| 한글제목(Korean Title) |
A Novel Framework Based on CNN-LSTM Neural Network for Prediction of Missing Values in Electricity Consumption Time-Series Datasets |
| 영문제목(English Title) |
A Novel Framework Based on CNN-LSTM Neural Network for Prediction of Missing Values in Electricity Consumption Time-Series Datasets |
| 저자(Author) |
Syed Nazir Hussain
Azlan Abd Aziz
Md. Jakir Hossen
Nor Azlina Ab Aziz
G. Ramana Murthy
Fajaruddin Bin Mustakim
|
| 원문수록처(Citation) |
VOL 18 NO. 01 PP. 0115 ~ 0129 (2022. 02) |
한글내용
(Korean Abstract) |
|
영문내용
(English Abstract) |
Adopting 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 perfor- mance superiority of the CNN-LSTM model over the single CNN and LSTM neural networks. |
| 키워드(Keyword) |
CNN-LSTM Neural Network
Electricity Consumption Prediction
Large Gaps of Missing Values
Prediction of Missing Values in Time-Series Data
smart home system
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| 파일첨부 |
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