A Machine Learned Approach to Identify the Anomalies in Load Pattern of Pakistan | [Journal of Applied and Emerging Sciences • 2021]

Author(s):

1. Khwaja Naveed: Department of Electrical Engineering, University of Engineering and Technology, Peshawar, Pakistan

2. Khalil Khan: Department of Electrical Engineering, University of Azad Jammu Kashmir, Muzaffarabad, Pakistan

3. Nasir Ahmad Khan: Department of Computer System Engineering, University of Engineering and Technology, Peshawar, Pakistan

4. Adeel Shams: Department of Electrical Engineering, University of Azad Jammu Kashmir,Muzaffarabad,Pakistan

5. Muhammad Junaid: Department of Information Technology, University of Haripur,Haripur,Pakistan

6. Yousaf Saeed: Department of Information Technology, University of Haripur,Haripur,Pakistan

Abstract:

The energy crisis of Pakistan is worsening day by day for different explicit and implicit reasons. Load pattern and its use in the studies of power system is a vital area. Here it is used for the identification and analysis of anomalies in the load pattern of Pakistan while using Support Vector Regressor Machine as a Machine Learning tool. The training phase has been provided with retrospective data of electrical load, temperature, and relative humidity so as to predict the future electrical load in the testing phase, based on the then data of temperature and relative humidity. Based on temperature, three groups of electrical load have been opted based on particle swarm optimization clustering namely moderate, cold and hot. The difference curve between the actual load and predicted load illustrated various anomalies in all of the three clusters. The high numbers of anomalies were found in the hot cluster whilst confirming the dependence of load pattern upon the weather parameters. The analysis of the difference curve between the patterns portrays to be deformed enormously. The analysis of the anomalies has also being carried out in terms of contextualized parameters.

Page(s): 120-124

DOI: 10.36785/jaes.111488

Published: Journal: Journal of Applied and Emerging Sciences , Volume: 11, Issue: 1, Year: 2021

Keywords:

Index Terms , Electric Load Pattern , SVM Regressor , machine learning , Hot Cluster , Cold Cluster , Moderate Cluster

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