Title: Energy-saving smart city: an edge computing-based renovation and upgrading scheme for old residential areas

Authors: Zhi Zhao

Addresses: Wenzhou Polytechnic, Wenzhou, China

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.

Keywords: energy-saving; smart city; neural network; feature extraction.

DOI: 10.1504/IJCAT.2023.132099

International Journal of Computer Applications in Technology, 2023 Vol.71 No.3, pp.251 - 257

Received: 26 Apr 2022
Received in revised form: 06 Jun 2022
Accepted: 12 Jun 2022

Published online: 11 Jul 2023 *

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