Next Article in Journal
Conceptualizing Household Energy Metabolism: A Methodological Contribution
Previous Article in Journal
Determinants of the Long-Term Correlation between Crude Oil and Stock Markets
Open AccessArticle

Developing a Data Mining Based Model to Extract Predictor Factors in Energy Systems: Application of Global Natural Gas Demand

1
Futures Studies Research Group, National Research Institute for Science Policy (NRISP), Tehran 15916-34311, Iran
2
Technology Foresight Group, Department of Management, Science and Technology, Amirkabir University of Technology (Tehran Polytechnic), Tehran 159163-4311, Iran
3
School of Arts and Sciences, Felician University, 262 South Main Street, Lodi, NJ 07644, USA
4
Director of Research and Technology, National Iranian Gas Company (NIGC), Tehran 15875-4533, Iran
5
Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City 758307, Vietnam
6
Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City 758307, Vietnam
*
Author to whom correspondence should be addressed.
Energies 2019, 12(21), 4124; https://doi.org/10.3390/en12214124
Received: 22 June 2019 / Revised: 6 August 2019 / Accepted: 31 August 2019 / Published: 29 October 2019
Recently, the natural gas (NG) global market attracted much attention as it is cleaner than oil and, simultaneously in most regions, is cheaper than renewable energy sources. However, price fluctuations, environmental concerns, technological development, emerging unconventional resources, energy security challenges, and shipment are some of the forces made the NG market more dynamic and complex. From a policy-making perspective, it is vital to uncover demand-side future trends. This paper proposed an intelligent forecasting model to forecast NG global demand, however investigating a multi-dimensional purified input vector. The model starts with a data mining (DM) step to purify input features, identify the best time lags, and pre-processing selected input vector. Then a hybrid artificial neural network (ANN) which is equipped with genetic optimizer is applied to set up ANN’s characteristics. Among 13 available input features, six features (e.g., Alternative and Nuclear Energy, CO2 Emissions, GDP per Capita, Urban Population, Natural Gas Production, Oil Consumption) were selected as the most relevant feature via the DM step. Then, the hybrid learning prediction model is designed to extrapolate the consumption of future trends. The proposed model overcomes competitive models refer to different five error based evaluation statistics consist of R2, MAE, MAPE, MBE, and RMSE. In addition, as the model proposed the best input feature set, results compared to the model which used the raw input set, with no DM purification process. The comparison showed that DmGNn overcame dramatically a simple GNn. Also, a looser prediction model, such as a generalized neural network with purified input features obtained a larger R2 indicator (=0.9864) than the GNn (=0.9679). View Full-Text
Keywords: natural gas demands; prediction; energy market; genetic algorithm; artificial neural network; data mining natural gas demands; prediction; energy market; genetic algorithm; artificial neural network; data mining
Show Figures

Figure 1

MDPI and ACS Style

Hafezi, R.; Akhavan, A.N.; Zamani, M.; Pakseresht, S.; Shamshirband, S. Developing a Data Mining Based Model to Extract Predictor Factors in Energy Systems: Application of Global Natural Gas Demand. Energies 2019, 12, 4124. https://doi.org/10.3390/en12214124

AMA Style

Hafezi R, Akhavan AN, Zamani M, Pakseresht S, Shamshirband S. Developing a Data Mining Based Model to Extract Predictor Factors in Energy Systems: Application of Global Natural Gas Demand. Energies. 2019; 12(21):4124. https://doi.org/10.3390/en12214124

Chicago/Turabian Style

Hafezi, Reza; Akhavan, Amir N.; Zamani, Mazdak; Pakseresht, Saeed; Shamshirband, Shahaboddin. 2019. "Developing a Data Mining Based Model to Extract Predictor Factors in Energy Systems: Application of Global Natural Gas Demand" Energies 12, no. 21: 4124. https://doi.org/10.3390/en12214124

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop