This page has only limited features, please log in for full access.

Unclaimed
Guo-Feng Fan
School of Mathematics and Statistics, Ping Ding Shan University, Ping Ding Shan 467000, Henan, China

Basic Info

Basic Info is private.

Honors and Awards

The user has no records in this section


Career Timeline

The user has no records in this section.


Short Biography

The user biography is not available.
Following
Followers
Co Authors
The list of users this user is following is empty.
Following: 0 users

Feed

Journal article
Published: 21 June 2018 in Energies
Reads 0
Downloads 0

Along with the high growth rate of economy and fast increasing air pollution, clean energy, such as the natural gas, has played an important role in preventing the environment from discharge of greenhouse gases and harmful substances in China. It is very important to accurately forecast the demand of natural gas in China is for the government to formulate energy policies. This paper firstly proposes a combined forecasting model, name GM-S-SIGM-GA model, to forecast the demand of natural gas in China from 2011 to 2017, by constructing the grey model (GM(1,1)) and the self-adapting intelligent grey model (SIGM), respectively; then, it employs a genetic algorithm to determine the combined weight coefficients between these two models. Finally, using the tendency index (the annual changes of the share of natural gas consumption from the total energy consumption), which completely reveal the annual natural gas consumption share among the market, to successfully adjust the fluctuated changes for each data period. The natural gas demand data from 2002 to 2010 in China are used to model the proposed GM-S-SIGM-GA model, and the data from 2011 to 2017 are used to evaluate the forecasting accuracy. The experimental results demonstrate that the proposed GM-S-SIGM-GA model is superior to other single forecasting models in terms of the mean absolute percentage error (MAPE; 4.48%), the root mean square error (RMSE; 11.59), and the mean absolute error (MAE; 8.41), respectively, and the forecasting performances also receive the statistical significance under 97.5% and 95% confident levels, respectively.

ACS Style

Guo-Feng Fan; An Wang; Wei-Chiang Hong. Combining Grey Model and Self-Adapting Intelligent Grey Model with Genetic Algorithm and Annual Share Changes in Natural Gas Demand Forecasting. Energies 2018, 11, 1625 .

AMA Style

Guo-Feng Fan, An Wang, Wei-Chiang Hong. Combining Grey Model and Self-Adapting Intelligent Grey Model with Genetic Algorithm and Annual Share Changes in Natural Gas Demand Forecasting. Energies. 2018; 11 (7):1625.

Chicago/Turabian Style

Guo-Feng Fan; An Wang; Wei-Chiang Hong. 2018. "Combining Grey Model and Self-Adapting Intelligent Grey Model with Genetic Algorithm and Annual Share Changes in Natural Gas Demand Forecasting." Energies 11, no. 7: 1625.

Journal article
Published: 26 October 2017 in Energies
Reads 0
Downloads 0

Providing accurate load forecasting plays an important role for effective management operations of a power utility. When considering the superiority of support vector regression (SVR) in terms of non-linear optimization, this paper proposes a novel SVR-based load forecasting model, namely EMD-PSO-GA-SVR, by hybridizing the empirical mode decomposition (EMD) with two evolutionary algorithms, i.e., particle swarm optimization (PSO) and the genetic algorithm (GA). The EMD approach is applied to decompose the load data pattern into sequent elements, with higher and lower frequencies. The PSO, with global optimizing ability, is employed to determine the three parameters of a SVR model with higher frequencies. On the contrary, for lower frequencies, the GA, which is based on evolutionary rules of selection and crossover, is used to select suitable values of the three parameters. Finally, the load data collected from the New York Independent System Operator (NYISO) in the United States of America (USA) and the New South Wales (NSW) in the Australian electricity market are used to construct the proposed model and to compare the performances among different competitive forecasting models. The experimental results demonstrate the superiority of the proposed model that it can provide more accurate forecasting results and the interpretability than others.

