Applied Forecasting of Short-Term Natural Gas Consumption

2011 ◽  
Author(s):  
Primož Potočnik ◽  
Edvard Govekar
Keyword(s):  
Natural Gas ◽  
Short Term ◽  
Gas Consumption ◽  
Natural Gas Consumption

Short-Term Forecasting of Natural Gas Consumption Using Factor Selection Algorithm and Optimized Support Vector Regression

2018 ◽  
Vol 141 (3) ◽  
Author(s):  
Nan Wei ◽  
Changjun Li ◽  
Chan Li ◽  
Hanyu Xie ◽  
Zhongwei Du ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Natural Gas ◽  
Support Vector Regression ◽  
Series Data ◽  
Support Vector ◽  
Selection Algorithm ◽  
Short Term ◽  
Proposed Model ◽  
Gas Consumption ◽  
Natural Gas Consumption

Forecasting of natural gas consumption has been essential for natural gas companies, customers, and governments. However, accurate forecasting of natural gas consumption is difficult, due to the cyclical change of the consumption and the complexity of the factors that influence the consumption. In this work, we constructed a hybrid artificial intelligence (AI) model to predict the short-term natural gas consumption and examine the effects of the factors in the consumption cycle. The proposed model combines factor selection algorithm (FSA), life genetic algorithm (LGA), and support vector regression (SVR), namely, as FSA-LGA-SVR. FSA is used to select factors automatically for different period based on correlation analysis. The LGA optimized SVR is utilized to provide the prediction of time series data. To avoid being trapped in local minima, the hyper-parameters of SVR are determined by LGA, which is enhanced due to newly added “learning” and “death” operations in conventional genetic algorithm. Additionally, in order to examine the effects of the factors in different period, we utilized the recent data of three big cities in Greece and divided the data into 12 subseries. The prediction results demonstrated that the proposed model can give a better performance of short-term natural gas consumption forecasting compared to the estimation value of existing models. Particularly, the mean absolute range normalized errors of the proposed model in Athens, Thessaloniki, and Larisa are 1.90%, 2.26%, and 2.12%, respectively.

A novel Grey Bernoulli model for short-term natural gas consumption forecasting

2020 ◽  
Vol 84 ◽  
pp. 393-404 ◽  
Author(s):  
Wenqing Wu ◽  
Xin Ma ◽  
Bo Zeng ◽  
Wangyong Lv ◽  
Yong Wang ◽  
...  
Keyword(s):  
Natural Gas ◽  
Bernoulli Model ◽  
Short Term ◽  
Gas Consumption ◽  
Natural Gas Consumption

Minimization of natural gas consumption of domestic boilers with convolutional, long-short term memory neural networks and genetic algorithm

2021 ◽  
Vol 299 ◽  
pp. 117256
Author(s):  
Georgios I. Tsoumalis ◽  
Zafeirios N. Bampos ◽  
Georgios V. Chatzis ◽  
Pandelis N. Biskas ◽  
Stratos D. Keranidis
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Natural Gas ◽  
Short Term Memory ◽  
Short Term ◽  
Term Memory ◽  
Gas Consumption ◽  
Natural Gas Consumption ◽  
Long Short Term Memory

Short-term natural gas consumption forecasting from long-term data collection

Energy ◽  
2021 ◽  
Vol 218 ◽  
pp. 119430
Author(s):  
Radek Svoboda ◽  
Vojtech Kotik ◽  
Jan Platos
Keyword(s):  
Natural Gas ◽  
Data Collection ◽  
Short Term ◽  
Gas Consumption ◽  
Natural Gas Consumption ◽  
Term Data

Detrending daily natural gas consumption series to improve short-term forecasts

2015 ◽  
Author(s):  
Ronald H. Brown ◽  
Steven R. Vitullo ◽  
George F. Corliss ◽  
Monica Adya ◽  
Paul E. Kaefer ◽  
...  
Keyword(s):  
Natural Gas ◽  
Short Term ◽  
Gas Consumption ◽  
Natural Gas Consumption
Sign in / Sign up

Export Citation Format

Share Document