- Tytuł:
- A deep learning model for electricity demand forecasting based on a tropical data
- Autorzy:
-
Adewuyi, Saheed A.
Aina, Segun
Oluwaranti, Adeniran I. - Tematy:
-
Electricity Demand Forecasting
STLF
Deep Learning Techniques
LSTM
CNN
MLP
prognozowanie zapotrzebowania na energię elektryczną
techniki głębokiego uczenia - Pokaż więcej
- Wydawca:
- Polskie Towarzystwo Promocji Wiedzy
- Powiązania:
- https://bibliotekanauki.pl/articles/118123.pdf Link otwiera się w nowym oknie
- Opis:
- Electricity demand forecasting is a term used for prediction of users’ consumption on the grid ahead of actual demand. It is very important to all power stakeholders across levels. The power players employ electricity demand forecasting for sundry purposes. Moreover, the government’s policy on its market deregulation has greatly amplified its essence. Despite numerous studies on the subject using certain classical approaches, there exists an opportunity for exploration of more sophisticated methods such as the deep learning (DL) techniques. Successful researches about DL applications to computer vision, speech recognition, and acoustic computing problems are motivation. However, such researches are not sufficiently exploited for electricity demand forecasting using DL methods. In this paper, we considered specific DL techniques (LSTM, CNN, and MLP) to short-term load forecasting problems, using tropical institutional data obtained from a Transmission Company. We also test how accurate are predictions across the techniques. Our results relatively revealed models appropriateness for the problem.
- Dostawca treści:
- Biblioteka Nauki
Artykuł