An overview of AMI data preprocessing to enhance the performance of load forecasting

@article{Quilumba2014AnOO,
  title={An overview of AMI data preprocessing to enhance the performance of load forecasting},
  author={Franklin L. Quilumba and Wei-Jen Lee and Heng Huang and David Yanshi Wang and Robert L. Szabados},
  journal={2014 IEEE Industry Application Society Annual Meeting},
  year={2014},
  pages={1-7}
}
Better understanding of actual customers' power consumption patterns is critical for improving load forecasting (LF) accuracy and efficient deployment of smart grid technologies to enhance operation, energy management, and planning of electric power systems. Though technical literature presented extensive methodologies and models to improve LF accuracy, most of them are based upon aggregated power consumption data at the system level with little or even no information regarding power… CONTINUE READING

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Mining Time Series Data

Data Mining and Knowledge Discovery Handbook • 2005

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