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, 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).
Highlights
The world energy demand increased in the two past decades and even predictions implying the growing trends for the decades [1,2,3,4]
Refer to International Energy Agency’s (IEA) 2016 report, fossil fuels in the form of liquid fuels, natural gas, and coal contain more than 80% of the world energy consumption [5]
This paper is aimed to develop an intelligent learning-based prediction model which is equipped with data mining (DM) techniques to purify and the setup input vector
Summary
The world energy demand increased in the two past decades and even predictions implying the growing trends for the decades [1,2,3,4]. Refer to International Energy Agency’s (IEA) 2016 report, fossil fuels in the form of liquid fuels, natural gas, and coal contain more than 80% of the world energy consumption [5]. Emergent ecological concerns and rethinking of a more peaceful future (sustainable development goals) attracted attention toward climate change challenges (such as greenhouse gases emissions and global warming) [9]. The two non-aligned objectives, on one hand, development and increasing needs for energy supply and on the other hand global environmental concerns, attracted researchers to study energy systems and develop different plausible future perspectives
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