A Scoping Review of Deep Neural Networks for Electric Load Forecasting

Nicolai Bo Vanting*, Zheng Ma, Bo Nørregaard Jørgensen

*Kontaktforfatter

Publikation: Bidrag til tidsskriftTidsskriftartikelForskningpeer review

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Abstract

The increasing dependency on electricity and demand for renewable energy sources means that distributed system operators face new challenges in their grid. Accurate forecasts of electric load can solve these challenges. In recent years deep neural networks have become increasingly popular in research, and researchers have carried out many experiments to create the most accurate deep learning models. Players in the energy sector can exploit the increasing amount of energy-related data collected from smart meters to improve the grid’s operating quality. This review investigates state-of-the-art methodologies relating to energy load forecasting using deep neural networks. A thorough literature search is conducted, which outlines and analyses essential aspects regarding deep learning load forecasts in the energy domain. The literature suggests two main perspectives: demand-side management and grid control on the supply side. Each perspective has multiple applications with its challenges to achieve accurate forecasts; households, buildings, and grids. This paper recommends using a hybrid deep learning multivariate model consisting of a convolutional and recurrent neural network based on the scoping review. The suggested input variables should be historical consumption, weather, and day features. Combining the convolutional and recurrent networks ensures that the model learns as many repeating patterns and features in the data as possible.
OriginalsprogEngelsk
Artikelnummer49
TidsskriftEnergy Informatics
Vol/bind4
Udgave nummerSuppl. 2
Antal sider13
ISSN2520-8942
DOI
StatusUdgivet - 2021
BegivenhedEnergy Informatics.Academy Conference Asia - China, Beijing, Kina
Varighed: 29. maj 202130. maj 2021
Konferencens nummer: 1
https://www.energyinformatics.academy/eia-asia-2021-conference

Konference

KonferenceEnergy Informatics.Academy Conference Asia
Nummer1
LokationChina
Land/OmrådeKina
ByBeijing
Periode29/05/202130/05/2021
Internetadresse

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