Prediction Models for Dynamic Demand Response Requirements, Challenges, and Insights

Aman, Saima and Frincu, Marc E and Chelmis, Charalampos and Noor, Muhammad and Simmhan, Yogesh and Prasanna, Viktor K (2015) Prediction Models for Dynamic Demand Response Requirements, Challenges, and Insights. In: IEEE International Conference on Smart Grid Communications (SmartGridComm), NOV 01-05, 2015, Miami, FL, pp. 338-343.

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Abstract

As Smart Grids move closer to dynamic curtailment programs, Demand Response ( DR) events will become necessary not only on fixed time intervals and weekdays predetermined by static policies, but also during changing decision periods and weekends to react to real-time demand signals. Unique challenges arise in this context vis-a-vis demand prediction and curtailment estimation and the transformation of such tasks into an automated, efficient dynamic demand response ( (DR)-R-2) process. While existing work has concentrated on increasing the accuracy of prediction models for DR, there is a lack of studies for prediction models for (DR)-R-2, which we address in this paper. Our first contribution is the formal definition of (DR)-R-2, and the description of its challenges and requirements. Our second contribution is a feasibility analysis of very-short-term prediction of electricity consumption for (DR)-R-2 over a diverse, large-scale dataset that includes both small residential customers and large buildings. Our third, and major contribution is a set of insights into the predictability of electricity consumption in the context of (DR)-R-2. Specifically, we focus on prediction models that can operate at a very small data granularity ( here 15-min intervals), for both weekdays and weekends - all conditions that characterize scenarios for (DR)-R-2. We find that short-term time series and simple averaging models used by Independent Service Operators and utilities achieve superior prediction accuracy. We also observe that workdays are more predictable than weekends and holiday. Also, smaller customers have large variation in consumption and are less predictable than larger buildings. Key implications of our findings are that better models are required for small customers and for non-workdays, both of which are critical for (DR)-R-2. Also, prediction models require just few days' worth of data indicating that small amounts of historical training data can be used to make reliable predictions, simplifying the complexity of big data challenge associated with (DR)-R-2.

Item Type:	Conference Proceedings
Additional Information:	Copy right for this article belongs to the IEEE, 345 E 47TH ST, NEW YORK, NY 10017 USA
Department/Centre:	Division of Interdisciplinary Sciences > Supercomputer Education & Research Centre
Date Deposited:	08 Oct 2016 06:45
Last Modified:	08 Oct 2016 06:45
URI:	http://eprints.iisc.ac.in/id/eprint/54774

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