A Review of Deep Learning Methods Applied on Load Forecasting

Abdulaziz Almalaq; George Edwards

doi:10.1109/ICMLA.2017.0-110

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Conference proceeding

A Review of Deep Learning Methods Applied on Load Forecasting

Abdulaziz Almalaq and George Edwards

2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA), Vol.2017-, pp.511-516

12/2017

DOI: https://doi.org/10.1109/ICMLA.2017.0-110

Abstract

Artificial neural networks

Computational modeling

Computer architecture

Forecasting

Learning (artificial intelligence)

Load forecasting

Load modeling

Logic gates

Machine learning

Smart grids

The utility industry has invested widely in smart grid (SG) over the past decade. They considered it the future electrical grid while the information and electricity are delivered in two-way flow. SG has many Artificial Intelligence (AI) applications such as Artificial Neural Network (ANN), Machine Learning (ML) and Deep Learning (DL). Recently, DL has been a hot topic for AI applications in many fields such as time series load forecasting. This paper introduces the common algorithms of DL in the literature applied to load forecasting problems in the SG and power systems. The intention of this survey is to explore the different applications of DL that are used in the power systems and smart grid load forecasting. In addition, it compares the accuracy results RMSE and MAE for the reviewed applications and shows the use of convolutional neural network CNN with k-means algorithm had a great percentage of reduction in terms of RMSE.

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Details

Title: A Review of Deep Learning Methods Applied on Load Forecasting
Creators - without role: Abdulaziz Almalaq - University of Denver
George Edwards - University of Denver
Publication Details: 2017 16th IEEE International Conference on Machine Learning and Applications (ICMLA), Vol.2017-, pp.511-516
Publisher: IEEE
Identifiers: 9931981908331
Academic Unit: University Ha'il
Language: English
Resource Type: Conference proceeding