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.
Highlights
The consumption of natural gas has seen a substantial increase during recent years, as it presents a reliable and economical energy and heating solution for businesses as well as households
The outcomes of each parameter selection for the artificial neural network (ANN), the Long Short-Term Memory (LSTM), and the deep neural network (DNN) architectures are presented in paragraphs 5.1, 5.2, and 5.3
In the case of LSTM implementation, it is clear that the forecasts for the one- and two-year ahead demands are more accurate than that of the ANN implementation
Summary
The consumption of natural gas has seen a substantial increase during recent years, as it presents a reliable and economical energy and heating solution for businesses as well as households. Its wide acceptance from large-scale infrastructures to small houses has created diverse consumption patterns, especially during high-demand occasions This has perplexed any attempt of forecasting its demand, especially when one considers the diversity of the consumers and the finite restrictions of the natural gas infrastructure, i.e., low accumulation ability within the grid. Analytical modelling of such complicated systems would require substantial effort in order to design the grid architecture and each of its consumers, apply correct heat losses throughout the pipes, and in general, include a variety of intricate parameters into the whole system before running the simulation computations. Being dependent on data alone, alternative scenarios based on different energy resources like fossil fuels, oil, or electricity may as well utilize these methods for their own forecasting purposes
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