- Tytuł:
- An overview of deep learning techniques for short-term electricity load forecasting
- Autorzy:
-
Adewuyi, Saheed
Aina, Segun
Uzunuigbe, Moses
Lawal, Aderonke
Oluwaranti, Adeniran - Powiązania:
- https://bibliotekanauki.pl/articles/117932.pdf
- Data publikacji:
- 2019
- Wydawca:
- Polskie Towarzystwo Promocji Wiedzy
- Tematy:
-
Short-term Load Forecasting
Deep Learning Architectures
RNN
LSTM
CNN
SAE
prognozowanie obciążenia krótkoterminowego
architektura głębokiego uczenia - Opis:
- This paper presents an overview of some Deep Learning (DL) techniques applicable to forecasting electricity consumptions, especially in the short-term horizon. The paper introduced key parts of four DL architectures including the RNN, LSTM, CNN and SAE, which are recently adopted in implementing Short-term (electricity) Load Forecasting problems. It further presented a model approach for solving such problems. The eventual implication of the study is to present an insightful direction about concepts of the DL methods for forecasting electricity loads in the short-term period, especially to a potential researcher in quest of solving similar problems.
- Źródło:
-
Applied Computer Science; 2019, 15, 4; 75-92
1895-3735 - Pojawia się w:
- Applied Computer Science
- Dostawca treści:
- Biblioteka Nauki
Artykuł