eprintid: 16336
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/63/36
datestamp: 2023-12-19 03:22:52
lastmod: 2023-12-19 03:22:52
status_changed: 2023-12-19 03:06:04
type: article
metadata_visibility: show
creators_name: Muneer, A.
creators_name: Ali, R.F.
creators_name: Almaghthawi, A.
creators_name: Taib, S.M.
creators_name: Alghamdi, A.
creators_name: Ghaleb, E.A.A.
title: Short term residential load forecasting using long short-term memory recurrent neural network
ispublished: pub
note: cited By 5
abstract: Load forecasting plays an essential role in power system planning. The efficiency and reliability of the whole power system can be increased with proper planning and organization. Residential load forecasting is indispensable due to its increasing role in the smart grid environment. Nowadays, smart meters can be deployed at the residential level for collecting historical data consumption of residents. Although the employment of smart meters ensures large data availability, the inconsistency of load data makes it challenging and taxing to forecast accurately. Therefore, the traditional forecasting techniques may not suffice the purpose. However, a deep learning forecasting network-based long short-term memory (LSTM) is proposed in this paper. The powerful nonlinear mapping capabilities of RNN in time series make it effective along with the higher learning capabilities of long sequences of LSTM. The proposed method is tested and validated through available real-world data sets. A comparison of LSTM is then made with two traditionally available techniques, exponential smoothing and auto-regressive integrated moving average model (ARIMA). Real data from 12 houses over three months is used to evaluate and validate the performance of load forecasts performed using the three mentioned techniques. LSTM model has achieved the best results due to its higher capability of memorizing large data in time series-based predictions. © 2022 Institute of Advanced Engineering and Science. All rights reserved.
date: 2022
publisher: Institute of Advanced Engineering and Science
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85135186001&doi=10.11591%2fijece.v12i5.pp5589-5599&partnerID=40&md5=0355fd52207e631ff29367b95d52205c
id_number: 10.11591/ijece.v12i5.pp5589-5599
full_text_status: none
publication: International Journal of Electrical and Computer Engineering
volume: 12
number: 5
pagerange: 5589-5599
refereed: TRUE
issn: 20888708
citation:   Muneer, A. and Ali, R.F. and Almaghthawi, A. and Taib, S.M. and Alghamdi, A. and Ghaleb, E.A.A.  (2022) Short term residential load forecasting using long short-term memory recurrent neural network.  International Journal of Electrical and Computer Engineering, 12 (5).  pp. 5589-5599.  ISSN 20888708