Energy-Related CO2 Emissions Forecasting Using an Improved LSSVM Model Optimized by Whale Optimization Algorithm
Abstract
:1. Introduction
- A novel LSSVM-based CO2 emissions forecasting model is proposed, of which the significant parameters in LSSVM are optimized by the meta-heuristic optimization algorithm WOA. It is verified that this hybrid intelligence forecasting method has the superiority in the forecasting precision of CO2 emissions through comparing with LSSVM model optimized by FOA, LSSVM without parameters optimization and OLS (ordinary least square). This paper extends the application domains of intelligence LSSVM forecasting technique.
- GDP, energy consumption and population, which are considered as the main driving forces of CO2 emissions, are imported into the proposed WOA-LSSVM approach. Therefore, the proposed CO2 emissions prediction model in this paper not only employs the intelligence forecasting technique, but also takes the social economic driving factors of CO2 emissions into consideration, which encapsulates the complicated nonlinear relationships of CO2 emissions with its main driving forces to some extent.
2. The Methodology of WOA-LSSVM Forecasting Model
2.1. Basic Methodology of LSSVM Model
2.2. Basic Theory of WOA
2.3. Basic Principle of WOA-LSSVM Model for CO2 Emissions Forecasting
3. Empirical Simulation and Analysis
3.1. Data Sources and Preprocessing of Data Sample
3.2. Optimal Parameters Determination for LSSVM Method
4. Forecasting Performance Evaluation
4.1. Selection of Comparison Models and Forecasting Performance Evaluation Index
4.2. Comparisons of Prediction Performance for Different Prediction Methods
5. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Driving Forces | GDP | Energy Consumption | Population | Total Export | Economic Structure * |
---|---|---|---|---|---|
Correlation degree | 0.6577 | 0.6727 | 0.6798 | 0.6377 | 0.6015 |
Year | Forecasting Value (Unit: Mt) | Actual Value (Unit: Mt) | The Gap * (Unit: Mt) |
---|---|---|---|
2011 | 9209.67 | 9206.12 | −3.55 |
2012 | 9378.71 | 9415.42 | 36.71 |
2013 | 9611.85 | 9674.22 | 62.37 |
2014 | 9753.36 | 9761.08 | 7.71 |
Model | FOA-LSSVM | LSSVM | WOA-LSSVM |
---|---|---|---|
0.2496 | 0.8 | 2.0684 | |
C | 5.6947 | 20 | 93.2203 |
Model | WOA-LSSVM | FOA-LSSVM | LSSVM | OLS |
---|---|---|---|---|
MAPE (%) | 0.29 | 0.62 | 0.45 | 0.67 |
RMSE (Mt) | 36.43 | 84.34 | 54.20 | 70.36 |
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Zhao, H.; Guo, S.; Zhao, H. Energy-Related CO2 Emissions Forecasting Using an Improved LSSVM Model Optimized by Whale Optimization Algorithm. Energies 2017, 10, 874. https://doi.org/10.3390/en10070874
Zhao H, Guo S, Zhao H. Energy-Related CO2 Emissions Forecasting Using an Improved LSSVM Model Optimized by Whale Optimization Algorithm. Energies. 2017; 10(7):874. https://doi.org/10.3390/en10070874
Chicago/Turabian StyleZhao, Haoran, Sen Guo, and Huiru Zhao. 2017. "Energy-Related CO2 Emissions Forecasting Using an Improved LSSVM Model Optimized by Whale Optimization Algorithm" Energies 10, no. 7: 874. https://doi.org/10.3390/en10070874