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Sensors and sensor networks are the future of fully automated industry solutions. With more capability and complex machinery, the requirements for sensing in larger factories are critical, considering the data amount, latency, and the number of sensors in operation. Given the excellent time-critical operation, bandwidth and the number of devices connected, the 5G indoor femtocells could prove an excellent option for building industrial sensor grids. For more flexibility in control and reliability, operating the 5G indoor femtocell network in license-free frequency bands could be an alternative to commercial 5G services. The 5G networks incorporate a very dense network of indoor femtocells. The Femtocells also enhance data rates, indoor performance, and coverage area both in residential and industrial environments. Therefore, keeping in view the above-stated actualities, this paper addresses different indoor scenarios for radio wave propagation and simulates several path loss models to calculate the likely and most suitable propagation model for indoor signaling. Multiple models for frequencies in the unlicensed band below 6 GHz and above 6 GHz (licensed) 5G femtocells are discussed in the paper considering the constraints of material types, attenuation due to obstacles, various floors, carrier frequency, and distance from the transmitter. The comparative analysis indicates that the ITU model and Keenan-Motley model give the highest path loss in residential and industrial environments, respectively, while the log-distance model has the lowest path loss in both environments for below 6 GHz frequencies in the unlicensed spectrum. For the above 6 GHz licensed bands, the Alpha Beta Gamma (ABG) model and Path Loss Exponent (CIF) model are observed to have the minimum path loss difference.
Noman Shabbir; Lauri Kütt; Muhammad M. Alam; Priit Roosipuu; Muhammad Jawad; Muhammad B. Qureshi; Ali R. Ansari; Raheel Nawaz. Vision towards 5G: Comparison of radio propagation models for licensed and unlicensed indoor femtocell sensor networks. Physical Communication 2021, 47, 101371 .
AMA StyleNoman Shabbir, Lauri Kütt, Muhammad M. Alam, Priit Roosipuu, Muhammad Jawad, Muhammad B. Qureshi, Ali R. Ansari, Raheel Nawaz. Vision towards 5G: Comparison of radio propagation models for licensed and unlicensed indoor femtocell sensor networks. Physical Communication. 2021; 47 ():101371.
Chicago/Turabian StyleNoman Shabbir; Lauri Kütt; Muhammad M. Alam; Priit Roosipuu; Muhammad Jawad; Muhammad B. Qureshi; Ali R. Ansari; Raheel Nawaz. 2021. "Vision towards 5G: Comparison of radio propagation models for licensed and unlicensed indoor femtocell sensor networks." Physical Communication 47, no. : 101371.
The growing use of nonlinear devices is introducing harmonics in power system networks that result in distortion of current and voltage signals causing damage to power distribution systems. Therefore, in power systems, the elimination of harmonics is of great significance. This paper presents an efficient techno-economical approach to suppress harmonics and improve the power factor in power distribution networks using Shunt Hybrid Active Power Filters (SHAPF) based on neural network algorithms like Artificial Neural Network (ANN), Adaptive Neuro-Fuzzy Inference System (ANFIS), and Recurrent Neural Network (RNN). The objective of the proposed algorithms for SHAPF is to enhance system performance by reducing Total Harmonic Distortion (THD). In our filter design approach, we tested and compared conventional pq0 theory and neural networks to detect the harmonics present in the power system. Moreover, for the regulation of DC supply to the inverter of the SHAPF, the conventional PI controller and neural networks-based controllers are used and compared. The applicability of the proposed filter is tested for three different nonlinear load cases. The simulation results show that the neural networks-based filter control techniques satisfy all international standards with minimum current THD, neutral wire current elimination, and small DC voltage fluctuations for voltage regulation current. Furthermore, the three neural network architectures are tested and compared based on accuracy and computational complexity, with RNN outperforming the rest.
Muzammil Iqbal; Muhammad Jawad; Mujtaba Hussain Jaffery; Saleem Akhtar; Muhammad Nadeem Rafiq; Muhammad Bilal Qureshi; Ali R. Ansari; Raheel Nawaz. Neural Networks Based Shunt Hybrid Active Power Filter for Harmonic Elimination. IEEE Access 2021, 9, 69913 -69925.
AMA StyleMuzammil Iqbal, Muhammad Jawad, Mujtaba Hussain Jaffery, Saleem Akhtar, Muhammad Nadeem Rafiq, Muhammad Bilal Qureshi, Ali R. Ansari, Raheel Nawaz. Neural Networks Based Shunt Hybrid Active Power Filter for Harmonic Elimination. IEEE Access. 2021; 9 ():69913-69925.
