التفاصيل البيبلوغرافية
| العنوان: |
Forecasting Electricity Demand Using a New Grey Prediction Model with Smoothness Operator |
| المؤلفون: |
Lianming Zhao, Xueyu Zhou |
| المصدر: |
Symmetry; Volume 10; Issue 12; Pages: 693 |
| بيانات النشر: |
Multidisciplinary Digital Publishing Institute |
| سنة النشر: |
2018 |
| المجموعة: |
MDPI Open Access Publishing |
| مصطلحات موضوعية: |
prediction of the electricity demand, random oscillation sequence, grey forecasting model with three parameters, smoothness operator, GFM_TP, IGFM_TP |
| الوصف: |
A stable electricity supply is the basis for ensuring the healthy and sustained development of a regional economy. Reasonable electricity prediction is the key to guaranteeing the stability and efficiency of electricity supply. To this end, we used a reformative grey prediction model to forecast electricity demand. In order to effectively improve the smoothness of a raw modelling sequence, we employed an existing smoothing algorithm that significantly compressed the amplitude of the random oscillation sequence. Then, an improved grey forecasting model with three parameters (IGFM_TP) was deduced. In the end, a new model was used to forecast the demand for electricity of one city in the western region of China, and comparisons of simulation values and errors with those of GFM_TP, GM(1,1), DGM(1,1) and SAIGM were conducted. The findings show that the mean absolute simulation percentage error of IGFM_TP was 7.8%, and those of the other four models were 12.1%, 12.3%, 11.1%, and 10.1%, respectively. Therefore, the simulation precision of the new model achieved an optimal effect. The proposed new grey model provides is an effective method for electricity demand prediction. |
| نوع الوثيقة: |
text |
| وصف الملف: |
application/pdf |
| اللغة: |
English |
| Relation: |
https://dx.doi.org/10.3390/sym10120693 |
| DOI: |
10.3390/sym10120693 |
| الاتاحة: |
https://doi.org/10.3390/sym10120693 |
| Rights: |
https://creativecommons.org/licenses/by/4.0/ |
| رقم الانضمام: |
edsbas.F1255785 |
| قاعدة البيانات: |
BASE |