Forecasting of Energy-Related CO2 Emissions in China Based on GM(1,1) and Least Squares Support Vector Machine Optimized by Modified Shuffled Frog Leaping Algorithm for Sustainability

Dai, Shuyu; Niu, Dongxiao; Han, Yaru

doi:10.3390/su10040958

Cited by 33 publications

(21 citation statements)

References 36 publications

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“…China's cumulative installed PV capacity is 131 GW, accounting for 32.57% of the world's total cumulative installed PV capacity and ranked first [7]. China is the largest CO 2 emitting country in the world, which accounts for 28% of the CO 2 emissions globally [8]. Therefore, the reduction of carbon dioxide emissions from China's PV industry has a direct impact on global trends.…”

Section: Introductionmentioning

confidence: 99%

Observational Study on the Impact of Large-Scale Photovoltaic Development in Deserts on Local Air Temperature and Humidity

Yue

Zhou

et al. 2020

Sustainability

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As an important form of clean energy, photovoltaic (PV) power generation is entering a rapid development phase. Qinghai, China is located on the Qinghai-Tibet Plateau. It has sufficient sunlight and rich heat and light resources, includes a large area of the Gobi Desert, and has become China’s largest base for PV power generation. However, large-scale PV development in deserts changes the local surface energy distribution and impacts local microclimates. This study considered the Gonghe PV Power Plant in Qinghai as an example. Three monitoring stations were set up in the PV power plant, transition, and reference areas, and the influence of large-scale PV developments on the local air temperature and humidity was studied based on long-term, multi-point field observation data. The results showed that the overall daytime air temperature in the PV power plant had changed slightly (increased and decreased), while the night-time temperature dropped significantly. Specifically, in spring and summer, the daytime temperature increased slightly, with a maximum increase of 0.34 °C; in autumn and winter, the daytime temperature decreased slightly, with a maximum decrease of 0.26 °C; in all seasons, the night-time temperature decreased, with a maximum decrease of 1.82 °C during the winter night. The relative humidity in the PV power plant generally increased; except for a slight decrease in summer, the daytime and night-time relative humidity in spring, autumn, and winter always increased. The humidification in winter was the most significant, with increases of 5.00% and 4.76% for the transition and reference areas, respectively. The diurnal air temperature and relative humidity ranges in the PV power plant were greater than those outside the PV power plant. The results obtained in this field observation study could serve as a basis for quantitative evaluation of the microclimate effects of large-scale PV development in deserts and provide technical support for guiding the future planning and development of the PV industry.

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Section: Introductionmentioning

confidence: 99%

Observational Study on the Impact of Large-Scale Photovoltaic Development in Deserts on Local Air Temperature and Humidity

Yue

Zhou

et al. 2020

Sustainability

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“…Among the single forecasting methods, there is the elastic coefficient method [10,11], regression method [12,13], system dynamics method [14,15], grey forecasting method [16,17], neural network method [18,19], support vector machine [20,21], etc. Damrongkulkamjorn et al [22] introduced a new method combining ARIMA (autoregressive integrated moving average) with classical decomposition techniques.…”

Section: Relevant Methods For Energy Demand Forecastingmentioning

confidence: 99%

Forecasting of Coal Demand in China Based on Support Vector Machine Optimized by the Improved Gravitational Search Algorithm

2019

Energies

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The main target of the energy revolution in the new period is coal, but the proportion of coal in primary energy consumption will gradually decrease. As coal is a major producer and consumer of energy, analyzing the trend of coal demand in the future is of great significance for formulating the policy of coal development planning and driving the revolution of energy sources in China. In order to predict coal demand scientifically and accurately, firstly, the index system of influencing factors of coal demand was constructed, and the grey relational analysis method was used to select key indicators as input variables of the model. Then, the kernel function of SVM (support vector machine) was optimized by taking advantage of the fast convergence speed of GSA (gravitational search algorithm), and the memory function and boundary mutation strategy of PSO (particle swarm optimization) were introduced to improve the gravitational search algorithm, and the improved GSA (IGSA)–SVM prediction model was obtained. After that, the effectiveness of IGSA–SVM in predicting coal demand was further proven through empirical and comparative analysis. Finally, IGSA–SVM was used to forecast China’s coal demand in 2018–2025. According to the forecasting results, relevant suggestions about coal supply, consumption, and transformation are put forward, providing scientific basis for formulating an energy development strategy.

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“…This could guide policy makers to develop energy-saving and emission-reduction policies. Consequently, Dai, Niu and Han [33] proposed to adapt the MSFLA-LSSVM model for CO 2 emissions prediction in China from 2018 to 2025. They concluded that China's CO 2 emissions would exhibit slow growth trend for the next few years.…”

Section: Literature Reviewmentioning

confidence: 99%

Forecasting Energy-Related Carbon Dioxide Emissions in Thailand’s Construction Sector by Enriching the LS-ARIMAXi-ECM Model

Sutthichaimethee

Kubaha

2018

Sustainability

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The Thailand Development Policy focuses on the simultaneous growth of the economy, society, and environment. Long-term goals have been set to improve economic and social well-being. At the same time, these aim to reduce the emission of CO 2 in the future, especially in the construction sector, which is deemed important in terms of national development and is a high generator of greenhouse gas. In order to achieve national sustainable development, policy formulation and planning is becoming necessary and requires a tool to undertake such a formulation. The tool is none other than the forecasting of CO 2 emissions in long-term energy consumption to produce a complete and accurate formulation. This research aims to study and forecast energy-related carbon dioxide emissions in Thailand's construction sector by applying a model incorporating the longand short-term auto-regressive (AR), integrated (I), moving average (MA) with exogenous variables (Xi) and the error correction mechanism (LS-ARIMAXi-ECM) model. This model is established and attempts to fill the gaps left by the old models. In fact, the model is constructed based on factors that are causal and influential for changes in CO 2 emissions. Both independent variables and dependent variables must be stationary at the same level. In addition, the LS-ARIMAXi-ECM model deploys a co-integration analysis and error correction mechanism (ECM) in its modeling. The study's findings reveal that the LS-ARIMAXi (2, 1, 1, X t−1 )-ECM model is a forecasting model with an appropriate time period (t − i), as justified by the Q-test statistic and is not a spurious model. Therefore, it is used to forecast CO 2 emissions for the next 20 years (2019 to 2038). From the study, the results show that CO 2 emissions in the construction sector will increase by 37.88% or 61.09 Mt CO 2 Eq. in 2038. Also, the LS-ARIMAXi (2, 1, 1, X t−1 )-ECM model has been evaluated regarding its performance, and it produces a mean absolute percentage error (MAPE) of 1.01% and root mean square error (RMSE) of 0.93% as compared to the old models. Overall, the results indicate that determining future national sustainable development policies requires an appropriate forecasting model, which is built upon causal and contextual factors according to relevant sectors, to serve as an important tool for future sustainable planning.

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Forecasting of Energy-Related CO2 Emissions in China Based on GM(1,1) and Least Squares Support Vector Machine Optimized by Modified Shuffled Frog Leaping Algorithm for Sustainability

Cited by 33 publications

References 36 publications

Observational Study on the Impact of Large-Scale Photovoltaic Development in Deserts on Local Air Temperature and Humidity

Observational Study on the Impact of Large-Scale Photovoltaic Development in Deserts on Local Air Temperature and Humidity

Forecasting of Coal Demand in China Based on Support Vector Machine Optimized by the Improved Gravitational Search Algorithm

Forecasting Energy-Related Carbon Dioxide Emissions in Thailand’s Construction Sector by Enriching the LS-ARIMAXi-ECM Model

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