Predicting Building Energy Consumption with a New Grey Model
Based on the existing grey prediction model, this paper proposes a new grey prediction model (the fractional discrete grey model, FDGM (1, 1, tα)), introduces the modeling mechanism and characteristics of the FDGM (1, 1, tα), and uses three groups of data to verify its effectiveness compared with th...
Enregistré dans:
| Auteurs principaux: | , , |
|---|---|
| Format: | article |
| Langue: | EN |
| Publié: |
Hindawi Limited
2021
|
| Sujets: | |
| Accès en ligne: | https://doaj.org/article/a5c7ff4bb6f5425a835b09d4f20cfd9e |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| Résumé: | Based on the existing grey prediction model, this paper proposes a new grey prediction model (the fractional discrete grey model, FDGM (1, 1, tα)), introduces the modeling mechanism and characteristics of the FDGM (1, 1, tα), and uses three groups of data to verify its effectiveness compared with that of other grey models. This paper forecasts the building energy consumption in China over the next five years based on the idea of metabolism. The results show that the FDGM (1, 1, tα) can be transformed into other grey models through parameter setting changes, so the new model has strong adaptability. The FDGM (1, 1, tα) is more reliable and effective than the other six compared grey models. From 2018 to 2022, the total energy consumption levels of civil buildings, urban civil buildings, and civil buildings specifically in Beijing will exhibit steady upward trends, with an average annual growth rate of 2.61%, 1.92%, and 0.78%, respectively. |
|---|