Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Recurrent neural networks in electricity load forecasting
KTH, School of Electrical Engineering and Computer Science (EECS).
2018 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Rekurrenta neurala nätverk i prognostisering av elkonsumtion (Swedish)
Abstract [en]

In this thesis two main studies are conducted to compare the predictive capabilities of feed-forward neural networks (FFNN) and long short-term memory networks (LSTM) in electricity load forecasting.

The first study compares univariate networks using past electricity load, as well as multivariate networks using past electricity load and air temperature, in day-ahead load forecasting using varying lookback periods and sparsity of past observations. The second study compares FFNNs and LSTMs of different complexities (i.e. network sizes) when restrictions imposed by limitations of the real world are taken into consideration.

No significant differences are found between the predictive performances of the two neural network approaches. However, adding air temperature as extra input to the LSTM is found to significantly decrease its performance. Furthermore, the predictive performance of the FFNN is found to significantly decrease as the network complexity grows, while the predictive performance of the LSTM is found to increase as the network complexity grows. All the findings considered, we do not find that there is enough evidence in favour of the LSTM in electricity load forecasting.

Abstract [sv]

I denna uppsats beskrivs två studier som jämför feed-forward neurala nätverk (FFNN) och long short-term memory neurala nätverk (LSTM) i prognostisering av elkonsumtion.

I den första studien undersöks univariata modeller som använder tidigare elkonsumtion, och flervariata modeller som använder tidigare elkonsumtion och temperaturmätningar, för att göra prognoser av elkonsumtion för nästa dag. Hur långt bak i tiden tidigare information hämtas ifrån samt upplösningen av tidigare information varieras. I den andra studien undersöks FFNN- och LSTM-modeller med praktiska begränsningar såsom tillgänglighet av data i åtanke. Även storleken av nätverken varieras.

I studierna finnes ingen skillnad mellan FFNN- och LSTM-modellernas förmåga att prognostisera elkonsumtion. Däremot minskar FFNN-modellens förmåga att prognostisera elkonsumtion då storleken av modellen ökar. Å andra sidan ökar LSTM-modellens förmåga då storkelen ökar. Utifrån dessa resultat anser vi inte att det finns tillräckligt med bevis till förmån för LSTM-modeller i prognostisering av elkonsumtion.

Place, publisher, year, edition, pages
2018.
Series
TRITA-EECS-EX ; 2018:488
Keywords [en]
Recurrent neural networks electricity load forecasting lstm renewable energy
National Category
Computer Sciences
Identifiers
URN: urn:nbn:se:kth:diva-233254OAI: oai:DiVA.org:kth-233254DiVA, id: diva2:1238889
External cooperation
Expektra
Educational program
Master of Science in Engineering - Computer Science and Technology
Supervisors
Examiners
Available from: 2018-09-19 Created: 2018-08-14 Last updated: 2018-09-19Bibliographically approved

Open Access in DiVA

fulltext(1737 kB)130 downloads
File information
File name FULLTEXT01.pdfFile size 1737 kBChecksum SHA-512
ea47ab45d44a015f6dee3cd89d41e0e9d9bff9b1eea28dfa2d3be92b10f6e09c33f2c461ed52c483f4ccd1553d632d1516ad5d610ab5eeef037133a6098c0a21
Type fulltextMimetype application/pdf

By organisation
School of Electrical Engineering and Computer Science (EECS)
Computer Sciences

Search outside of DiVA

GoogleGoogle Scholar
Total: 130 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 689 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf