Detecting Faults and Estimating Missing Values in Smart Meter Data
|Title||Detecting Faults and Estimating Missing Values in Smart Meter Data|
|Summary||Finding outliers and missing energy consumptions, and replace them with estimated values|
|Keywords||data mining, smart meter data|
|Prerequisites||Cooperating Intelligent Systems and Learning Systems courses|
|Supervisor||Sławomir Nowaczyk, Anita Sant'Anna, Hassan Mashad Nemati|
This project is about investigating available techniques and methods for: outlier analysis, missing data detection, and electricity consumption estimation on smart meters data. The idea is to use data mining and machine learning techniques to check for inconsistencies in the data and replace them based on information of customer’s electricity usage.
Data are collected from real Smart Grid distribution network and contain active power consumption (daily or hourly consumption).