ub.xmlui.mirage2.page-structure.muninLogoub.xmlui.mirage2.page-structure.openResearchArchiveLogo
    • EnglishEnglish
    • norsknorsk
  • Velg spraaknorsk 
    • EnglishEnglish
    • norsknorsk
  • Administrasjon/UB
Vis innførsel 
  •   Hjem
  • Fakultet for naturvitenskap og teknologi
  • Institutt for fysikk og teknologi
  • Artikler, rapporter og annet (fysikk og teknologi)
  • Vis innførsel
  •   Hjem
  • Fakultet for naturvitenskap og teknologi
  • Institutt for fysikk og teknologi
  • Artikler, rapporter og annet (fysikk og teknologi)
  • Vis innførsel
JavaScript is disabled for your browser. Some features of this site may not work without it.

Short-Term Load Forecasting with Missing Data using Dilated Recurrent Attention Networks

Permanent lenke
https://hdl.handle.net/10037/19949
Thumbnail
Åpne
article.pdf (651.2Kb)
Publisert versjon (PDF)
Dato
2020-02-06
Type
Conference object
Konferansebidrag

Forfatter
Choi, Changkyu; Bianchi, Filippo Maria; Kampffmeyer, Michael; Jenssen, Robert
Sammendrag
Forecasting the dynamics of time-varying systems is essential to maintaining the sustainability of the systems. Recent studies have discovered that Recurrent Neural Networks(RNN) applied in the forecasting tasks outperform conventional models that include AutoRegressive Integrated Moving Average(ARIMA). However, due to the structural limitation of vanilla RNN which holds unit-length internal connections, learning the representation of time series with missing data can be severely biased.

The goal of this paper is to provide a robust RNN architecture against the bias from missing data.

We propose Dilated Recurrent Attention Networks (DRAN).

The proposed model has a stacked structure of multiple RNNs which layer of each having a different length of internal connections.

This structure allows incorporating previous information at different time scales.

DRAN updates its state by a weighted average of the layers. In order to focus more on the layer that carries reliable information against bias from missing data, it leverages attention mechanism which learns the distribution of attention weights among the layers. We report that our model outperforms conventional ones with respect to the forecast accuracy from two benchmark datasets, including a real-world electricity load dataset.

Beskrivelse
Poster presented at the Northern Lights Deep Learning Workshop (NLDL) 2020, 19.01.20 - 21.01.20, UiT The Arctic University of Norway, Tromsø
Forlag
Septentrio Academic Publishing
Sitering
Choi, C., Bianchi, F.M., Kampffmeyer, M. & Jenssen, R. (2020). Short-Term Load Forecasting with Missing Data using Dilated Recurrent Attention Networks. Poster presentation at the Northern Lights Deep Learning Workshop (NLDL) 2020, 19.01.20 - 21.01.20, UiT The Arctic University of Norway, Tromsø
Metadata
Vis full innførsel
Samlinger
  • Artikler, rapporter og annet (fysikk og teknologi) [1057]
Copyright 2020 The Author(s)

Bla

Bla i hele MuninEnheter og samlingerForfatterlisteTittelDatoBla i denne samlingenForfatterlisteTittelDato
Logg inn

Statistikk

Antall visninger
UiT

Munin bygger på DSpace

UiT Norges Arktiske Universitet
Universitetsbiblioteket
uit.no/ub - munin@ub.uit.no

Tilgjengelighetserklæring