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

Reza Hafezi, Amir Naser Akhavan, Mazdak Zamani, Saeed Pakseresht and Shahaboddin Shamshirband
Additional contact information
Reza Hafezi: Futures Studies Research Group, National Research Institute for Science Policy (NRISP), Tehran 15916-34311, Iran
Amir Naser Akhavan: Technology Foresight Group, Department of Management, Science and Technology, Amirkabir University of Technology (Tehran Polytechnic), Tehran 159163-4311, Iran
Mazdak Zamani: School of Arts and Sciences, Felician University, 262 South Main Street, Lodi, NJ 07644, USA
Saeed Pakseresht: Director of Research and Technology, National Iranian Gas Company (NIGC), Tehran 15875-4533, Iran
Shahaboddin Shamshirband: Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City 758307, Vietnam

Energies, 2019, vol. 12, issue 21, 1-22

Abstract: 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, CO 2 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 R 2 , 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 R 2 indicator (=0.9864) than the GNn (=0.9679).

Keywords: natural gas demands; prediction; energy market; genetic algorithm; artificial neural network; data mining (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

Downloads: (external link)
https://www.mdpi.com/1996-1073/12/21/4124/pdf (application/pdf)
https://www.mdpi.com/1996-1073/12/21/4124/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:12:y:2019:i:21:p:4124-:d:281381

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().