KEYWORDS: Data mining, System integration, Mining, Data processing, Temperature metrology, Standards development, Power supplies, Fuzzy logic, Feature extraction, Data centers
Traditional mining methods are easily influenced by typical factors such as nature, so the precision of data mining is not ideal. This paper presents a data mining method for energy consumption behavior of integrated energy system considering typical factors. The K-means algorithm is used to correct the damaged data and to mine and marginalize the partially disturbed data in the data. Therefore, the standardized processing method is used to quantify the data. After filtering, the characteristics of user's electricity consumption behavior can be accurately extracted. Experimental results show that the proposed method can effectively extract the accurate electricity consumption data, and the accuracy is higher than the traditional method.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.