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Title: A sequential DNN based Baseline Energy Prediction Framework with Long term Error Mitigation

Abstract

In this paper,we aim to present a novel sequential framework containing two deep network architectures for baseline energy prediction, applied to a simulated commercial building dataset. We have observed that this sequential framework helps to improve long term prediction accuracy, there by mitigating prediction error accumulation over time. The proposed framework utilizes convolution layers to extract features from the input data space without changing spatial relations between variables. These features are memorized by tensor train based gated recurrent units for an accurate long-term prediction. Although, architecture comprising the amalgamation of convolution layers and memory cell have shown promising results in domains such as social media analysis, language modeling, video frame prediction and image recognition, this paper extends its scope to the context of energy applications. Furthermore, the addition of tensor train based gated recurrent units is motivated by the necessity of computational time reduction during the training process, there by making this framework more suitable for future in-field deployment. Lastly, results show the current framework outperforms other existing machine learning based methods, in both short-term and long-term prediction categories.

Authors:
ORCiD logo [1];  [1];  [1]
  1. BATTELLE (PACIFIC NW LAB)
Publication Date:
Research Org.:
Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1532524
Report Number(s):
PNNL-SA-141775
DOE Contract Number:  
AC05-76RL01830
Resource Type:
Conference
Resource Relation:
Conference: Proceedings of the Tenth ACM International Conference on Future Energy Systems (e-Energy 2019), June 25-28, 2019, Phoenix, AZ
Country of Publication:
United States
Language:
English

Citation Formats

Chakraborty, Indrasis, Chandan, Vikas, and Vrabie, Draguna L. A sequential DNN based Baseline Energy Prediction Framework with Long term Error Mitigation. United States: N. p., 2019. Web. doi:10.1145/3307772.3331027.
Chakraborty, Indrasis, Chandan, Vikas, & Vrabie, Draguna L. A sequential DNN based Baseline Energy Prediction Framework with Long term Error Mitigation. United States. doi:10.1145/3307772.3331027.
Chakraborty, Indrasis, Chandan, Vikas, and Vrabie, Draguna L. Fri . "A sequential DNN based Baseline Energy Prediction Framework with Long term Error Mitigation". United States. doi:10.1145/3307772.3331027.
@article{osti_1532524,
title = {A sequential DNN based Baseline Energy Prediction Framework with Long term Error Mitigation},
author = {Chakraborty, Indrasis and Chandan, Vikas and Vrabie, Draguna L.},
abstractNote = {In this paper,we aim to present a novel sequential framework containing two deep network architectures for baseline energy prediction, applied to a simulated commercial building dataset. We have observed that this sequential framework helps to improve long term prediction accuracy, there by mitigating prediction error accumulation over time. The proposed framework utilizes convolution layers to extract features from the input data space without changing spatial relations between variables. These features are memorized by tensor train based gated recurrent units for an accurate long-term prediction. Although, architecture comprising the amalgamation of convolution layers and memory cell have shown promising results in domains such as social media analysis, language modeling, video frame prediction and image recognition, this paper extends its scope to the context of energy applications. Furthermore, the addition of tensor train based gated recurrent units is motivated by the necessity of computational time reduction during the training process, there by making this framework more suitable for future in-field deployment. Lastly, results show the current framework outperforms other existing machine learning based methods, in both short-term and long-term prediction categories.},
doi = {10.1145/3307772.3331027},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {6}
}

Conference:
Other availability
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