Chicago/Turabian StyleMuzammil Iqbal; Muhammad Jawad; Mujtaba Hussain Jaffery; Saleem Akhtar; Muhammad Nadeem Rafiq; Muhammad Bilal Qureshi; Ali R. Ansari; Raheel Nawaz. 2021. "Neural Networks Based Shunt Hybrid Active Power Filter for Harmonic Elimination." IEEE Access 9, no. : 69913-69925.
In modern power systems, since most loads are inductive by nature, there is an ongoing power quality issue and researchers’ interest in improving the power factor is widespread, as inductive loads have a low power factor that depletes the system’s capacity and has an adverse effect on the voltage level. The measurement and acute analysis of voltage- and current-level waveforms is essential to tackle power quality issues. This article presents a detailed case study and analysis of real-time data measured from a frequency converter, which is used to operate the motor of a ventilation system. The output of the frequency converter is a highly distorted current wave. A hybrid Fourier transform (FT)- and wavelet transform-based solution has been proposed here to diagnose and identify the causes of motor failure in the ventilation system. The traditional FT did not give a detailed analysis of this type of signal, which is highly contaminated by noise. Therefore, first, the signal is preprocessed for data denoising using the wavelet transform. Second, the Fourier analysis is performed on the filtered signal for frequency analysis and segregation of fundamental frequency components, higher-order harmonics, and suppressed noise. The spectrum analysis reveals that the noise is generated due to the rapidly switching circuits in the frequency converter and this unfiltered signal at the output of the frequency converter causes motor failure.
Noman Shabbir; Lauri Kütt; Bilal Asad; Muhammad Jawad; Muhammad Iqbal; Kamran Daniel. Spectrum Analysis for Condition Monitoring and Fault Diagnosis of Ventilation Motor: A Case Study. Energies 2021, 14, 2001 .
AMA StyleNoman Shabbir, Lauri Kütt, Bilal Asad, Muhammad Jawad, Muhammad Iqbal, Kamran Daniel. Spectrum Analysis for Condition Monitoring and Fault Diagnosis of Ventilation Motor: A Case Study. Energies. 2021; 14 (7):2001.
Chicago/Turabian StyleNoman Shabbir; Lauri Kütt; Bilal Asad; Muhammad Jawad; Muhammad Iqbal; Kamran Daniel. 2021. "Spectrum Analysis for Condition Monitoring and Fault Diagnosis of Ventilation Motor: A Case Study." Energies 14, no. 7: 2001.
The electric power systems are becoming smart as well as complex with every passing year, especially in response to the changing environmental conditions. Resilience of power generation and transmission infrastructure is important to avoid power outages, ensure robust service, and to achieve sustained economic benefits. In this study, we employ a two-stage model to estimate the power outage in terms of its intensity as well as the duration. We identify the top three potentially critical states of United States of America, not merely based on duration of the power outage, but by also incorporating outage related revenue loss. In the proposed model, the first stage classifies the intensity of the outage event while the second stage predicts the duration of the outage itself. Moreover, the key predictors are characterized and their association with outage duration is illustrated. We use a comprehensive and publicly available dataset, which provides the information related to historical power outage events, such as electricity usage patterns, climatological annotations, socio-economic indicators, and land-use data. Our rigorous analysis and results suggest that the power outage interval is the function of several parameters, such as climatological condition, economic indicators as well as the time of the year. The proposed study can help the regulatory authorities taking appropriate decisions for long term economic paybacks. It can also help disaster management authorities to take risk-informed resilient decisions for system safety.
Naveed Taimoor; Ikramullah Khosa; Muhammad Jawad; Jahanzeb Akhtar; Imran Ghous; Muhammad Bilal Qureshi; Ali R. Ansari; Raheel Nawaz. Power Outage Estimation: The Study of Revenue-Led Top Affected States of U.S. IEEE Access 2020, 8, 223271 -223286.
AMA StyleNaveed Taimoor, Ikramullah Khosa, Muhammad Jawad, Jahanzeb Akhtar, Imran Ghous, Muhammad Bilal Qureshi, Ali R. Ansari, Raheel Nawaz. Power Outage Estimation: The Study of Revenue-Led Top Affected States of U.S. IEEE Access. 2020; 8 (99):223271-223286.
