Neural Network-based Smart Meter Demand Response Analysis: A Case Study of Addis Ababa Power System

dc.contributor.advisorDereje, Shiferaw (PhD)
dc.contributor.authorBetelhem, Abera
dc.date.accessioned2021-11-17T04:20:35Z
dc.date.accessioned2023-11-28T14:20:37Z
dc.date.available2021-11-17T04:20:35Z
dc.date.available2023-11-28T14:20:37Z
dc.date.issued2021-10
dc.description.abstractMost African countries including Ethiopia used the old way of going door to door to record usages of electricity. This results lots of guesswork, which has direct impact on consumers, especially in billing. In case of Ethiopian Electric Utility (EEU) getting real-time information about power interruption, maximum power consumption in the grid is difficult to maintain and implement especially when going down towards the end consumers. The concepts of Smart Meters are introduced to address problems associated in electricity transmission throughout the usual traditional grid, which allows a bidirectional communication between the household smart meters and the supplier. This study aims to explore demand response analysis of smart meters using available recorded information by training Neural Network method to identify maximum demand response, type of power interruption and identify theft, by means of Artificial Neural Networks (ANNs) with Feedforward backpropagation algorithm for the selected cases in Addis Ababa as a case study. Four districts in the EEU Addis Ababa City were used for collecting quantitative data. The result for theft identification purpose consists of 169,296 samples, 25 neurons, two outputs. The best validation performance is 0.003124, and the overall correctly predicted percentage becomes 99.7%. In power fluctuation classifications, the model data sets consist of 3,596 sample sizes with 30 hidden neurons. The best validation performance is 0.03197. Moreover, the overall percentage of correctly predicted values is 97.2%. Finally, for the maximum power demand the percentage of correctly predicted values is 100%. The data analysis highly affected the performance of the NN system. Lastly the study recommends, further improvements can be achieved by process real-time data from millions of smart metering, more efficient modeling can lead to higher prediction accuracy.en_US
dc.identifier.urihttp://etd.aau.edu.et/handle/12345678/28709
dc.language.isoen_USen_US
dc.publisherAddis Ababa Universityen_US
dc.subjectSmart Griden_US
dc.subjectDemand Responseen_US
dc.subjectSmart Meteren_US
dc.subjectDataen_US
dc.subjectNeural Networken_US
dc.subjectFault detection Theft detectionen_US
dc.subjectMATLABen_US
dc.titleNeural Network-based Smart Meter Demand Response Analysis: A Case Study of Addis Ababa Power Systemen_US
dc.typeThesisen_US

Files

Original bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
Betelhem Abera.pdf
Size:
4.09 MB
Format:
Adobe Portable Document Format
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Plain Text
Description: