The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
This paper presents a new approach based on semi-supervised machine learning and wavelet design applied to non-intrusive load monitoring. Co-training of two machine learning classifiers is used to automate the process of learning the load pattern after designing new wavelets. The numerical results demonstrating the effectiveness of the proposed approach are discussed and conclusions are drawn.
This paper presents an application of discrete wavelet and ensemble decision tree classifier to the non-intrusive load monitoring (NILM). The effect of different order of Daubechies wavelet filter on the classification accuracy is investigated. Also the paper studies the effect of increasing the number of decision trees contained in the ensemble on the performance of the classifier by measuring the...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.