Chicago/Turabian StyleNaveed Taimoor; Ikramullah Khosa; Muhammad Jawad; Jahanzeb Akhtar; Imran Ghous; Muhammad Bilal Qureshi; Ali R. Ansari; Raheel Nawaz. 2020. "Power Outage Estimation: The Study of Revenue-Led Top Affected States of U.S." IEEE Access 8, no. 99: 223271-223286.
Minimizing the costs of cloud services is important for cloud service providers to grow their business. In cost calculation of cloud services, power consumption cost is a major factor. The variability present in renewable power generation, electricity price, and power demand variates the power utilization expenditures. Due to high computational requests, datacenters consume enormous energy every day, thereby necessitating the need for energy efficient designs. Therefore, an energy consumption cost reduction mechanism is required to optimize the data center’s power consumption cost locally and to have the option to redistribute the workload on geo-distributed data centers in peak hours for further reduction in power consumption cost. In this paper, the authors investigate the energy consumption cost optimization problem in cloud datacenters and propose a blockchain-based decentralized workload distribution and management model. Moreover, we minimize request scheduler time for transferring any job from one data center to another and secure the energy cost optimization process due to shut down/ communication failure. The proposed work is the primitive to present the energy cost optimization problem in geo-distributed data centers by introducing blockchain-based secure workload scheduling method considering both the temporal and spatial disparities associated with electricity tariffs and workload arrival process. The simulations are evaluated on real-world workload of the Google data center and associated electricity tariffs. The blockchain-based decentralized workload management framework is evaluate based on privacy and security analysis, update overhead, latency, and throughput. Moreover, the results show blockchain model migrate workload in minimum 46 % reduced time compared to conventional model.
Sara Sajid; Muhammad Jawad; Kanza Hamid; Muhammad U.S. Khan; Sahibzada M. Ali; Assad Abbas; Samee U. Khan. Blockchain-based decentralized workload and energy management of geo-distributed data centers. Sustainable Computing: Informatics and Systems 2020, 29, 100461 .
AMA StyleSara Sajid, Muhammad Jawad, Kanza Hamid, Muhammad U.S. Khan, Sahibzada M. Ali, Assad Abbas, Samee U. Khan. Blockchain-based decentralized workload and energy management of geo-distributed data centers. Sustainable Computing: Informatics and Systems. 2020; 29 ():100461.
Chicago/Turabian StyleSara Sajid; Muhammad Jawad; Kanza Hamid; Muhammad U.S. Khan; Sahibzada M. Ali; Assad Abbas; Samee U. Khan. 2020. "Blockchain-based decentralized workload and energy management of geo-distributed data centers." Sustainable Computing: Informatics and Systems 29, no. : 100461.
The combination of renewable energy sources and prosumer-based smart grid is a sustainable solution to cater to the problem of energy demand management. A pressing need is to develop an efficient Energy Management Model (EMM) that integrates renewable energy sources with smart grids. However, the variable scenarios and constraints make this a complex problem. Machine Learning (ML) methods can often model complex and non-linear data better than the statistical models. Therefore, developing an ML algorithm for the EMM is a suitable option as it reduces the complexity of the EMM by developing a single trained model to predict the performance parameters of EMM for multiple scenarios. However, understanding latent correlations and developing trust in highly complex ML models for designing EMM within the stochastic prosumer-based smart grid is still a challenging task. Therefore, this paper integrates ML and Gaussian Process Regression (GPR) in the EMM. At the first stage, an optimization model for Prosumer Energy Surplus (PES), Prosumer Energy Cost (PEC), and Grid Revenue (GR) is formulated to calculate base performance parameters (PES, PEC, and GR) for the training of the ML-based GPR model. In the second stage, stochasticity of renewable energy sources, load, and energy price, same as provided by the Genetic Algorithm (GA) based optimization model for PES, PEC, and GR, and base performance parameters act as input covariates to produce a GPR model that predicts PES, PEC, and GR. Seasonal variations of PES, PEC, and GR are incorporated to remove hitches from seasonal dynamics of prosumers energy generation and prosumers energy consumption. The proposed adaptive Service Level Agreement (SLA) between energy prosumers and the grid benefits both these entities. The results of the proposed model are rigorously compared with conventional optimization (GA and PSO) based EMM to prove the validity of the proposed model.
Waqar Ahmed; Hammad Ansari; Bilal Khan; Zahid Ullah; Sahibzada Muhammad Ali; Chaudhry Arshad Arshad Mehmood; Muhammad B. Qureshi; Iqrar Hussain; Muhammad Jawad; Muhammad Usman Shahid Khan; Amjad Ullah; Raheel Nawaz. Machine Learning Based Energy Management Model for Smart Grid and Renewable Energy Districts. IEEE Access 2020, 8, 185059 -185078.
