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Unearthing Details of Time Series of Load: A Dual-scale Input Structured LSTM Approach

Published: 18 June 2020 Publication History

Abstract

Ultrashort term load forecasting for fine-grained residential load has been a challenge due to the uncertainty of residential electricity consumption behaviors. It is a good idea to mine more power consumption details for precise forecasting. Long short-term memory network (LSTM) can do well in characterizing general load shapes referring to daily life routine, but is limited in characterizing peaks and valleys referring to detailed knowledge lying in event-driven activities. To unearth more details, a new input structure named event window (EW) is designed based on reliable analysis that explains a symbiotic relationship between randomness and determinacy of household consumptive activities. Combining an EW structured LSTM to the conventional LSTM, this paper proposes a dual-scale input structured LSTM ultrashort term load forecaster (DILF). The DILF shows satisfactory increased performance in forecasting accuracy in the experiment with an increase in the proportion of training data carrying details led by the synthetic input. More understanding is delivered via cases analyses on the two original networks, i.e., conventional LSTM and pure EW structured LSTM. Finally, applicability of the dual-scale input structure to other neural networks is verified in the experiment.

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cover image ACM Other conferences
e-Energy '20: Proceedings of the Eleventh ACM International Conference on Future Energy Systems
June 2020
601 pages
ISBN:9781450380096
DOI:10.1145/3396851
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 18 June 2020

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Author Tags

  1. Deep learning
  2. LSTM
  3. feature engineering
  4. residential load
  5. ultrashort term load forecasting

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  • Research-article
  • Research
  • Refereed limited

Funding Sources

  • the National Key Research and Development Program of China
  • Key Project of Shanghai Science and Technology Committee
  • Shanghai Sailing Program

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e-Energy '20
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e-Energy '20 Paper Acceptance Rate 77 of 173 submissions, 45%;
Overall Acceptance Rate 160 of 446 submissions, 36%

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