• DOI:10.1109/TKDE.2014.2327022
Corpus ID: 14923492

Holistic Measures for Evaluating Prediction Models in Smart Grids

@article{Aman2015HolisticMF,
  title={Holistic Measures for Evaluating Prediction Models in Smart Grids},
  author={S. Aman and Y. Simmhan and V. Prasanna},
  journal={IEEE Transactions on Knowledge and Data Engineering},
  year={2015},
  volume={27},
  pages={475-488}
}

S. Aman, Y. Simmhan, V. Prasanna

Published 2015

Computer Science

IEEE Transactions on Knowledge and Data Engineering

The performance of prediction models is often based on “abstract metrics” that estimate the model's ability to limit residual errors between the observed and predicted values. However, meaningful evaluation and selection of prediction models for end-user domains requires holistic and application-sensitive performance measures. Inspired by energy consumption prediction models used in the emerging “big data” domain of Smart Power Grids, we propose a suite of performance measures to rationally… CONTINUE READING

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