Research on City Gas Load Forecasting Method

Ting Ting Wang; Hong Yan Tan

doi:10.4028/www.scientific.net/AMM.405-408.2986

Paper Titles

A New Air-Conditioning System with Chilled Water Storage
p.2964

The Study of Soil Temputerature Change of GSHP System in Winter Conditions
p.2969

Exergy Analysis of a Compressor Assisted Triple-Effect Absorption Refrigerating Cycle
p.2975

Numerical Simulation Analysis of Composite Air Purifying Device
p.2980

Research on City Gas Load Forecasting Method
p.2986

The Study of the Ventilation Influence to Gas-Based Direct Reduction Shaft Furnace Flow Field
p.2990

Basic Research of Solar Seasonal Thermal Storage Capacity Increase to Existing Heating System
p.2994

Research on the SVM Classification of Active and Passive Remote Sensing Data Based on the Feature Per-Parcel
p.3001

The Application of Network RTK Technology in Road Construction Survey
p.3007

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 405-408Research on City Gas Load Forecasting Method

Research on City Gas Load Forecasting Method

Abstract:

The paper mainly studied the gas forecasting in gas-consuming rush hour and long-term load forecasting.The paper analyzed the factors influencing the volume of gas forecasting in rush hour and forecast the volume of gas in rush hour using fuzzy method and RBF neural network. In long term city gas load forecasting, there are some characters such as longer time and uncertain demand increasing. A method is proposed to weakening the original data by using buffer operator before use GM(1,1)model. It indicated that the result acquired by these methods was satisfied with the requirement of engineering, and it was helpful to dispatchers.

You might also be interested in these eBooks

Progress in Industrial and Civil Engineering II

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 405-408)

Pages:

2986-2989

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.405-408.2986

Citation:

Cite this paper

Online since:

September 2013

Authors:

Ting Ting Wang*, Hong Yan Tan

Keywords:

Gray Method, Load Forecasting, RBF Neural Network

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] Xin-ChenGuo, Zhou-YinChen. Short-term load forecasting using neural network with principal component analysis. Proceeding of the Third International Conference on Machine Learning and Cybernetics, Shanghai, (2004).

DOI: 10.1109/icmlc.2004.1380362

Google Scholar

[2] J. Nazarko,W. Zalewski. The Fuzzy Regression Approach to Peak Load Estimation in Power Distribution Systems. IEEE Transactions on Power Systems (1999).

DOI: 10.1109/59.780890

Google Scholar

[3] Jiao Wen-ling, Yan Ming-qing, Lian Le-ming. Grey prediction of city gas load. Gas and Heat(2001).

Google Scholar

[4] Chien-Ying Li, Tsong-Liang Huang. Optimal Design of the Grey Prediction PID Controller for Power System Stabilizers by Evolu- tionary Programming. Proceeding of the IEEE, International Conference on Networking, Sensing & Control(2004).

DOI: 10.1109/icnsc.2004.1297147

Google Scholar

[5] Hiroyuki Mori, Tadahiro Itagaki. A Fuzzy Inference Neural Network Based Method for Short-term Load Forecasting. IEEE, (2004).

DOI: 10.1109/ijcnn.2004.1381004

Google Scholar

[6] WenXin, ZhouLu, Li Dongjiang. The analysis and apply of fuzzy logicwork-box in Matlab(2001).

Google Scholar

[7] Ranaweera D K, Hubele N F, Karady G G Fuzzy logic for short term load forecasting. Electrical Power & Energy System(1996).

DOI: 10.1016/0142-0615(95)00060-7

Google Scholar

[8] Tamimi M, Egbert R. Short term electric load forecasting via fuzzy neural collaboration. Electric Power Systems Research(2000).

DOI: 10.1016/s0378-7796(00)00123-1

Google Scholar

[9] Wang Yu-chun, Li Jina-min, Zhu Yong. A new method to predict the dynamic demand of gas consumption, Petroleum Planning&Engineering(1999).

Google Scholar