Prediction models for dynamic demand response: Requirements, challenges, and insights

@article{Aman2015PredictionMF,
  title={Prediction models for dynamic demand response: Requirements, challenges, and insights},
  author={S. Aman and M. Fr{\^i}ncu and C. Chelmis and M. Noor and Y. Simmhan and V. Prasanna},
  journal={2015 IEEE International Conference on Smart Grid Communications (SmartGridComm)},
  year={2015},
  pages={338-343}
}
  • S. Aman, M. Frîncu, +3 authors V. Prasanna
  • Published 2015
  • Engineering, Computer Science
  • 2015 IEEE International Conference on Smart Grid Communications (SmartGridComm)
    • 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 (D2R) process. While existing… CONTINUE READING
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    References

    SHOWING 1-10 OF 38 REFERENCES
    Empirical Comparison of Prediction Methods for Electricity Consumption Forecasting
    • 23
    • PDF
    Understanding the Effect of Baseline Modeling Implementation Choices on Analysis of Demand Response Performance
    • 11
    • PDF
    Holistic Measures for Evaluating Prediction Models in Smart Grids
    • 46
    • PDF
    A framework for baseline load estimation in demand response: Data mining approach
    • 24
    • PDF
    Modeling and forecasting hourly electric load by multiple linear regression with interactions
    • 110
    Data-driven forecasting algorithms for building energy consumption
    • H. Noh, R. Rajagopal
    • Computer Science, Engineering
    • Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring
    • 2013
    • 21
    • PDF
    Short-Term Load Forecasting Based on a Semi-Parametric Additive Model
    • 349
    • PDF
    Toward data-driven demand-response optimization in a campus microgrid
    • 18
    • PDF
    A Large-Scale Study on Predicting and Contextualizing Building Energy Usage
    • 121
    • PDF
    The potential of smart home sensors in forecasting household electricity demand
    • 34
    • Highly Influential
    • PDF