Forecasting Residential Consumption of Natural Gas Using Genetic Algorithms

Aras, Nil

doi:10.1260/014459808787548705

Cited by 37 publications

(24 citation statements)

References 51 publications

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“…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%

“…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%

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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“…In their study, they proposed a quadratic forecasting equation that depends on population, gross domestic product (GDP), export, import, residential housing production and cement production, house appliances sales of refrigerator, washing machine, television and vacuum cleaner figures of Turkey. Aras [7] presented an application of GAs to forecast shortterm demand for natural gas in residences in Turkey. Aras [7] assumed that residential demand is a function of time, heating degree-day value, and consumer price index.…”

Section: Introductionmentioning

confidence: 99%

“…Aras [7] presented an application of GAs to forecast shortterm demand for natural gas in residences in Turkey. Aras [7] assumed that residential demand is a function of time, heating degree-day value, and consumer price index. Therefore, Aras proposed a GA to estimate parameters of a multiple nonlinear regression model which mathematically represents the relationship between natural gas consumption and influential variables.…”

Section: Introductionmentioning

confidence: 99%

Artificial bee colony algorithm to forecast natural gas consumption of Turkey

Arık

2019

SN Appl. Sci.

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Natural gas is one of the most important energy sources in the world because it is cheaper and cleaner than most of its alternatives. Therefore, natural gas is used for residential and industrial purposes such as heating, electricity generation and manufacturing. Although Turkey has its own resources to meet the demand for natural gas, most of the needed natural gas amount is imported from other countries. The produced or imported natural gas has been used for heating, electricity generation and manufacturing in Turkey. Therefore approximate forecasting with a less statistical error of vital energy sources such as natural gas is extremely significant for countries having an imbalance between export and import values. The main purpose of this study is to determine the best coefficients with less statistical error for the forecasting equation for natural gas demand of Turkey. This paper suggests an Artificial Bee Colony (ABC) algorithm for forecasting natural gas consumption in billion cubic meters (bcm) for the years between 2018 and 2030 by using some historical indicators between 1998 and 2017. ABC algorithm is firstly used for determining coefficients of a forecasting equation for natural gas demand of Turkey. ABC algorithm was originally designed for continuous optimization problems and with its simple structure and applicability can be useful for decision or policymakers. Therefore such a strong metaheuristic algorithm in the literature should be used for a vital problem of the countries having current account deficit like Turkey. Annual gross domestic product at chained volume index, population, export and import of Turkey are selected as historical indicators and independent variables for forecasting in this study. Furthermore, a linear regression model is also created to compare the proposed forecasting model. Both models are used to forecast Turkey future natural gas consumption for two different scenarios. The proposed algorithm outperforms the linear regression model in view of total absolute relative errors from previous natural gas consumptions of Turkey.

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Artificial neural network-based genetic algorithm to predict natural gas consumption

Karimi

Dastranj

2014

Energy Syst

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Forecasting Residential Consumption of Natural Gas Using Genetic Algorithms

Cited by 37 publications

References 51 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

Artificial bee colony algorithm to forecast natural gas consumption of Turkey

Artificial neural network-based genetic algorithm to predict natural gas consumption

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