Pamir Shams

COMSATS University Islamabad | CUI · Department of Computer Sciences

The rapid increase in non technical loss (NTL) has become a principal concern for distribution system operators (DSOs) over the years. Electricity theft makes the major part of NTL. It causes losses for the DSOs and also deteriorates the quality of electricity. The introduction of advanced metering infrastructurte (AMI) along with the upgradation o...

2 Financial loss in the power systems is an emerging problem that needs to be resolved. 3 To tackle the mentioned problem, the energy generated from various generation sources 4 in the power network needs proper scheduling. In order to determine the best settings 5 for the control variables, this study formulates and solves an optimal power flow (O...

In the modern world, numerous opportunities help detect electricity theft happening in electricity grids due to the widespread shifting of people from old metering infrastructure to advanced metering infrastructure (AMI). It is done by studying the consumers' energy consumption (EC) readings provided by smart meters (SM). The literature introduces...

Electricity theft is one of the challenging problems in smart grids. The power utilities around the globe face huge economic loss due to ET. The traditional electricity theft detection (ETD) models confront several challenges, such as highly imbalance distribution of electricity consumption data, curse of dimensionality and inevitable effects of no...

In this paper, a novel hybrid deep learning approach is proposed to detect the nontechnical losses (NTLs) that occur in smart grids due to illegal use of electricity, faulty meters, meter malfunctioning, unpaid bills, etc. The proposed approach is based on data-driven methods due to the sufficient availability of smart meters’ data. Therefore, a bi...

Electricity theft is considered one of the most significant reasons of the non technical losses (NTL). It negatively influences the utilities in terms of the power supply quality, grid’s safety, and economic loss. Therefore, it is necessary to effectively deal with the electricity theft problem. For detecting electricity theft in smart grids (SGs),...

Obtaining outstanding electricity theft detection (ETD) performance in the realm of advanced metering infrastructure (AMI) and smart grids (SGs) is quite difficult due to various issues. The issues include limited availability of theft data as compared to benign data, neglecting dimensionality reduction, usage of the standalone (single) electricity...

A stacking ensemble model (SEM) is proposed in this paper to identify non-technical losses. Three layers make up the proposed model. Data pre-processing is performed at the first layer, where issues of data imbalance, missing values, and data normalization are dealt with. Min-max and a simple imputer are used to handle data normalization and missin...

In this paper, a data driven based solution is proposed to detect Non-Technical Losses (NTLs) in the smart grids. In the real world, the number of theft samples are less as compared to the benign samples, which leads to data imbalance issue. To resolve the issue, diverse theft attacks are applied on the benign samples to generate synthetic theft sa...

In this paper, a problem of misclassification due to cross pairs across a decision boundary is investigated. A cross pair is a junction of the two opposite class samples. These cross pairs are identified using Tomek links technique. The majority class sample associated with cross pairs are removed to segregate the two opposite classes through an af...

In this paper, we present a novel approach for the electricity theft detection (ETD). It comprises of two modules: (1) implementations of the six theft attacks for dealing with the data imbalanced issue and (2) a gated recurrent unit (GRU) to tackle the model’s poor performance in terms of high false positive rate (FPR) due to some non malicious re...

In this paper, a hybrid deep learning model is presented to detect electricity theft in the power grids, which happens due to the Non-Technical Losses (NTLs). The NTLs emerge due to meter malfunctioning, meter bypassing, meter tampering, etc. The main focus of this study is to detect the NTLs. However, the detection of NTLs faces three major challe...

In this paper, we present a novel approach for the electricity theft detection (ETD). It comprises of two modules:(1) implementations of the six theft attacks for dealing with the data imbalanced issue and (2) a gated recurrent unit (GRU)to tackle the model’s poor performance in terms of high false positive rate (FPR) due to some non malicious reas...

In this paper, a novel hybrid deep learning approach is proposed to detect the nontechnical losses (NTLs) that occur in smart grids due to illegal use of electricity, faulty meters, meter malfunctioning, unpaid bills, etc. The proposed approach is based on data-driven methods due to the sufficient availability of smart meters' data. Therefore, a bi...

In this paper, a hybrid deep learning model is presented to detect electricity theft in the power grids, which happens due to the Non-Technical Losses (NTLs). The NTLs emerge due to meter malfunctioning, meter bypassing, meter tampering, etc. The main focus of this study is to detect the NTLs. However, the detection of NTLs faces three major challe...

