| Title |
EMLP: short-term gas load forecasting based on ensemble multilayer perceptron with adaptive weight correction |
| ID_Doc |
41026 |
| Authors |
Li, FY; Sun, M |
| Title |
EMLP: short-term gas load forecasting based on ensemble multilayer perceptron with adaptive weight correction |
| Year |
2021 |
| Published |
Mathematical Biosciences And Engineering, 18, 2 |
| DOI |
10.3934/mbe.2021082 |
| Abstract |
This paper tackles a recent challenge in smart city that how to improve the accuracy of short-term natural gas load forecasting. Existing works on natural gas forecasting mostly reply on a combined forecasting model by simply integrating several single-forecasting models. However, due to the existence of redundant single-forecasting models, these works may not attain a higher prediction accuracy. To address the problem, we design a new natural gas load forecasting scheme based on ensemble multilayer perceptron (EMLP) with adaptive weight correction. Our method firstly normalizes multi-source data as original data set, which is further segmented by a window model. Then, the abnormal data is removed and subsequently interpolated to form a complete normalized data set. Furthermore, we integrate a series of multilayer perceptron (MLP) network to construct an ensemble forecasting model. An adaptive weight correction function is introduced to dynamically modify the weight of the previous predicted result. Since the correction function can match well the volatility characteristics of load data, the prediction accuracy is significantly improved. Extensive experiments demonstrate that our method outperforms existing state-of-the-art load forecasting schemes in terms of the prediction accuracy and stability. |
| Author Keywords |
load forecasting; ensemble learning; multilayer perceptron; smart city |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:000656821600020 |
| WoS Category |
Mathematical & Computational Biology |
| Research Area |
Mathematical & Computational Biology |
| PDF |
https://doi.org/10.3934/mbe.2021082
|