Please use this identifier to cite or link to this item: https://gnanaganga.inflibnet.ac.in:8443/jspui/handle/123456789/766
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dc.contributor.authorMurthy, Gajula Ramana-
dc.date.accessioned2023-05-24T14:01:51Z-
dc.date.available2023-05-24T14:01:51Z-
dc.date.issued2022-02-01-
dc.identifier.urihttps://doi.org/10.3745/JIPS.04.0235-
dc.identifier.urihttp://gnanaganga.inflibnet.ac.in:8080/jspui/handle/123456789/766-
dc.description.abstractAdopting Internet of Things (IoT)-based technologies in smart homes helps users analyze home appliances electricity 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, or connection errors. In this paper, a novel framework has been proposed to predict large gaps of missing values from the SHS home appliances electricity consumption time-series datasets. The framework follows a series of steps to detect, predict and reconstruct the input time-series datasets of missing values. A hybrid convolutional neural network-long short term memory (CNN-LSTM) neural network used to forecast large missing values gaps. A comparative experiment has been conducted to evaluate the performance of hybrid CNN-LSTM with its single variant CNN and LSTM in forecasting missing values. The experimental results indicate a performance superiority of the CNN-LSTM model over the single CNN and LSTM neural networks.en_US
dc.language.isoenen_US
dc.publisherJIPSen_US
dc.subjectCNN-LSTM Neural Networken_US
dc.subjectElectricity Consumption Predictionen_US
dc.subjectLarge Gaps of Missing Valuesen_US
dc.subjectPrediction of Missing Values in Time-Series Dataen_US
dc.subjectSmart Home Systemen_US
dc.titleA Novel Framework Based on CNN-LSTM Neural Network for Prediction of Missing Values in Electricity Consumption Time-Series Datasetsen_US
dc.typeArticleen_US
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