Ahmad Nadeem

Lahore University of Management Sciences

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Co-Authors

Naveed Arshad

Lahore University of Management Sciences

Ameera Arif

National University of Sciences and Technology

Ahmad Naeem

Sumeyya Javaid

Lahore University of Management Sciences

Muhammad Hassaan

University of Health Sciences Lahore

Sean Stankowski

Institute of Science and Technology Austria

Malik Muhammad Arslan

Xidian University

Wasif Tahir

Lahore University of Management Sciences

Elhomossany Mahmoud

Hiroshima University

Muhammad Abdullah

University of the Punjab

Cooperative Institutions

Lahore University of Management Sciences

National University of Sciences and Technology

COMSATS University Islamabad

University of Sheffield

Government College University, Lahore

University of Engineering and Technology Lahore

University of Veterinary and Animal Sciences

Karachi Medical and Dental College

Islamia University of Bahawalpur

Universiti Teknologi MARA

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Estimating Battery State of Health using Machine Learning

Ameera Arif Muhammad Hassaan Mujahid Abdullah Ahmad Nadeem Naveed Arshad

The share of energy consumption in the transportation sector is projected to increase at an annual average rate of 1.4% up to 2040. This is primarily due to a transition towards electric vehicles (EVs) from internal combustion engine- based modes of transportation. Batteries are the most crucial component in EVs, constituting a significant share of the price of the vehicle. With usage, batteries degrade, thereby, limiting their ability to store energy which adversely impacts the driving range offered by EVs. Therefore, the need is to study the deterioration of batteries in electric means of transportation. We have created data-driven models to monitor battery health, predict the deterioration in batteries and give insights to the EV owners to make better decisions. The dataset used in this study is published by Sandia National Labs (SNL). It is a result of experiments performed on NMC cells. We present a comparison of three models - multiple linear regression, support vector regression, and artificial neural network for battery health monitoring with mean average percentage error (MAPE) of 1.99, 0.74, and 0.72 respectively.

Limiting

State of health

Driving range

10.1109/icsgce55997.2022.9953596

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Citations (1)

Sustainable energy transition optimization through decentralized hybrid energy systems with various energy storage technologies under multi-criterion indices: A mid-career repowering scenarios

Journal of Energy Storage (2024)

Shahid Nawaz Khan Muhammad Abdullah Ahmad Nadeem Naveed Arshad

Sustainable Energy

10.1016/j.est.2024.112490

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Citations (2)

A data-driven approach to reduce electricity theft in developing countries

Utilities Policy (2021)

Ahmad Nadeem Naveed Arshad

Identification

10.1016/j.jup.2021.101304

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Citations (8)

Does Pakistan have enough electricity generation to support massive penetration of electric vehicles?

Anam Iqbal Ahmad Nadeem Malik Muhammad Arslan Mohammad Abubakr Javed Naveed Arshad

Pakistan experienced a grave power shortfall between 2006 and 2017. The government responded to the situation through fast track development of power plants. However, the energy planners were unable to accurately forecast the electricity demand, resulting in a situation where more generation is available than needed. The power plants are backed by take-or-pay contracts. Thus, even if the power plants are not operating, a substantial capacity payment is still paid. To utilize this surplus available capacity year around electricity loads are needed. On the other hand, Northeastern Pakistan has one of the worst air quality in the world. Over 45% of the emissions are attributed to the transport sector. Therefore, electric vehicles (EVs) are an ideal load that utilizes the excess generation capacity and at the same time, improves air quality in the region. In this paper, we analyse the impact of various penetration levels of EVs on the utilization of excess generation capacity. This analysis will help us to determine EV penetration goals such that the capacity is optimally utilized. The impact of EVs is analysed using three optimistic scenarios of 30%, 50% and 70% new vehicle sales for two, three and four wheelers starting from 2020. This showed that by 2024, EVs will add 1250 MW at 70% new vehicle sales and only with peak demand, it is expected to reach available generation capacity.

Electricity demand

Market Penetration

Nameplate capacity

Peak demand

10.1109/tpec51183.2021.9384948

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Modelling Residential-Scale Consumer Demographics using Monthly Electricity Consumption Data

Abdur Rahman Ameera Arif Ahmad Nadeem Naveed Arshad

Electricity is one of the most widely used forms of energy that plays a significant part in sufficing the fundamental energy demand based on contemporary human needs. It is becoming highly tedious for the energy sector to manage and surveil the modern energy demands based on the constantly changing consumer demographics. In order to progress as a business, it has become pertinent for the distribution companies to evolve their development plans, tariffs, and business models according to the consumer requirements. This article proposes three different models to predict the attributes of a consumer household, namely the multivariate linear regression (MLR), the support vector regression (SVR), and the artificial neural network (ANN). The study uses the PRECON dataset, which is based on the monthly electricity consumption of households in Lahore, Pakistan. All of the proposed models play significant roles in predicting the required consumer demographics for forecasting. The linear model shows the ability to predict the number of people with very low MAPE of 3.57% as compared to other models. So far, ANN has shown the best results in predicting the number of fans, air conditioners, and rooms. However, the MAPE reports extracted from this study show the inability of the used models to explain the variation of property area confidently.

