Daily natural gas consumption forecasting based on a structure-calibrated support vector regression approach

Bai, Yun; Li, Chuan

doi:10.1016/j.enbuild.2016.06.020

Cited by 74 publications

(24 citation statements)

References 39 publications

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“…A method that integrates the SVR algorithm with the reconstruction of time series properties and optimizes the original local predictor by removing false neighbours' is tested in [41]. Bai and Li [42], used a structurally calibrated SVR approach.…”

Section: Machine Learning (Ml)mentioning

confidence: 99%

Application of Predictive Models for Natural Gas Needs - Current State and Future Trends Review

et al. 2020

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Nowadays, in terms of trading on the world scale, to foresee a natural gas consumption represents an essential activity. In the first part, the paper examines the current state of the Serbian natural gas sector and methodology applied for prediction and capacity planning. In addition, the study intends to give a comprehensive assessment of predictive algorithms for natural gas needs involved in the last decade with projections and suggestions for future applications. The primary task is to evaluate used predictive models with an emphasis on the accuracy of the predictions obtained. Additionally, the paper will analyse used parameters, consumption scale, prediction scope, forecast algorithms, and other related information. The main objective of this study is to review the new-fangled information related analyses data from peer-reviewed journals, international conferences, and books.

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Section: Machine Learning (Ml)mentioning

confidence: 99%

Application of Predictive Models for Natural Gas Needs - Current State and Future Trends Review

et al. 2020

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“…Many methods have been developed for NGLF over the past several decades [5,6], including conventional statistical models [7][8][9] and machine-learning (ML)-based models [10]. Since the nonlinear relationships among the parameters are complicated, conventional statistical models cannot obtain the desired prediction accuracy [11]. Lee and Tong [12] found that the forecasting accuracy of the autoregressive integrated moving average (ARIMA) model was poor for nonlinear data.…”

Section: Introductionmentioning

confidence: 99%

Daily Natural Gas Load Forecasting Based on a Hybrid Deep Learning Model

Wei

Duan

et al. 2019

Energies

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Forecasting daily natural gas load accurately is difficult because it is affected by various factors. A large number of redundant factors existing in the original dataset will increase computational complexity and decrease the accuracy of forecasting models. This study aims to provide accurate forecasting of natural gas load using a deep learning (DL)-based hybrid model, which combines principal component correlation analysis (PCCA) and (LSTM) network. PCCA is an improved principal component analysis (PCA) and is first proposed here in this paper. Considering the correlation between components in the eigenspace, PCCA can not only extract the components that affect natural gas load but also remove the redundant components. LSTM is a famous DL network, and it was used to predict daily natural gas load in our work. The proposed model was validated by using recent natural gas load data from Xi’an (China) and Athens (Greece). Additionally, 14 weather factors were introduced into the input dataset of the forecasting model. The results showed that PCCA–LSTM demonstrated better performance compared with LSTM, PCA–LSTM, back propagation neural network (BPNN), and support vector regression (SVR). The lowest mean absolute percentage errors of PCCA–LSTM were 3.22% and 7.29% for Xi’an and Athens, respectively. On these bases, the proposed model can be regarded as an accurate and robust model for daily natural gas load forecasting.

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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%

“…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. are adopted; Intelligent algorithm effectively improves prediction accuracy, namely genetic algorithm [17,21], neural network algorithm [11,22,23,29], support vector machine [24][25][26][27][28], particle swarm optimization [11,17,25], simulated annealing algorithm [21] and other combinations. Jolanta Szoplik et al [22] used the neural network method and Bai et al [24] used Support Vector Machines, constructed the prediction model of natural gas consumption, and calculated the forecast of daily natural gas consumption.…”

Section: Introductionmentioning

confidence: 99%

“…are adopted; Intelligent algorithm effectively improves prediction accuracy, namely genetic algorithm [17,21], neural network algorithm [11,22,23,29], support vector machine [24][25][26][27][28], particle swarm optimization [11,17,25], simulated annealing algorithm [21] and other combinations. Jolanta Szoplik et al [22] used the neural network method and Bai et al [24] used Support Vector Machines, constructed the prediction model of natural gas consumption, and calculated the forecast of daily natural gas consumption. Wang et al [11] analyzed the main influencing factors of natural gas consumption of China, constructed the hybrid prediction model based on particle swarm optimization algorithm and wavelet neural network (PSO-WNN), used the PSO algorithm to optimize the initial weight and wavelet parameters, and through dynamic learning rate to update, to improve training speed, prediction accuracy, and reduce WNN fluctuations.…”

Section: Introductionmentioning

confidence: 99%

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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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Daily natural gas consumption forecasting based on a structure-calibrated support vector regression approach

Cited by 74 publications

References 39 publications

Application of Predictive Models for Natural Gas Needs - Current State and Future Trends Review

Application of Predictive Models for Natural Gas Needs - Current State and Future Trends Review

Daily Natural Gas Load Forecasting Based on a Hybrid Deep Learning Model

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

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