التفاصيل البيبلوغرافية
| العنوان: |
Forecasting Fossil Fuel Energy Consumption for Power Generation Using QHSA-Based LSSVM Model |
| المؤلفون: |
Wei Sun, Yujun He, Hong Chang |
| المصدر: |
Energies, Vol 8, Iss 2, Pp 939-959 (2015) |
| بيانات النشر: |
MDPI AG, 2015. |
| سنة النشر: |
2015 |
| المجموعة: |
LCC:Technology |
| مصطلحات موضوعية: |
fossil fuel energy forecasting, power generation, LSSVM, quantum harmony search algorithm (QHSA), Technology |
| الوصف: |
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. |
| نوع الوثيقة: |
article |
| وصف الملف: |
electronic resource |
| اللغة: |
English |
| تدمد: |
1996-1073 |
| Relation: |
http://www.mdpi.com/1996-1073/8/2/939; https://doaj.org/toc/1996-1073 |
| DOI: |
10.3390/en8020939 |
| URL الوصول: |
https://doaj.org/article/2487d1e2c29a4bb7bc2f51d4d6027aa9 |
| رقم الانضمام: |
edsdoj.2487d1e2c29a4bb7bc2f51d4d6027aa9 |
| قاعدة البيانات: |
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