Breeder hybrid algorithm approach for natural gas demand forecasting model

Karadede, Yusuf; Özdemir, Gültekin; Aydemir, Erdal

doi:10.1016/j.energy.2017.09.130

Cited by 70 publications

(23 citation statements)

References 52 publications

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“…where S is the designed length of the input part; T is the longest time that a disturbance takes to spread to the whole pipeline network system; P i is the shortest period that can influence the future demand evolution of gas demand i. According to the works for demand prediction, the demands of gas can be influenced by various of factors (such as weather, demand history, population and so on) (Karadede et al, 2017). In this work, without lack of generality, we only consider demand history.…”

Section: Moving Data Window Methods and Model Updatingmentioning

confidence: 99%

A systematic hybrid method for real-time prediction of system conditions in natural gas pipeline networks

Zio

Zhang

et al. 2018

Journal of Natural Gas Science and Engineering

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et al.. A systematic hybrid method for real-time prediction of system conditions in natural gas pipeline networks. A B S T R A C TThe current framework of management of natural gas pipeline systems, based on off-line simulation, is facing challenges because of the increasing complexity, uncertainty and a number of time-dependent factors. To be effective, it requires comprehensive knowledge of system characteristics, accurate initial and boundary conditions. In an attempt to circumvent these problems, in this work we propose to use the deep learning method in the natural gas transmission system operation and management context. A data-driven prediction method is developed from real-time data of operation pressure and gas consumption. Specifically, the deep learning method is combined with the data window method and structural controllability theory to predict the conditions of gas pipeline network components. The data window method is applied to reconstruct the data structure and build a "memory" for the deep learning method. Structural controllability theory is applied to extract critical parameters, for reducing the problem size. The developed method allows accurate and efficient predictions, especially in abnormal conditions. For demonstration, the method is applied to a complex gas pipeline network. The results show that the developed method can provide accurate real-time predictions useful for reducing potential losses in operation, and perform efficient and effective management of the gas pipeline system. In the case study, the average prediction accuracy is higher than 0.99.

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Section: Moving Data Window Methods and Model Updatingmentioning

confidence: 99%

A systematic hybrid method for real-time prediction of system conditions in natural gas pipeline networks

Zio

Zhang

et al. 2018

Journal of Natural Gas Science and Engineering

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“…They stated that ANN-ABC is a strong, stable, and effective method with a low error rate of 14.9 mean absolute percentage error for training utilizing mean absolute percentage error (MAPE) with a univariate sliding window technique. Karadede et al [15] proposed a Breeder hybrid algorithm consisting of the constitution of nonlinear regression-based Breeder genetic algorithm and simulated annealing is proposed for the objective of forecasting the NGD of Turkey in years between 2015 and 2030 with a smaller error rate. They used GDP, population and growth rate of Turkey in years between 2001 and 2014 and presented forecasted NGD of Turkey considering two different scenarios.…”

Section: Introductionmentioning

confidence: 99%

“…As far as we know, this study is the first study that uses ABC method to forecast NGD demand of a country. Some researchers have used metaheuristic algorithms such as GA [13], SA [8], SAGA [13] and Breeder [15] algorithms for forecasting NGD of Turkey. Most of the used algorithms having less statistical error are either an evolutionary algorithm or hybrid algorithm using an evolutionary algorithm.…”

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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“…The other is predicted by a combined intelligent algorithm. That is, genetic algorithm [17,21], neural network algorithm [5,11,22,23], support vector machine [24][25][26][27][28], particle swarm algorithm [11,17,25], simulated annealing algorithm [21] and other combinations. In the research results of natural gas consumption forecasting, considering the prediction of uncertainties in natural gas consumption, the concepts of grey theory [17,18], Bayesian average model [19], logistic model [20], etc.…”

Section: Introductionmentioning

confidence: 99%

Forecasting China’s Natural Gas Consumption Based on AdaBoost-Particle Swarm Optimization-Extreme Learning Machine Integrated Learning Method

Gao

2018

Energies

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With the orderly advancement of ‘China's energy development strategic action plan’, the natural gas industry has achieved unprecedented development. Currently, it is planned that by 2020, China’s natural gas consumption will account for at least 10% of the total primary energy consumption, have an orderly and improved energy structure, and achieved energy-saving and emission-reduction targets. Therefore, the accurate prediction of natural gas consumption becomes significantly important. Firstly, based on the research status of forecasting methods and the factors which affect natural gas consumption, this paper used the particle swarm optimization (PSO) algorithm to obtain the input layer weight, and used the optimized extreme learning machine (ELM) algorithm to obtain the hidden layer threshold; by using PSO-ELM as the base predictor and the AdaBoost algorithm, we have constructed the natural gas consumption integrated learning prediction model. Secondly, from the perspective of different provinces and industries, we deeply analyze the current status of natural gas consumption, and the random forest algorithm is used to extract the core influencing factors of natural gas consumption as the independent variables of the prediction model. Finally, data on China's natural gas consumption from 1995 to 2017 are selected, then the feasibility analysis and comparative analysis with other methods are performed. The results show: 1) Using the random forest algorithm to extract the core influencing factors, economic growth, population, household consumption and import dependence degree are significantly representative. 2) Based on the AdaBoost integrated learning algorithm, transforming the weak predictor with poor prediction effect into a strong predictor with strong prediction effect, compared with PSO-ELM、AdaBoost-ELM and ELM algorithm, with R-Square as 0.9999, Mean Square Error (MSE) as 0.8435，Mean Absolute Error (MAE) as 0.2379, Mean Absolute Percentage Error (MAPE) as 0.0008，effectively validated the significant effect of the AdaBoost-PSO-ELM prediction model. 3) Based on the AdaBoost-PSO-ELM prediction model, predict the natural gas core influencing factors and natural gas consumption in the year of 2018–2030. There is an apparent growth trend in the next 13 years, and the average growth rate of natural gas consumption has reached 7.68%.

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Breeder hybrid algorithm approach for natural gas demand forecasting model

Cited by 70 publications

References 52 publications

A systematic hybrid method for real-time prediction of system conditions in natural gas pipeline networks

A systematic hybrid method for real-time prediction of system conditions in natural gas pipeline networks

Artificial bee colony algorithm to forecast natural gas consumption of Turkey

Forecasting China’s Natural Gas Consumption Based on AdaBoost-Particle Swarm Optimization-Extreme Learning Machine Integrated Learning Method

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