Abstract
For decades, electricity customers have been treated as mere recipients of electricity in vertically integrated power systems. However, as customers have widely adopted distributed energy resources and other forms of customer participation in active dispatch (such as demand response) have taken shape, the value of mining knowledge from customer behavior patterns and using it for power system operation is increasing. Further, the variability of renewable energy resources has been considered a liability to the grid. However, electricity consumption has shown the same level of variability and uncertainty, and this is sometimes overlooked. This article investigates data analytics and forecasting methods to identify correlations between electricity consumption behavior and distributed photovoltaic (PV) output. The forecasting results feed into a predictive energy management system that optimizes energy consumption in the near future to balance customer demand and power system needs.
Original language | American English |
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Article number | 8012304 |
Pages (from-to) | 59-63 |
Number of pages | 5 |
Journal | IEEE Intelligent Systems |
Volume | 32 |
Issue number | 4 |
DOIs | |
State | Published - 2017 |
Bibliographical note
Publisher Copyright:© 2001-2011 IEEE.
NREL Publication Number
- NREL/JA-5D00-70064
Keywords
- abnormal days
- artificial intelligence
- electricity consumption behavior
- intelligent systems
- machine learning
- online forecast
- predictive distribution energy management
- time series analysis
- very-short-term energy forecast