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 ◽  
...  

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.

2021 ◽  
Vol 299 ◽  
pp. 117256
Author(s):  
Georgios I. Tsoumalis ◽  
Zafeirios N. Bampos ◽  
Georgios V. Chatzis ◽  
Pandelis N. Biskas ◽  
Stratos D. Keranidis

2020 ◽  
Vol 2 (4) ◽  
pp. 297-308
Author(s):  
Mohamed Ali Ismail ◽  
Eman Mahmoud Abd El-Metaal

Purpose This paper aims to obtain accurate forecasts of the hourly residential natural gas consumption, in Egypt, taken into consideration the volatile multiple seasonal nature of the gas series. This matter helps in both minimizing the cost of energy and maintaining the reliability of the Egyptian power system as well. Design/methodology/approach Double seasonal autoregressive integrated moving average-generalized autoregressive conditional heteroskedasticity model is used to obtain accurate forecasts of the hourly Egyptian gas consumption series. This model captures both daily and weekly seasonal patterns apparent in the series as well as the volatility of the series. Findings Using the mean absolute percentage error to check the forecasting accuracy of the model, it is proved that the produced outcomes are accurate. Therefore, the proposed model could be recommended for forecasting the Egyptian natural gas consumption. Originality/value The contribution of this research lies in the ingenuity of using time series models that accommodate both daily and weekly seasonal patterns, which have not been taken into consideration before, in addition to the series volatility to forecast hourly consumption of natural gas in Egypt.


2020 ◽  
Vol 84 ◽  
pp. 393-404 ◽  
Author(s):  
Wenqing Wu ◽  
Xin Ma ◽  
Bo Zeng ◽  
Wangyong Lv ◽  
Yong Wang ◽  
...  

Sign in / Sign up

Export Citation Format

Share Document