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.
Smart meters are being gradually adopted by energy providers for commercial use due to multiple benefits. The extraction of actionable knowledge from smart meter readings can lead to informed decision-making in demand forecasting and consumption analysis. This paper extends an incremental learning approach for pattern characterization in a smart meter data stream environment, with the incorporation...
This paper presents a novel methodology for the incremental characterization and prediction of electricity consumption based on smart meter readings. A self-learning algorithm is developed to incrementally discover patterns in a data stream environment and sustain acquired knowledge for subsequent learning. It generates an evolving columnar structure composed of learning outcomes from each phase....
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.