Big Data Analytics for Demand Response: Clustering Over Space and Time
- Univ. of Southern California, Los Angeles, CA (United States)
- Nirma Univ., Gujarat (India)
The pervasive deployment of advanced sensing infrastructure in Cyber-Physical systems, such as the Smart Grid, has resulted in an unprecedented data explosion. Such data exhibit both large volumes and high velocity characteristics, two of the three pillars of Big Data, and have a time-series notion as datasets in this context typically consist of successive measurements made over a time interval. Time-series data can be valuable for data mining and analytics tasks such as identifying the “right” customers among a diverse population, to target for Demand Response programs. However, time series are challenging to mine due to their high dimensionality. In this paper, we motivate this problem using a real application from the smart grid domain. We explore novel representations of time-series data for BigData analytics, and propose a clustering technique for determining natural segmentation of customers and identification of temporal consumption patterns. Our method is generizable to large-scale, real-world scenarios, without making any assumptions about the data. We evaluate our technique using real datasets from smart meters, totaling ~ 18,200,000 data points, and show the efficacy of our technique in efficiency detecting the number of optimal number of clusters.
- Research Organization:
- City of Los Angeles Department, CA (United States)
- Sponsoring Organization:
- USDOE Office of Electricity (OE)
- DOE Contract Number:
- OE0000192
- OSTI ID:
- 1332333
- Report Number(s):
- DOE-USC-00192-75
- Resource Relation:
- Conference: "2015 IEEE International Conference on Big Data" , Santa Clara, CA (United States), 29 Oct-1 Nov 2015
- Country of Publication:
- United States
- Language:
- English
Similar Records
Prediction Models for Dynamic Demand Response
Challenge Online Time Series Clustering For Demand Response A Theory to Break the ‘Curse of Dimensionality'