Modeling and forecasting industrial end-use natural gas consumption

Sánchez‐Úbeda, Eugenio F.; Berzosa, Ana

doi:10.1016/j.eneco.2007.01.015

Cited by 86 publications

(29 citation statements)

References 13 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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“…The error value of each epoch is the sum of the squares of the iteration errors Equation (5). The error value mean squared error (MSE) of the last epoch is the error at the end of the training, and it is expected to be low:…”

Section: Backpropagation Algorithmmentioning

confidence: 99%

Day-Ahead Natural Gas Demand Forecasting Using Optimized ABC-Based Neural Network with Sliding Window Technique: The Case Study of Regional Basis in Turkey

2017

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Abstract:The increase of energy consumption in the world is reflected in the consumption of natural gas. However, this increment requires additional investment. This effect leads imbalances in terms of demand forecasting, such as applying penalties in the case of error rates occurring beyond the acceptable limits. As the forecasting errors increase, penalties increase exponentially. Therefore, the optimal use of natural gas as a scarce resource is important. There are various demand forecast ranges for natural gas and the most difficult range among these demands is the day-ahead forecasting, since it is hard to implement and makes predictions with low error rates. The objective of this study is stabilizing gas tractions on day-ahead demand forecasting using low-consuming subscriber data for minimizing error using univariate artificial bee colony-based artificial neural networks (ANN-ABC). For this purpose, households and low-consuming commercial users' four-year consumption data between the years of 2011-2014 are gathered in daily periods. Previous consumption values are used to forecast day-ahead consumption values with sliding window technique and other independent variables are not taken into account. Dataset is divided into two parts. First, three-year daily consumption values are used with a seven day window for training the networks, while the last year is used for the day-ahead demand forecasting. Results show that ANN-ABC is a strong, stable, and effective method with a low error rate of 14.9 mean absolute percentage error (MAPE) for training utilizing MAPE with a univariate sliding window technique.

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“…The advent of computer technology has led scientists to developing complex models to forecast natural gas consumption by improving calculation algorithms and using different statistical methods (Smith et al 1996;Gorucu and Gumrah 2004;Sánchez-Úbeda and Berzosa 2007;Forouzanfar et al 2010;Soldo 2012). An efficient management of energy distribution system often requires outlook prediction in relation to energy demand (Mirasgedis et al 2006;Potocnik et al 2007;Dovrtel and Medved 2011;Oldewurtel et al 2012;Petersen and Bundgaard 2014), which is strictly related to seasonal weather and climatic trends.…”

Section: Introductionmentioning

confidence: 99%

Benchmark analysis of forecasted seasonal temperature over different climatic areas

et al. 2015

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From a long-term perspective, an improvement of seasonal forecasting, which is often exclusively based on climatology, could provide a new capability for the management of energy resources in a time scale of just a few months. This paper regards a benchmark analysis in relation to long-term temperature forecasts over Italy in the year 2010, comparing the eni-kassandra meteo forecast (e-kmf ® ) model, the Climate Forecast System-National Centers for Environmental Prediction (CFS-NCEP) model, and the climatological reference (based on 25-year data) with observations. Statistical indexes are used to understand the reliability of the prediction of 2-m monthly air temperatures with a perspective of 12 weeks ahead. The results show how the best performance is achieved by the e-kmf ® system which improves the reliability for long-term forecasts compared to climatology and the CFS-NCEP model. By using the reliable highperformance forecast system, it is possible to optimize the natural gas portfolio and management operations, thereby obtaining a competitive advantage in the European energy market.

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Modeling and forecasting industrial end-use natural gas consumption

Cited by 86 publications

References 13 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

Day-Ahead Natural Gas Demand Forecasting Using Optimized ABC-Based Neural Network with Sliding Window Technique: The Case Study of Regional Basis in Turkey

Benchmark analysis of forecasted seasonal temperature over different climatic areas

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