Demographics

Consumption

10.1109/epec52095.2021.9621651

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Using Clustering To Reduce Models Required For Medium Term Load Forecasting

Ameera Arif Ahmad Nadeem Naveed Arshad

Distribution companies sustain their business by providing electricity to its customers reliably. An essential step in this process is forecasting of electricity demand so that the company can develop their business plan for the future accordingly. Distribution companies use a bottom-up approach for medium-term load forecasting which requires several regression models to be developed. This requires a lot of effort to design models for each power feeder. To solve this problem we propose the use of k-means clustering to reduce the number of models required to create a trade-off between effort and satisfactory forecasting accuracy. By using k-means clustering, we group energy consumption for each category and feeder according to their energy consumption pattern. The centroids of each group are then fed to multivariate linear regression (MLR) models. The brute force way is to create a model for each feeder separately. For evaluation, we compare our results with the brute force way. The results show that for about 300 feeders we just require 100 models for a minimal increase of only 5% in average mean average precision error (MAPE) for domestic category.

Centroid

Probabilistic Forecasting

10.1109/acemp-optim57845.2023.10287074

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Temporomandibular Joint: A Short Insight

Indian Journal of Medical & Health Sciences (2018)

Ahmad Naeem Ahmad Nadeem Mohamed Mohsen Abuzenada Basem Alsharif Abdulmohsin Al-Ghalib Thamer Adel Helmy Ahmed Ali

Temporomandibular joint is a synovial articulation, formed by condyle of the mandible and glenoid (mandibular) fossa and articular eminence of temporal bone at the base of the skull. It differs from other joints of body because it shows the properties of both hinge and sliding joint so it also called as ginglymo-arthrodial joint. Temporomandibular joint abnormalities affect 5-12% of the population. The correct diagnosis of early abnormalities of this joint is of paramount importance because they can result in temporomandibular joint disorders. In the present article, a short insight is presented on Temporomandibular joint with emphasis on its anatomy, physiology & applied aspect.

Synovial joint

Muscles of mastication

Articulation (sociology)

Mandible (arthropod mouthpart)

10.21088/ijmhs.2347.9981.5118.6

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The speciation survey app: an interactive dashboard for exploring the results of the online survey

Authorea (Authorea) (2022)

Sean Stankowski Ahmad Nadeem Mark Ravinet

Dashboard

Survey research

Mobile apps

Smartphone app

10.22541/au.164303992.23074150/v1

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Citations (1)

PRECON

Ahmad Nadeem Naveed Arshad

Buildings consume on average of over 40% of energy throughout the world[1]. Therefore, it is crucial to fully understand the consumption behaviour of building occupants for energy efficiency, efficient load balancing and better demand-side management. To this end, small number of datasets are available from developing countries, particularly South Asia, that can model consumption behaviours of a wide range of residential electricity users. In this paper, we present PRECON dataset, collected over a period of one year, of electricity consumption patterns for 42 residential properties having varied demographics. Data is collected for the whole house consumption and from high powered devices as well as from major areas of the building. This dataset can play a pivotal role for distribution companies and policymakers to use data-driven optimization of generation, perform better demand-side management and improve energy efficiency.

Demographics

Consumption

Demand side

Electricity demand

Load management

10.1145/3307772.3328317

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Citations (28)

Performance Evaluation of Low Sampling Rates in Event Detection and Appliance Recognition in Non-Intrusive Load Monitoring System

Sumeyya Javaid Muhammad Abdullah Ahmad Nadeem Naveed Arshad

The worldwide share of renewables in energy generation is over 28%. With the growing energy consumption patterns and urge to transition from fossil fuel-based energy to cleaner sources i.e., renewable energy generation is continuously rising. Due to the intermittent nature of the renewables, continuous load monitoring is required. Various techniques are available in literature to perform Non-Intrusive Load Monitoring (NILM) to segregate load consumption of a building to individual appliances. In this paper, the effect of various sampling frequencies is investigated. Low sampling rates from Is to 30s are used. The data sampled at different resolutions are used to train convolution neural networks to draw performance comparisons of event detection and appliance recognition at various sampling rates. And finally, the overall accuracy of the model is calculated.

Convolution (computer science)

10.1109/gpecom58364.2023.10175779

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