AMA StyleWaqar Ahmed, Hammad Ansari, Bilal Khan, Zahid Ullah, Sahibzada Muhammad Ali, Chaudhry Arshad Arshad Mehmood, Muhammad B. Qureshi, Iqrar Hussain, Muhammad Jawad, Muhammad Usman Shahid Khan, Amjad Ullah, Raheel Nawaz. Machine Learning Based Energy Management Model for Smart Grid and Renewable Energy Districts. IEEE Access. 2020; 8 (99):185059-185078.
Chicago/Turabian StyleWaqar Ahmed; Hammad Ansari; Bilal Khan; Zahid Ullah; Sahibzada Muhammad Ali; Chaudhry Arshad Arshad Mehmood; Muhammad B. Qureshi; Iqrar Hussain; Muhammad Jawad; Muhammad Usman Shahid Khan; Amjad Ullah; Raheel Nawaz. 2020. "Machine Learning Based Energy Management Model for Smart Grid and Renewable Energy Districts." IEEE Access 8, no. 99: 185059-185078.
Deployment of efficient and cost-effective parking lots is a known bottleneck for the electric vehicles (EVs) sector. A comprehensive solution incorporating the requirements of all key stakeholders is required. Taking up the challenge, we propose a real-time EV smart parking lot model to attain the following objectives: (a) maximize the smart parking lot revenue by accommodating maximum number of EVs and (b) minimize the cost of power consumption by participating in a demand response (DR) program offered by the utility since it is a tool to answer and handle the electric power usage requirements for charging the EV in the smart parking lot. With a view to achieving these objectives, a linear programming-based binary/cyclic (0/1) optimization technique is developed for the EV charge scheduling process. It is difficult to solve the problems of binary optimization in real-time given that the complexity of the problem increases with the increase in number of EV. We deploy a simplified convex relaxation technique integrated with the linear programming solution to overcome this problem. The algorithm achieves: minimum power consumption cost of the EV smart parking lot; efficient utilization of available power; maximization of the number of the EV to be charged; and minimum impact on the EV battery lifecycle. DR participation provide benefits by offering time-based and incentive-based hourly intelligent charging schedules for the EV. A thorough comparison is drawn with existing variable charging rate-based techniques in order to demonstrate the comparative validity of our proposed technique. The simulation results show that even under no DR event, the proposed scheme results in 2.9% decrease in overall power consumption cost for a 500 EV scenario when compared to variable charging rate method. Moreover, in similar conditions, such as no DR event and for 500 EV arrived per day, there is a 2.8% increase in number of EV charged per day, 3.2% improvement in the average state-of-charge (SoC) of the EV, 12.47% reduction in the average time intervals required to achieve final SoC.
Muhammad Jawad; Muhammad Bilal Qureshi; Sahibzada Muhammad Ali; Noman Shabbir; Muhammad Usman Shahid Khan; Afnan Aloraini; Raheel Nawaz. A Cost-Effective Electric Vehicle Intelligent Charge Scheduling Method for Commercial Smart Parking Lots Using a Simplified Convex Relaxation Technique. Sensors 2020, 20, 4842 .
AMA StyleMuhammad Jawad, Muhammad Bilal Qureshi, Sahibzada Muhammad Ali, Noman Shabbir, Muhammad Usman Shahid Khan, Afnan Aloraini, Raheel Nawaz. A Cost-Effective Electric Vehicle Intelligent Charge Scheduling Method for Commercial Smart Parking Lots Using a Simplified Convex Relaxation Technique. Sensors. 2020; 20 (17):4842.
Chicago/Turabian StyleMuhammad Jawad; Muhammad Bilal Qureshi; Sahibzada Muhammad Ali; Noman Shabbir; Muhammad Usman Shahid Khan; Afnan Aloraini; Raheel Nawaz. 2020. "A Cost-Effective Electric Vehicle Intelligent Charge Scheduling Method for Commercial Smart Parking Lots Using a Simplified Convex Relaxation Technique." Sensors 20, no. 17: 4842.
