Scalable Prediction of Energy Consumption using Incremental Time Series Clustering
Time series datasets are a canonical form of high velocity Big Data, and often generated by pervasive sensors, such as found in smart infrastructure. Performing predictive analytics on time series data can be computationally complex, and requires approximation techniques. In this paper, we motivate this problem using a real application from the smart grid domain. We propose an incremental clustering technique, along with a novel affinity score for determining cluster similarity, which help reduce the prediction error for cumulative time series within a cluster. We evaluate this technique, along with optimizations, using real datasets from smart meters, totaling ~700,000 data points, and show the efficacy of our techniques in improving the prediction error of time series data within polynomial time.
- Research Organization:
- City of Los Angeles Department
- Sponsoring Organization:
- USDOE Office of Electricity (OE)
- DOE Contract Number:
- OE0000192
- OSTI ID:
- 1332550
- Report Number(s):
- DOE-USC-00192-95
- Resource Relation:
- Conference: IEEE International Conference on Big Data - Workshop on Big Data and Smarter Cities Santa Clara, CA, USA 9-Oct-13
- Country of Publication:
- United States
- Language:
- English
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