Journal article
Two approaches for synthesizing scalable residential energy consumption data
Sustainability, Department of Technology, Management and Economics, Technical University of Denmark1
Energy Systems Analysis, Sustainability, Department of Technology, Management and Economics, Technical University of Denmark2
Department of Technology, Management and Economics, Technical University of Denmark3
University College of Northern Denmark4
University of Technology Sydney5
Department of Civil Engineering, Technical University of Denmark6
Energy and Services, Department of Civil Engineering, Technical University of Denmark7
Many fields require scalable and detailed energy consumption data for different study purposes. However, due to privacy issues, it is often difficult to obtain sufficiently large datasets. This paper proposes two different methods for synthesizing fine-grained energy consumption data for residential households, namely a regression-based method and a probability-based method.
They each use a supervised machine learning method, which trains models with a relatively small real-world dataset and then generates large-scale time series based on the models. This paper describes the two methods in details, including data generation process, optimization techniques, and parallel data generation.
This paper evaluates the performance of the two methods, which compare the resulting consumption profiles with real-world data, including patterns, statistics, and parallel data generation in the cluster. The results demonstrate the effectiveness of the proposed methods and their efficiency in generating large-scale datasets.
Language: | English |
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Year: | 2019 |
Pages: | 586-600 |
ISSN: | 18727115 and 0167739x |
Types: | Journal article |
DOI: | 10.1016/j.future.2019.01.045 |
ORCIDs: | Liu, Xiufeng , Li, Rongling and Nielsen, Per Sieverts |