Fast Change Point Detection for Electricity Market Analysis
Electricity is a vital part of our daily life; therefore it is important to avoid irregularities such as the California Electricity Crisis of 2000 and 2001. In this work, we seek to predict anomalies using advanced machine learning algorithms. These algorithms are effective, but computationally expensive, especially if we plan to apply them on hourly electricity market data covering a number of years. To address this challenge, we significantly accelerate the computation of the Gaussian Process (GP) for time series data. In the context of a Change Point Detection (CPD) algorithm, we reduce its computational complexity from O($$n^{5}$$) to O($$n^{2}$$). Our efficient algorithm makes it possible to compute the Change Points using the hourly price data from the California Electricity Crisis. By comparing the detected Change Points with known events, we show that the Change Point Detection algorithm is indeed effective in detecting signals preceding major events.
- Research Organization:
- Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
- Sponsoring Organization:
- USDOE Office of Science (SC)
- DOE Contract Number:
- DE-AC02-05CH11231
- OSTI ID:
- 1165211
- Report Number(s):
- LBNL-6388E
- Resource Relation:
- Conference: Workshop on Scalable Machine Learning: Theory and Applications, Santa Clara, CA, USA, October 6, 2013
- Country of Publication:
- United States
- Language:
- English
Similar Records
The Prospect of using Three-Dimensional Earth Models To Improve Nuclear Explosion Monitoring and Ground Motion Hazard Assessment
Scalable time series change detection for biomass monitoring using gaussian process