A Novel Framework Based on CNN-LSTM Neural Network for Prediction of Missing Values in Electricity Consumption Time-Series Datasets

Adopting Internet of Things (IoT)-based technologies in smart homes helps users analyze home applianceselectricity consumption for better overall cost monitoring. The IoT application like smart home system (SHS)could suffer from large missing values gaps due to several factors such as security attac...

Ausführliche Beschreibung

Gespeichert in:
Bibliographische Detailangaben
Veröffentlicht in:Journal of information processing systems 2022, 18(1), 73, pp.115-129
Hauptverfasser: Syed Nazir Hussain, Azlan Abd Aziz, Md. Jakir Hossen, Nor Azlina Ab Aziz, G. Ramana Murthy, Fajaruddin Bin Mustakim
Format: Artikel
Sprache:eng
Schlagworte:
Online-Zugang:Volltext
Tags: Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
Beschreibung
Zusammenfassung:Adopting Internet of Things (IoT)-based technologies in smart homes helps users analyze home applianceselectricity consumption for better overall cost monitoring. The IoT application like smart home system (SHS)could suffer from large missing values gaps due to several factors such as security attacks, sensor faults, orconnection errors. In this paper, a novel framework has been proposed to predict large gaps of missing valuesfrom the SHS home appliances electricity consumption time-series datasets. The framework follows a series ofsteps to detect, predict and reconstruct the input time-series datasets of missing values. A hybrid convolutionalneural network-long short term memory (CNN-LSTM) neural network used to forecast large missing valuesgaps. A comparative experiment has been conducted to evaluate the performance of hybrid CNN-LSTM withits single variant CNN and LSTM in forecasting missing values. The experimental results indicate a performancesuperiority of the CNN-LSTM model over the single CNN and LSTM neural networks. KCI Citation Count: 0
ISSN:1976-913X
2092-805X
DOI:10.3745/JIPS.04.0235