In this paper, a data driven based solution is proposed to detect Non-Technical Losses (NTLs) in the smart grids. In the real world, the number of theft samples are less as compared to the benign samples, which leads to data imbalance issue. To resolve the issue, diverse theft attacks are applied on the benign samples to generate synthetic theft sa...

Demand side management (DSM) in smart grid (SG) makes users able to take informed decisions according to their power usage pattern and assists the electric utility in minimizing higher power demand in the duration of higher energy demand intervals. Where, this ultimately leads to carbon emission reduction, electricity monetary cost minimization and...

Demand side management (DSM) in smart grid (SG) makes users able to take informed decisions according to the power usage pattern of the electricity users and assists the utility in minimizing peak power demand in the duration of high energy demand slots. Where, this ultimately leads to carbon emission reduction, total electricity cost minimization...

In smart grid (SG), demand side management (DSM) is a set or group of programs, allow consumers to play a vital role in transferring of their own load demand during peak time periods and minimizing their hourly based power consumption and total monetary cost of the electricity consumed and it also helps the electric utility in reducing higher power...

In this paper, we used two techniques: Enhanced Differential Evolution (EDE) and Crow Search Algorithm (CSA), in order to evaluate the performance of Home Energy Management System (HEMS). The total load is categorized into three groups based on their energy consumption pattern, and time of use of appliances. Critical Peak Pricing (CPP) scheme is us...

The electricity demand from residential buildings is increasing gradually day by day. Home Energy Management Systems (HEMS) are used to meet this demand by using Demand Sides Management (DSM) to reduce the pressure on consumers and utility companies. In this paper, HEMS is facilitated by using different meta-heuristic scheduling techniques: The Str...

In this paper, two meta-heuristic techniques Chicken Swarm Optimization (CSO) and Enhanced Differential Evolution (EDE) are used for demand side management. We have integrated Traditional Grids with Demand Side Management (DSM) We have categorized appliances in two categories; fixed and shiftable/elastic appliances. Real Time Pricing (RTP) is used...

Today, electricity is the most worthwhile resource which makes human life very easy. To overcome the gap among demand and supply of electricity, new techniques and methods are being explored. However, electricity demand is increasing constantly, which causes serious crisis. To tackle this problem, demand side management integrated with traditional...

In this work, we evaluated the performance of home energy management system (HEMS) using two meta-heuristic optimization algorithms: harmony search algorithm (HSA) and crow search algorithms (CSA). For electricity bill calculation we use real time pricing (RTP) signals. Our main objectives are optimization of energy consumption, electricity cost mi...

Demand side management (DSM) in smart grid (SG) makes users able to take informed decisions according to the power usage pattern of the electricity users and assists the utility in minimizing peak power demand in the duration of high energy demand slots. Where, this ultimately leads to carbon emission reduction, total electricity cost minimization...

In smart grid (SG), demand side management (DSM) is a set or group of programs, allow consumers to play a vital role in transferring of their own load demand during peak time periods and minimizing their hourly based power consumption and total monetary cost of the electricity consumed and it also helps the electric utility in reducing higher power...

In this paper, we used two techniques: Enhanced Differential Evolution (EDE) and Crow Search Algorithm (CSA), in order to evaluate the performance of Home Energy Management System (HEMS). The total load is categorized into three groups based on their energy consumption pattern, and time of use of appliances. Critical Peak Pricing (CPP) scheme is us...

The electricity demand from residential buildings is increasing gradually day by day. Home Energy Management Systems (HEMS) are used to meet this demand by using Demand Sides Management (DSM) to reduce the pressure on consumers and utility companies. In this paper, HEMS is facilitated by using different meta-heuristic scheduling techniques: The Str...

In this paper, two meta-heuristic techniques Chicken Swarm Optimization (CSO) and Enhanced Differential Evolution (EDE) are used for demand side management. We have integrated Traditional Grids with Demand Side Management (DSM) We have categorized appliances in two categories; fixed and shiftable/elastic appliances. Real Time Pricing (RTP) is used...

Today, electricity is the most worthwhile resource which makes human life very easy. To overcome the gap among demand and supply of electricity, new techniques and methods are being explored. However, electricity demand is increasing constantly, which causes serious crisis. To tackle this problem, demand side management integrated with traditional...

In this work, we evaluated the performance of home energy management system (HEMS) using two meta-heuristic optimization algorithms: harmony search algorithm (HSA) and crow search algorithms (CSA). For electricity bill calculation we use real time pricing (RTP) signals. Our main objectives are optimization of energy consumption, electricity cost mi...