TIIS (한국인터넷정보학회)
Current Result Document : 142 / 143
| 한글제목(Korean Title) |
Building Energy Time Series Data Mining for Behavior Analytics and Forecasting Energy consumption |
| 영문제목(English Title) |
Building Energy Time Series Data Mining for Behavior Analytics and Forecasting Energy consumption |
| 저자(Author) |
Balachander.K
Paulraj.D
|
| 원문수록처(Citation) |
VOL 15 NO. 06 PP. 1957 ~ 1980 (2021. 06) |
한글내용
(Korean Abstract) |
|
영문내용
(English Abstract) |
The significant aim of this research has always been to evaluate the mechanism for efficient and inherently aware usage of vitality in-home devices, thus improving the information of smart metering systems with regard to the usage of selected homes and the time of use. Advances in information processing are commonly used to quantify gigantic building activity data steps to boost the activity efficiency of the building energy systems. Here, some smart data mining models are offered to measure, and predict the time series for energy in order to expose different ephemeral principles for using energy. Such considerations illustrate the use of machines in relation to time, such as day hour, time of day, week, month and year relationships within a family unit, which are key components in gathering and separating the effect of consumers behaviors in the use of energy and their pattern of energy prediction. It is necessary to determine the multiple relations through the usage of different appliances from simultaneous information flows. In comparison, specific relations among interval-based instances where multiple appliances use continue for certain duration are difficult to determine. In order to resolve these difficulties, an unsupervised energy time-series data clustering and a frequent pattern mining study as well as a deep learning technique for estimating energy use were presented. A broad test using true data sets that are rich in smart meter data were conducted. The exact results of the appliance designs that were recognized by the proposed model were filled out by Deep Convolutional Neural Networks (CNN) and Recurrent Neural Networks (LSTM and GRU) at each stage, with consolidated accuracy of 94.79%, 97.99%, 99.61%, for 25%, 50%, and 75%, respectively. |
| 키워드(Keyword) |
Behavioral Analytics
Big Data Mining
Clustering Analysis
CNN
energy consumption
Energy Prediction
LSTM
|
| 파일첨부 |
PDF 다운로드
|
