowl Based Syst 2021. [DOI: 10.1016/j.knosys.2021.107435] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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