Open AccessThe Predictor for Urban Buildings’ Hourly Electricity Consumption
Author(s) -
Shubing Shan,
Bisong Cao,
Zhìqiáng Wú
Publication year - 2019
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/322/1/012007
Subject(s) - electricity , consumption (sociology) , software deployment , computer science , environmental economics , energy consumption , mains electricity , engineering , economics , social science , voltage , sociology , electrical engineering , operating system
The accurate prediction approach of urban buildings’ electricity consumption is an important foundation for smart urban energy management. It provides the decision basis for electricity deployment at peak times. This paper presents a knowledge graph of urban building electricity consumption called ECKG. It provides an effective way to obtain the influencing factors of buildings’ electricity consumption. In addition, in order to improve the accuracy of prediction, this paper proposes the logarithmic electricity consumption gravity model and error correction model based on data selection. We use 17520 hours’ electricity consumption of a five-star hotel building in Shanghai, China as the study case, and 8 common models as benchmarks to conduct the comparisons. Our approach outperforms all benchmarks in terms of average accuracy.