Analysis and Modeling for China’s Electricity Demand Forecasting Using a Hybrid Method Based on Multiple Regression and Extreme Learning Machine: A View from Carbon Emission

Liang, Yi; Niu, Dongxiao; Cao, Ye; Hong, Wei‐Chiang

doi:10.3390/en9110941

Cited by 23 publications

(16 citation statements)

References 26 publications

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“…Later, the finding showed a growing trend of the share. Liang, Niu, Cao and Hong [18] conducted an analysis and constructed a model to forecast China's electricity demand, in terms of carbon emissions. They began the study with an integration of the Grey relation degree (GRD) and induced ordered weighted harmonic averaging operator (IOWHA) in order to construct the optimal hybrid forecasting model, based on multiple regression and an extreme learning machine.…”

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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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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“…Hybrid models which have been proposed previously in the literature are concentrated on forecasting electricity demand time series based on time series of different nature such as economic (GDP, electricity price), demographic (total population) and in some cases the average temperature, CO2 emission etc. [24][25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

A Study on the Efficiency of Hybrid Models in Forecasting Precipitations and Water Inflow Albania Case Study

Gjika¹,

Ferrja²,

Kamberi³

2019

Adv. sci. technol. eng. syst. j.

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Climatic changes have a significant impact on many real life processes. Climacteric position of Albania makes precipitations and water inflows in HPP the main variables influencing the amount of electric energy produced in the country. Taking into account their volatility it has considerably increased the need of using hybrid models to improve the quality of predictions. After a detailed analysis of the time series components, we develop a group of hybrid models and propose modifications to increase the accuracy in prediction. Among the contributions of this work is the challenge to choose between hybrid models presented earlier in literature and the modified version according to the nature of data. The final decision on the most accurate model is made based on many goodness of fit test. This study suggest that an accurate selection of the forecasting techniques increases significantly the quality of forecast on precipitations and water inflow data.

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“…The model was validated on the foundation of actual data. Reference [9] put up with an ELM-based approach for medium and long term electricity demand prediction with the target of a low carbon economy. Reference [10] evaluated the effectiveness of a BPNN and a RBFNN for engineering cost prediction.…”

Section: Introductionmentioning

confidence: 99%

Cost Forecasting Model of Transformer Substation Projects Based on Data Inconsistency Rate and Modified Deep Convolutional Neural Network

et al. 2019

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Precise and steady substation project cost forecasting is of great significance to guarantee the economic construction and valid administration of electric power engineering. This paper develops a novel hybrid approach for cost forecasting based on a data inconsistency rate (DIR), a modified fruit fly optimization algorithm (MFOA) and a deep convolutional neural network (DCNN). Firstly, the DIR integrated with the MFOA is adopted for input feature selection. Simultaneously, the MFOA is utilized to realize parameter optimization in the DCNN. The effectiveness of the MFOA–DIR–DCNN has been validated by a case study that selects 128 substation projects in different regions for training and testing. The modeling results demonstrate that this established approach is better than the contrast methods with regard to forecasting accuracy and robustness. Thus, the developed technique is feasible for the cost prediction of substation projects in various voltage levels.

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Analysis and Modeling for China’s Electricity Demand Forecasting Using a Hybrid Method Based on Multiple Regression and Extreme Learning Machine: A View from Carbon Emission

Cited by 23 publications

References 26 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

A Study on the Efficiency of Hybrid Models in Forecasting Precipitations and Water Inflow Albania Case Study

Cost Forecasting Model of Transformer Substation Projects Based on Data Inconsistency Rate and Modified Deep Convolutional Neural Network

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