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

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).

Forecasting Spare Parts Demand of Military Aircraft: Comparisons of Data Mining Techniques and Managerial Features from the Case of South Korea

“…Data mining has emerged as an alternative tool for modeling and forecasting due to its ability to capture the non-linearity in the data. While the shortcoming of data mining is a large amount of training data [14], with the advent of big data era, data mining has recently been widely used for demand forecasting in the various fields, where data can be collected easily, such as energy [10,15], tourism [16,17], transportation [18][19][20], water management [21,22], remanufacturing [23], bike sharing [24,25], retail pharmacies [26], hospitals [27,28], logistics [14], and spare parts management [14,[29][30][31], showing its usefulness.…”

How Artificial Intelligence Can Improve Understanding in Challenging Chaotic Environments

“…As an example, a recent study is investigated. Hafezi et al (Hafezi, Akhavan, Zamani, et al 2019) tested a hypothesis to prove the usefulness of learning models to purify driving forces in a complex system. Driving forces are those features which shape the future and a decision-maker can interpret upcoming trends refer to driving forces different orientations.…”

“…Table 1. Statistical errors for different input vectors (raw versus processed) based on (Hafezi, Akhavan, Zamani, et al 2019) In this research authors extracted a subset of 13 features contains only 6 features (the input vector dimension reduced almost about 50%) using a feature selection method named correlation-based feature selection (CFS) (proposed by hall (Hall 1999)). DM based feature selection guarantees to reduce problem space (i.e.…”

“…Data mining ignored the mentioned simplifying assumption and instead it assumes in the real world variables are correlated but with varying degrees (Hafezi, Shahrabi, and Hadavandi 2015). Data mining (DM) is defined as the process of extracting appealing patterns and deriving knowledge in massive datasets (Hafezi, Akhavan, Zamani, et al 2019). Refer to Han et al: "the principal dimensions are data, knowledge, applications, and technologies" (Han, Pei, and Kamber 2011).…”

How Artificial Intelligence Can Improve Understanding in Challenging Chaotic Environments

“…DM ignored the mentioned simplifying assumption and instead it assumes in the real world variables are correlated but with varying degrees (Hafezi et al 2015). DM is defined as the process of extracting appealing patterns and deriving knowledge in massive data sets (Hafezi, Akhavan, Zamani, et al 2019). Refer to Han et al (2011): “the principal dimensions are data, knowledge, applications, and technologies.” Moreover, machine learning (ML) is used extensively to predict in the case of complex systems and dynamic environments (Hafezi and Akhavan 2018; Lotfinejad et al 2018).…”