Conference paper
A Prediction-based Smart Meter Data Generator
University College of Northern Denmark1
Department of Management Engineering, Technical University of Denmark2
Systems Analysis, Department of Management Engineering, Technical University of Denmark3
DTU Climate Centre, Systems Analysis, Department of Management Engineering, Technical University of Denmark4
Technical University of Denmark5
CITIES - Centre for IT-Intelligent Energy Systems, Centers, Technical University of Denmark6
With the prevalence of cloud computing and In-ternet of Things (IoT), smart meters have become one of the main components of smart city strategy. Smart meters generate large amounts of fine-grained data that is used to provide useful information to consumers and utility companies for decision-making.
Now-a-days, smart meter analytics systems consist of analytical algorithms that process massive amounts of data. These analytics algorithms require ample amounts of realistic data for testing and verification purposes. However, it is usually difficult to obtain adequate amounts of realistic data, mainly due to privacy issues.
This paper proposes a smart meter data generator that can generate realistic energy consumption data by making use of a small real-world dataset as seed. The generator generates data using a prediction-based method that depends on historical energy consumption patterns along with Gaussian white noise.
In this paper, we comprehensively evaluate the efficiency and effectiveness of the proposed method based on a real-world energy data set.
| Language: | English |
|---|---|
| Publisher: | IEEE |
| Year: | 2016 |
| Pages: | 173-180 |
| Proceedings: | 2016 19th International Conference on Network-Based Information SystemsInternational Conference on Network-Based Information Systems |
| ISBN: | 1509009795 , 1509009809 , 9781509009794 and 9781509009800 |
| ISSN: | 21570426 and 21570418 |
| Types: | Conference paper |
| DOI: | 10.1109/NBiS.2016.15 |
| ORCIDs: | Liu, Xiufeng |
Data models Energy consumption Gaussian white noise Generators Internet of Things IoT Mathematical model Predictive models Smart meters Time series analysis cloud computing computerised instrumentation data analysis data generator data processing decision making energy consumption energy consumption data historical energy consumption patterns prediction-based smart meter data generator real-world energy data set smart cities smart city strategy smart meter smart meter analytics systems smart meters time-series
