International carbon market price forecasting using an integration model based on SVR

Jiang, Linbo; Wu, Peng

doi:10.2991/emeeit-15.2015.61

Cited by 13 publications

(6 citation statements)

References 19 publications

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“…Energy and emissions average prices are forecasted starting from their past trends through ML and, in particular, Feed Forward Neural Networks (FFNN). The accuracy of the models is in line with the status-quo of the energy and emission media price literature predictions [36][37][38][39][40]. Lower performances were obtained for the electricity price prediction.…”

Section: Energy and Emission Media Marketsupporting

confidence: 70%

Hydrogen role in the valorization of integrated steelworks process off-gases through methane and methanol syntheses

Matino

Dettori

Zaccara

et al. 2021

Matériaux & Techniques

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The valorization of integrated steelworks process off-gases as feedstock for synthesizing methane and methanol is in line with European Green Deal challenges. However, this target can be generally achieved only through process off-gases enrichment with hydrogen and use of cutting-edge syntheses reactors coupled to advanced control systems. These aspects are addressed in the RFCS project i3upgrade and the central role of hydrogen was evident from the first stages of the project. First stationary scenario analyses showed that the required hydrogen amount is significant and existing renewable hydrogen production technologies are not ready to satisfy the demand in an economic perspective. The poor availability of low-cost green hydrogen as one of the main barriers for producing methane and methanol from process off-gases is further highlighted in the application of an ad-hoc developed dispatch controller for managing hydrogen intensified syntheses in integrated steelworks. The dispatch controller considers both economic and environmental impacts in the cost function and, although significant environmental benefits are obtainable by exploiting process off-gases in the syntheses, the current hydrogen costs highly affect the dispatch controller decisions. This underlines the need for big scale green hydrogen production processes and dedicated green markets for hydrogen-intensive industries, which would ensure easy access to this fundamental gas paving the way for a C-lean and more sustainable steel production.

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Section: Energy and Emission Media Marketsupporting

confidence: 70%

Hydrogen role in the valorization of integrated steelworks process off-gases through methane and methanol syntheses

Matino

Dettori

Zaccara

et al. 2021

Matériaux & Techniques

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“…The comparison with forecasting results in [46,47,49,50,52] shows that the obtained testing RMSE of 0.33 EUR/t CO2 of the network configuration in model C6 (corresponding to a testing NRMSE of 0.023) is in line with the status-quo of CO 2 price prediction (Figure 13). Atsalakis [49], for example, states an RMSE of 0.27 EUR/t CO2 , Zhu [48] an RMSE of 0.3 EUR/t CO2 , and Fan et al [52] an RMSE of 0.27 EUR/t CO2 .…”

Section: Comparison With the Literaturesupporting

confidence: 52%

“…This is also reflected by the choice of the combination of FFNN and regression models in hybrid modeling approaches. For example, Han et al [38] use a FFNN as part of a hybrid model with a regression approach and Jiang and Wu [47] use a hybrid model consisting of an FFNN and an ARIMA model, among others.…”

Section: Prediction Of Co 2 Prices Using Artificial Neural Networkmentioning

confidence: 99%

Short-Term Natural Gas and Carbon Price Forecasting Using Artificial Neural Networks

Böhm,

Kolb,

Plankenbühler

et al. 2023

Energies

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Methods of computational intelligence show a high potential for short-term price forecasting of the energy market as they offer the possibility to cope with the complexity, multi-parameter dependency, and non-linearity of pricing mechanisms. While there is a large number of publications applying neural networks to the prediction of electricity prices, the analysis of natural gas and carbon prices remains scarce. Identifying a best practice from the literature, this study presents an iterative approach to optimize both the input values and network configuration of neural networks. We apply the approach to the natural gas and carbon market, sequentially testing autoregressive and exogenous explanatory variables as well as different neural network architectures. We subsequently discuss the influence of architectural properties, input parameters, data preparation, and the models’ resilience to singular events. Results show that the selection of appropriate lags of gas and carbon prices to account for autoregressive properties of the respective time series leads to a high degree of forecasting accuracy. Additionally, including ambient temperature data can slightly reduce errors of natural gas price forecasting whereas carbon price predictions benefit from electricity prices as a further explanatory input. The best configurations presented in this contribution achieve a root mean square error (RMSE) of 0.64 EUR/MWh (natural gas prices) corresponding to a normalized RMSE of 0.037 and 0.33 EUR/tCO2 (carbon prices) corresponding to a normalized RMSE of 0.023.

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“…There is increasing evidence to show that machine learning methods outperform other models for nonlinear time series prediction [9]. Compared with the traditional model, the machine learning-based predictive models, such as Support Vector Machine (SVM) [10], Artificial Neural Network (ANN) [11], Convolutional Neural Network (CNN), and Long Short-Term Memory network (LSTM) [12], have greater forecasting accuracy when applied to carbon market prediction. The shortcomings of these models are embodied in the results being highly dependent on parameter tuning, which can be blamed for overfitting the nonlinearity of data.…”

Section: Progress In Carbon Market Predictionmentioning

confidence: 99%

Forecasting the Return of Carbon Price in the Chinese Market Based on an Improved Stacking Ensemble Algorithm

Siddik

2023

Energies

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Recently, carbon price forecasting has become critical for financial markets and environmental protection. Due to their dynamic, nonlinear, and high noise characteristics, predicting carbon prices is difficult. Machine learning forecasting often uses stacked ensemble algorithms. As a result, common stacking has many limitations when applied to time series data, as its cross-validation process disrupts the temporal sequentiality of the data. Using a double sliding window scheme, we proposed an improved stacking ensemble algorithm that avoided overfitting risks and maintained temporal sequentiality. We replaced cross-validation with walk-forward validation. Our empirical experiment involved the design of two dynamic forecasting frameworks utilizing the improved algorithm. This incorporated forecasting models from different domains as base learners. We used three popular machine learning models as the meta-model to integrate the predictions of each base learner, further narrowing the gap between the final predictions and the observations. The empirical part of this study used the return of carbon prices from the Shenzhen carbon market in China as the prediction target. This verified the enhanced accuracy of the modified stacking algorithm through the use of five statistical metrics and the model confidence set (MCS). Furthermore, we constructed a portfolio to examine the practical usefulness of the improved stacking algorithm. Empirical results showed that the improved stacking algorithm could significantly and robustly improve model prediction accuracy. Support vector machines (SVR) aggregated results better than the other two meta-models (Random forest and XGBoost) in the aggregation step. In different volatility states, the modified stacking algorithm performed differently. We also found that aggressive investment strategies can help investors achieve higher investment returns with carbon option assets.

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International carbon market price forecasting using an integration model based on SVR

Cited by 13 publications

References 19 publications

Hydrogen role in the valorization of integrated steelworks process off-gases through methane and methanol syntheses

Hydrogen role in the valorization of integrated steelworks process off-gases through methane and methanol syntheses

Short-Term Natural Gas and Carbon Price Forecasting Using Artificial Neural Networks

Forecasting the Return of Carbon Price in the Chinese Market Based on an Improved Stacking Ensemble Algorithm

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