Application of Artificial Neural Networks for Natural Gas Consumption Forecasting

Athanasios Anagnostis, Elpiniki Papageorgiou and Dionysis Bochtis
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Athanasios Anagnostis: Institute for Bio-economy and Agri-technology (iBO), Center for Research and Technology—Hellas (CERTH), Thessaloniki GR57001, Greece
Elpiniki Papageorgiou: Institute for Bio-economy and Agri-technology (iBO), Center for Research and Technology—Hellas (CERTH), Thessaloniki GR57001, Greece
Dionysis Bochtis: Institute for Bio-economy and Agri-technology (iBO), Center for Research and Technology—Hellas (CERTH), Thessaloniki GR57001, Greece

Sustainability, 2020, vol. 12, issue 16, 1-29

Abstract: The present research study explores three types of neural network approaches for forecasting natural gas consumption in fifteen cities throughout Greece; a simple perceptron artificial neural network (ANN), a state-of-the-art Long Short-Term Memory (LSTM), and the proposed deep neural network (DNN). In this research paper, a DNN implementation is proposed where variables related to social aspects are introduced as inputs. These qualitative factors along with a deeper, more complex architecture are utilized for improving the forecasting ability of the proposed approach. A comparative analysis is conducted between the proposed DNN, the simple ANN, and the advantageous LSTM, with the results offering a deeper understanding the characteristics of Greek cities and the habitual patterns of their residents. The proposed implementation shows efficacy on forecasting daily values of energy consumption for up to four years. For the evaluation of the proposed approach, a real-life dataset for natural gas prediction was used. A detailed discussion is provided on the performance of the implemented approaches, the ANN and the LSTM, that are characterized as particularly accurate and effective in the literature, and the proposed DNN with the inclusion of the qualitative variables that govern human behavior, which outperforms them.

Keywords: machine learning; artificial neural networks; natural gas; demand forecasting (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:12:y:2020:i:16:p:6409-:d:396667

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