Forecasting of natural gas consumption with artificial neural networks

Szoplik, J.

doi:10.1016/j.energy.2015.03.084

Cited by 266 publications

(108 citation statements)

References 43 publications

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“…In the second group, publications can be grouped as regional [8][9][10][11][12]15,18,19,[21][22][23][24] or national [6,7,13,14,20,[24][25][26][27][28][29][30][31] consumptions are investigated. In the third group, papers are investigated by consumer types.…”

Section: Related Workmentioning

confidence: 99%

“…This group includes household consumers [6][7][8][9][10][11][12], commercial consumers [11,13,25] and consumers where all consumption sectors are included [14][15][16][17][18][24][25][26][27][28][29][30][31]. In the fourth group, where studies are categorized by data used, papers are divided with respect to the use of only consumption data using univariate approaches [28][29][30][31] or independent variable [6][7][8][9][10][11][12][13][14][15][16][17][18]20,[22][23][24][25][26][27] included studies. Investigation of these studies showed that, mostly independent variable included regional-based natural gas consumption prediction is done.…”

Section: Related Workmentioning

confidence: 99%

“…In the first group, publications can be divided according to the use of timeframes which apply the time series method in daily periods [6][7][8][9][10][11][12][13][14][15][16][17][18][19] and monthly periods [20][21][22][23][24]. In the second group, publications can be grouped as regional [8][9][10][11][12]15,18,19,[21][22][23][24] or national [6,7,13,14,20,[24][25][26][27][28][29][30][31] consumptions are investigated.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Year Ahead Demand Forecast of City Natural Gas Using Seasonal Time Series Methods

Akpınar

Yumuşak

2016

Energies

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Consumption of natural gas, a major clean energy source, increases as energy demand increases. We studied specifically the Turkish natural gas market. Turkey's natural gas consumption increased as well in parallel with the world's over the last decade. This consumption growth in Turkey has led to the formation of a market structure for the natural gas industry. This significant increase requires additional investments since a rise in consumption capacity is expected. One of the reasons for the consumption increase is the user-based natural gas consumption influence. This effect yields imbalances in demand forecasts and if the error rates are out of bounds, penalties may occur. In this paper, three univariate statistical methods, which have not been previously investigated for mid-term year-ahead monthly natural gas forecasting, are used to forecast natural gas demand in Turkey's Sakarya province. Residential and low-consumption commercial data is used, which may contain seasonality. The goal of this paper is minimizing more or less gas tractions on mid-term consumption while improving the accuracy of demand forecasting. In forecasting models, seasonality and single variable impacts reinforce forecasts. This paper studies time series decomposition, Holt-Winters exponential smoothing and autoregressive integrated moving average (ARIMA) methods. Here, 2011-2014 monthly data were prepared and divided into two series. The first series is 2011-2013 monthly data used for finding seasonal effects and model requirements. The second series is 2014 monthly data used for forecasting. For the ARIMA method, a stationary series was prepared and transformation process prior to forecasting was done. Forecasting results confirmed that as the computation complexity of the model increases, forecasting accuracy increases with lower error rates. Also, forecasting errors and the coefficients of determination values give more consistent results. Consequently, when there is only consumption data in hand, all methods provide satisfying results and the differences between each method is very low. If a statistical software tool is not used, time series decomposition, the most primitive method, or Winters exponential smoothing requiring little mathematical knowledge for natural gas demand forecasting can be used with spreadsheet software. A statistical software tool containing ARIMA will obtain the best results.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Year Ahead Demand Forecast of City Natural Gas Using Seasonal Time Series Methods

Akpınar

Yumuşak

2016

Energies

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“…To estimate these three parameters, ϕ 1 , ϕ 2 , and ϕ 3 in Equation (15), the OLS method is used to minimize the simulation errors, as defined as Equation (16) [1]:…”

Section: The Self-adapting Intelligent Grey Model (Sigm)mentioning

confidence: 99%

Combining Grey Model and Self-Adapting Intelligent Grey Model with Genetic Algorithm and Annual Share Changes in Natural Gas Demand Forecasting

Fan¹,

Wang²,

Hong

2018

Energies

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Along with the high growth rate of economy and fast increasing air pollution, clean energy, such as the natural gas, has played an important role in preventing the environment from discharge of greenhouse gases and harmful substances in China. It is very important to accurately forecast the demand of natural gas in China is for the government to formulate energy policies. This paper firstly proposes a combined forecasting model, name GM-S-SIGM-GA model, to forecast the demand of natural gas in China from 2011 to 2017, by constructing the grey model (GM(1,1)) and the self-adapting intelligent grey model (SIGM), respectively; then, it employs a genetic algorithm to determine the combined weight coefficients between these two models. Finally, using the tendency index (the annual changes of the share of natural gas consumption from the total energy consumption), which completely reveal the annual natural gas consumption share among the market, to successfully adjust the fluctuated changes for each data period. The natural gas demand data from 2002 to 2010 in China are used to model the proposed GM-S-SIGM-GA model, and the data from 2011 to 2017 are used to evaluate the forecasting accuracy. The experimental results demonstrate that the proposed GM-S-SIGM-GA model is superior to other single forecasting models in terms of the mean absolute percentage error (MAPE; 4.48%), the root mean square error (RMSE; 11.59), and the mean absolute error (MAE; 8.41), respectively, and the forecasting performances also receive the statistical significance under 97.5% and 95% confident levels, respectively.

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“…The effect of energy policy on the different segments of consumers can be studied based on the way these different segments consume this energy source. A substantial amount of effort has been put into the gas demand forecasting [11][12][13][14][15][16] and into the determinant factors of residential gas consumption [17,18]. The study of energy savings has been conducted for buildings, as well, mostly inserted in projects for the development of sustainable cities [19,20].…”

Section: Introductionmentioning

confidence: 99%

Segmentation of Residential Gas Consumers Using Clustering Analysis

et al. 2017

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Abstract:The growing environmental concerns and liberalization of energy markets have resulted in an increased competition between utilities and a strong focus on efficiency. To develop new energy efficiency measures and optimize operations, utilities seek new market-related insights and customer engagement strategies. This paper proposes a clustering-based methodology to define the segmentation of residential gas consumers. The segments of gas consumers are obtained through a detailed clustering analysis using smart metering data. Insights are derived from the segmentation, where the segments result from the clustering process and are characterized based on the consumption profiles, as well as according to information regarding consumers' socio-economic and household key features. The study is based on a sample of approximately one thousand households over one year. The representative load profiles of consumers are essentially characterized by two evident consumption peaks, one in the morning and the other in the evening, and an off-peak consumption. Significant insights can be derived from this methodology regarding typical consumption curves of the different segments of consumers in the population. This knowledge can assist energy utilities and policy makers in the development of consumer engagement strategies, demand forecasting tools and in the design of more sophisticated tariff systems.

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Forecasting of natural gas consumption with artificial neural networks

Cited by 266 publications

References 43 publications

Year Ahead Demand Forecast of City Natural Gas Using Seasonal Time Series Methods

Year Ahead Demand Forecast of City Natural Gas Using Seasonal Time Series Methods

Combining Grey Model and Self-Adapting Intelligent Grey Model with Genetic Algorithm and Annual Share Changes in Natural Gas Demand Forecasting

Segmentation of Residential Gas Consumers Using Clustering Analysis

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