| Title |
Energy-saving smart city: an edge computing-based renovation and upgrading scheme for old residential areas |
| ID_Doc |
38391 |
| Authors |
Zhao, Z |
| Title |
Energy-saving smart city: an edge computing-based renovation and upgrading scheme for old residential areas |
| Year |
2023 |
| Published |
International Journal Of Computer Applications In Technology, 71, 3 |
| DOI |
10.1504/IJCAT.2023.132099 |
| Abstract |
The renovation of old communities has become an important issue in the current development of new urbanisation. The development of edge computing provides a powerful pillar for the energy-saving renovation of old residential areas. Accurately predicting the electricity usage can provide a more personalised electricity consumption plan for the users of the community, thus making the overall energy saving possible. Therefore, we propose a power prediction model based on the stacking model to provide a strategy for saving power and energy in old communities. First, we adopt the word2vec algorithm to extract the discrete feature word vector and to capture the co-occurrence relationship from the discrete feature. Second, we adopt a neural network model to perform feature extraction on for continuous features. Third, we design a power prediction model based on the stacking model by using XGBoost algorithm, LightGBM algorithm and linear regression. The experimental results prove that the method proposed in this paper has good prediction performance. |
| Author Keywords |
energy-saving; smart city; neural network; feature extraction |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Emerging Sources Citation Index (ESCI) |
| EID |
WOS:001027902600010 |
| WoS Category |
Computer Science, Interdisciplinary Applications |
| Research Area |
Computer Science |
| PDF |
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