Preprint Article Version 1 This version is not peer-reviewed

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

Version 1 : Received: 19 June 2019 / Approved: 20 June 2019 / Online: 20 June 2019 (15:58:25 CEST)

A peer-reviewed article of this Preprint also exists.

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. 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.

Journal reference: Energies 2019, 12, 4124
DOI: 10.3390/en12214124

Abstract

Recently natural gas (NG) global market attracted much attention in case 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 that 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 to pre-process selected input vector. Then a hybrid artificial neural network (ANN) which 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) selected as the most critical feature via the DM step. Then, the hybrid prediction model is designed to extrapolate the consumption of future trends. The proposed model overcomes competitive models refer to different error based evaluation statistics. Besides, 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.

Subject Areas

Natural gas demands; Prediction; Energy market; Genetic algorithm; Artificial neural network; Data mining.

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