Assessment Analysis and Forecasting for Security Early Warning of Energy Consumption Carbon Emissions in Hebei Province, China

Liang, Yi; Niu, Dongxiao; Wang, Haichao; Chen, Hanyu

doi:10.3390/en10030391

Cited by 11 publications

(8 citation statements)

References 33 publications

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“…From the review of the literature, this kind of study has been shown to be relevant to other past research in terms of model applications in CO 2 emission forecasting. Zhao, Zhao, and Guo [9] used GM (1,1) optimized by MFO with a rolling mechanism to forecast the electricity consumption of Inner Mongolia; Chang, Sun, and Gu [13] forecast energy CO 2 emissions using a quantum harmony search algorithm-based DMSFE combination model; Zeng, Xu, Wang, Chen, and Li [14] forecasted the allocative efficiency of carbon emission allowance financial assets in china at the provincial level in 2020; Liang, Niu, Wang, and Chen [15] did an assessment analysis and forecast for the secure early warning of energy consumption carbon emissions in Hebei Province, China; Li, Yang, and Li [30] forecast China's coal power installed capacity using a comparison of MGM, ARIMA, GM-ARIMA, and NMGM Models.…”

Section: Discussionmentioning

confidence: 99%

“…With that, they therefore provided a suggestion of which particular provinces have to cut off their CO 2 emission. Also, Liang, Niu, Wang and Chen [15] did an evaluation on the security early warning of energy consumption carbon emissions (ECCE) in Hebei Province of China. They constructed an assessment index system according to the pressure-state-response (P-S-R) model, as well as deploying the variance method and linearity weighted method in order to compute such an early warning index of ECCE.…”

Section: Literature Reviewmentioning

confidence: 99%

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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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Section: Discussionmentioning

confidence: 99%

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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“…Lastly, the solutions in FB are put in order and the best solution is updated. The filtration rate is updated and FB and W are merged (Jaddi et al, 2017; Liang et al, 2017; Taqi and Ali, 2017). When the termination condition is satisfied, this iterative process stops.…”

Section: Kamentioning

confidence: 99%

Parameter optimization of power system stabilizers via kidney-inspired algorithm

Ekinci

Demirören

Hekimoğlu

2018

Transactions of the Institute of Measurement and Control

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This article describes the application of a new population-based meta-heuristic optimization algorithm inspired by the kidney process in the human body for the tuning of power system stabilizers (PSSs) in a multi-machine power system. The tuning problem of PSS parameters is formulated as an optimization problem that aims at maximizing the damping ratio of the electromechanical modes and the kidney-inspired algorithm (KA) is used to search for the optimal parameters. The efficacy of the KA-based PSS design was successfully tested on a well-known 16-machine, 68-bus power system. The obtained results are evaluated and compared with the other results obtained by the original particle swarm optimization (PSO) and the bat algorithm (BA) methods. From the detailed eigenvalue analysis, the nonlinear simulation studies and some performance indices it has been found out that for damping oscillations, the performance of the proposed KA approach in this study is better than that obtained by other intelligent techniques (PSO and BA). Moreover, the efficiency and the superior performance of the proposed method over the other two algorithms in terms of computation time, convergence rate and solution quality are confirmed.

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“…Bu özelliği sayesinde iyi bir yakınsama performansı gösterir [11]. Yeni bir algoritma olmasından ötürü, birkaç mühendislik uygulaması [13][14][15][16][17]…”

Section: Introductionunclassified

Böbrek-ilhamlı Algoritma ile Ayarlanan PID Kontrolör Kullanarak DC Motor Hız Kontrolü

Hekimoğlu

2019

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi

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Öz DC motor hız kontrol sistemlerinin birçok endüstriyel uygulamasında, çoğunlukla oransal-integral-türevsel (PID) kontrolörler kullanılmaktadır. Bu çalışmada, DC motor hız kontrolünün en uygun PID kontrolör parametreleri, yani oransal (Kp), integral (Ki) ve türev (Kd) kazançları, etkin ve hızlı bir ayar yöntemi olan böbrek-ilhamlı algoritma (Kidney-inspired Algorithm-KA) ile belirlenmektedir. Kontrol sisteminin tasarımında, kontrolör parametrelerinin KA tarafından optimize edilebilmesi için zaman bölgesi tabanlı bir performans ölçütü kullanılmıştır. Bu amaç fonksiyonu ile önerilen yaklaşımın performansını değerlendirmek için son yıllarda yayımlanmış gri kurt optimizasyon (Grey Wolf Optimization-GWO) algoritması, istilacı ot optimizasyon (Invasive Weed Optimization-IWO) algoritması ve stokastik fraktal arama (Stochastic Fractal Search-SFS) algoritması gibi diğer modern sezgisel-üstü optimizasyon algoritmalarına dayalı yaklaşımlarla karşılaştırmalar yapılmıştır. Simülasyon sonuçlarından, DC motorun hız kontrolü için tasarlanan KA tabanlı PID (KA-PID) kontrolörün kapalı çevrim sisteminin aşım, yerleşme zamanı ve yükselme zamanı gibi sistem karakteristiklerini en az iterasyonla önemli ölçüde iyileştirdiği görülmüştür. KA-PID kontrolör yaklaşımının gürbüzlük analizi de, DC motor parametrelerindeki değişikliklerle gerçekleştirilmiştir.

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Assessment Analysis and Forecasting for Security Early Warning of Energy Consumption Carbon Emissions in Hebei Province, China

Cited by 11 publications

References 33 publications

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

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

Parameter optimization of power system stabilizers via kidney-inspired algorithm

Böbrek-ilhamlı Algoritma ile Ayarlanan PID Kontrolör Kullanarak DC Motor Hız Kontrolü

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