Forecasting Fossil Fuel Energy Consumption for Power Generation Using QHSA-Based LSSVM ModelReport as inadecuate




Forecasting Fossil Fuel Energy Consumption for Power Generation Using QHSA-Based LSSVM Model - Download this document for free, or read online. Document in PDF available to download.

1

School of Economics and Management, North China Electric Power University, Baoding 071003, Hebei, China

2

Department of Electronic & Communication Engineering, North China Electric Power University, Baoding 071003, Hebei, China

3

Key Laboratory of Advanced Control and Optimization for Chemical Processes, East China University of Science and Technology, Shanghai 200240, China





*

Authors to whom correspondence should be addressed.



Academic Editor: Vincenzo Dovì

Abstract Accurate forecasting of fossil fuel energy consumption for power generation is important and fundamental for rational power energy planning in the electricity industry. The least squares support vector machine LSSVM is a powerful methodology for solving nonlinear forecasting issues with small samples. The key point is how to determine the appropriate parameters which have great effect on the performance of LSSVM model. In this paper, a novel hybrid quantum harmony search algorithm-based LSSVM QHSA-LSSVM energy forecasting model is proposed. The QHSA which combines the quantum computation theory and harmony search algorithm is applied to searching the optimal values of and C in LSSVM model to enhance the learning and generalization ability. The case study on annual fossil fuel energy consumption for power generation in China shows that the proposed model outperforms other four comparative models, namely regression, grey model 1, 1 GM 1, 1, back propagation BP and LSSVM, in terms of prediction accuracy and forecasting risk. View Full-Text

Keywords: fossil fuel energy forecasting; power generation; LSSVM; quantum harmony search algorithm QHSA fossil fuel energy forecasting; power generation; LSSVM; quantum harmony search algorithm QHSA





Author: Wei Sun 1,* , Yujun He 2 and Hong Chang 3,*

Source: http://mdpi.com/



DOWNLOAD PDF




Related documents