ACS Style

Guo-Feng Fan; Li-Ling Peng; Xiangjun Zhao; Wei-Chiang Hong. Applications of Hybrid EMD with PSO and GA for an SVR-Based Load Forecasting Model. Energies 2017, 10, 1713 .

AMA Style

Guo-Feng Fan, Li-Ling Peng, Xiangjun Zhao, Wei-Chiang Hong. Applications of Hybrid EMD with PSO and GA for an SVR-Based Load Forecasting Model. Energies. 2017; 10 (11):1713.

Chicago/Turabian Style

Guo-Feng Fan; Li-Ling Peng; Xiangjun Zhao; Wei-Chiang Hong. 2017. "Applications of Hybrid EMD with PSO and GA for an SVR-Based Load Forecasting Model." Energies 10, no. 11: 1713.

Journal article
Published: 19 March 2016 in Energies
Reads 0
Downloads 0

Electric load forecasting is an important issue for a power utility, associated with the management of daily operations such as energy transfer scheduling, unit commitment, and load dispatch. Inspired by strong non-linear learning capability of support vector regression (SVR), this paper presents an SVR model hybridized with the differential empirical mode decomposition (DEMD) method and quantum particle swarm optimization algorithm (QPSO) for electric load forecasting. The DEMD method is employed to decompose the electric load to several detail parts associated with high frequencies (intrinsic mode function—IMF) and an approximate part associated with low frequencies. Hybridized with quantum theory to enhance particle searching performance, the so-called QPSO is used to optimize the parameters of SVR. The electric load data of the New South Wales (Sydney, Australia) market and the New York Independent System Operator (NYISO, New York, USA) are used for comparing the forecasting performances of different forecasting models. The results illustrate the validity of the idea that the proposed model can simultaneously provide forecasting with good accuracy and interpretability.

ACS Style

Li-Ling Peng; Guo-Feng Fan; Min-Liang Huang; Wei-Chiang Hong. Hybridizing DEMD and Quantum PSO with SVR in Electric Load Forecasting. Energies 2016, 9, 221 .

AMA Style

Li-Ling Peng, Guo-Feng Fan, Min-Liang Huang, Wei-Chiang Hong. Hybridizing DEMD and Quantum PSO with SVR in Electric Load Forecasting. Energies. 2016; 9 (3):221.

Chicago/Turabian Style

Li-Ling Peng; Guo-Feng Fan; Min-Liang Huang; Wei-Chiang Hong. 2016. "Hybridizing DEMD and Quantum PSO with SVR in Electric Load Forecasting." Energies 9, no. 3: 221.

Journal article
Published: 02 April 2013 in Energies
Reads 0
Downloads 0

Electric load forecasting is an important issue for a power utility, associated with the management of daily operations such as energy transfer scheduling, unit commitment, and load dispatch. Inspired by strong non-linear learning capability of support vector regression (SVR), this paper presents a SVR model hybridized with the empirical mode decomposition (EMD) method and auto regression (AR) for electric load forecasting. The electric load data of the New South Wales (Australia) market are employed for comparing the forecasting performances of different forecasting models. The results confirm the validity of the idea that the proposed model can simultaneously provide forecasting with good accuracy and interpretability.

ACS Style

Guo-Feng Fan; Shan Qing; Hua Wang; Wei-Chiang Hong; Hong-Juan Li. Support Vector Regression Model Based on Empirical Mode Decomposition and Auto Regression for Electric Load Forecasting. Energies 2013, 6, 1887 -1901.

AMA Style

Guo-Feng Fan, Shan Qing, Hua Wang, Wei-Chiang Hong, Hong-Juan Li. Support Vector Regression Model Based on Empirical Mode Decomposition and Auto Regression for Electric Load Forecasting. Energies. 2013; 6 (4):1887-1901.

Chicago/Turabian Style

Guo-Feng Fan; Shan Qing; Hua Wang; Wei-Chiang Hong; Hong-Juan Li. 2013. "Support Vector Regression Model Based on Empirical Mode Decomposition and Auto Regression for Electric Load Forecasting." Energies 6, no. 4: 1887-1901.