Academic Journal
Developing a Data Mining Based Model to Extract Predictor Factors in Energy Systems: Application of Global Natural Gas Demand
| العنوان: | Developing a Data Mining Based Model to Extract Predictor Factors in Energy Systems: Application of Global Natural Gas Demand |
|---|---|
| المؤلفون: | Reza Hafezi, Amir Naser Akhavan, Mazdak Zamani, Saeed Pakseresht, Shahaboddin Shamshirband |
| المصدر: | Energies, Vol 12, Iss 21, p 4124 (2019) |
| بيانات النشر: | MDPI AG, 2019. |
| سنة النشر: | 2019 |
| المجموعة: | LCC:Technology |
| مصطلحات موضوعية: | natural gas demands, prediction, energy market, genetic algorithm, artificial neural network, data mining, Technology |
| الوصف: | 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). |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1996-1073 12214124 |
| Relation: | https://www.mdpi.com/1996-1073/12/21/4124; https://doaj.org/toc/1996-1073 |
| DOI: | 10.3390/en12214124 |
| URL الوصول: | https://doaj.org/article/44c7abc66b164d5d9056b4a376544153 |
| رقم الانضمام: | edsdoj.44c7abc66b164d5d9056b4a376544153 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 19961073 12214124 |
|---|---|
| DOI: | 10.3390/en12214124 |