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Article

Application of Artificial Neural Networks for Natural Gas Consumption Forecasting

1
Institute for Bio-economy and Agri-technology (iBO), Center for Research and Technology—Hellas (CERTH), Thessaloniki GR57001, Greece
2
Department of Computer Science, University of Thessaly, Lamia GR35131, Greece
3
Department of Energy Systems, Faculty of Technology, University of Thessaly, Geopolis Campus Ring Road of Larissa-Trikala, Larissa GR41500, Greece
*
Author to whom correspondence should be addressed.
Sustainability 2020, 12(16), 6409; https://doi.org/10.3390/su12166409
Received: 3 July 2020 / Revised: 5 August 2020 / Accepted: 6 August 2020 / Published: 9 August 2020
(This article belongs to the Special Issue Green, Closed Loop, Circular Bio-Economy)
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. View Full-Text
Keywords: machine learning; artificial neural networks; natural gas; demand forecasting machine learning; artificial neural networks; natural gas; demand forecasting
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MDPI and ACS Style

Anagnostis, A.; Papageorgiou, E.; Bochtis, D. Application of Artificial Neural Networks for Natural Gas Consumption Forecasting. Sustainability 2020, 12, 6409. https://doi.org/10.3390/su12166409

AMA Style

Anagnostis A, Papageorgiou E, Bochtis D. Application of Artificial Neural Networks for Natural Gas Consumption Forecasting. Sustainability. 2020; 12(16):6409. https://doi.org/10.3390/su12166409

Chicago/Turabian Style

Anagnostis, Athanasios, Elpiniki Papageorgiou, and Dionysis Bochtis. 2020. "Application of Artificial Neural Networks for Natural Gas Consumption Forecasting" Sustainability 12, no. 16: 6409. https://doi.org/10.3390/su12166409

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