Pakistan is facing an increasing energy crisis with every passing year due to a growing population and increased industrial zones. Moreover, the energy cost is very high as it is mostly generated from conventional energy sources. Conversely, Pakistan has excellent potential for solar power generation as it lies near the equator. Due to the recent decrease in solar Photovoltaic (PV) costs, Pakistan is moving towards these solutions for both on-grid and off-grid systems. Therefore, this article focuses on the solar irradiance behavior and computation of the PV panel’s optimum angle for maximum energy harvesting in Pakistan. Moreover, the domestic economic analysis of rooftop solar PV systems is conducted based on investment cost, payback period, electricity bills reduction, and optimal metering scheme selection. Furthermore, the impact of domestic PV systems on the power grid is evaluated based on a reduction in the electrical load. This study has the potential to encourage consumers to gain monetary benefits from the investments in domestic PV systems by selling excess energy to the local distribution company. All simulations are performed in sol-metric sun-eye software. These simulations results are more versatile and meaningful under the ground conditions and actualities of Pakistan.
Noman Shabbir; Muhammad Usman; Muhammad Jawad; Muhammad H. Zafar; Muhammad N. Iqbal; Lauri Kütt. Economic analysis and impact on national grid by domestic photovoltaic system installations in Pakistan. Renewable Energy 2020, 153, 509 -521.
AMA StyleNoman Shabbir, Muhammad Usman, Muhammad Jawad, Muhammad H. Zafar, Muhammad N. Iqbal, Lauri Kütt. Economic analysis and impact on national grid by domestic photovoltaic system installations in Pakistan. Renewable Energy. 2020; 153 ():509-521.
Chicago/Turabian StyleNoman Shabbir; Muhammad Usman; Muhammad Jawad; Muhammad H. Zafar; Muhammad N. Iqbal; Lauri Kütt. 2020. "Economic analysis and impact on national grid by domestic photovoltaic system installations in Pakistan." Renewable Energy 153, no. : 509-521.
Modeling Energy Consumption (EC) system based on environmental drifts, consumer psychology, and consumer body dynamics is a demanding task. No prior works have modeled EC system with respect to the above features. The use of an optimized, intelligent, and accurate model with all above inputs will help electric grid policymakers for (a) lowering energy cost (b) accurate predicted and forecasted energy models, and (c) optimized energy utilization, and (d) increased consumer empowerment with pollutants free atmosphere. Considering the above motivation, we discussed in-depth various promising features of the environment, climate, and weather shaping the energy demand of consumers. Our work describes detailed taxonomies of the above parameters with their respective trends and inter-relationship to each other. We investigated critically the mutual effects of surroundings and consumers.
S. M. Ali; Z. Ullah; I. Khan; M. A. Sarwar; B. Khan; U. Farid; C. A. Mehmood; Muhammad Jawad. Mutual Interactive Effects of Environment and Consumer Biological Dynamics on Energy Consumption. 2020 3rd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET) 2020, 1 -6.
AMA StyleS. M. Ali, Z. Ullah, I. Khan, M. A. Sarwar, B. Khan, U. Farid, C. A. Mehmood, Muhammad Jawad. Mutual Interactive Effects of Environment and Consumer Biological Dynamics on Energy Consumption. 2020 3rd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET). 2020; ():1-6.
Chicago/Turabian StyleS. M. Ali; Z. Ullah; I. Khan; M. A. Sarwar; B. Khan; U. Farid; C. A. Mehmood; Muhammad Jawad. 2020. "Mutual Interactive Effects of Environment and Consumer Biological Dynamics on Energy Consumption." 2020 3rd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET) , no. : 1-6.
Wind energy forecasting is a very challenging task as it involves many variable factors from wind speed, weather season, location and many other factors. Its accurate prediction can be quite helpful in maintaining the balance between demand and supply, and issues related to the reliability of a power system. In this article, the Recurrent Neural Network (RNN) based forecasting algorithm is used for the three day-ahead predictions of energy generation from wind sources in Estonia. Then a comparison is made between the predicted energy generation of Estonian energy regulatory authority's algorithm and this RNN based algorithm. The simulation results show that our proposed algorithm has lower Root Mean Square Error (RMSE) value and it gives better forecasting.
Noman Shabbir; Lauri Kutt; Muhammad Jawad; Roya Amadiahanger; Muhammad N. Iqbal; Argo Rosin. Wind Energy Forecasting Using Recurrent Neural Networks. 2019 Big Data, Knowledge and Control Systems Engineering (BdKCSE) 2019, 1 -5.
AMA StyleNoman Shabbir, Lauri Kutt, Muhammad Jawad, Roya Amadiahanger, Muhammad N. Iqbal, Argo Rosin. Wind Energy Forecasting Using Recurrent Neural Networks. 2019 Big Data, Knowledge and Control Systems Engineering (BdKCSE). 2019; ():1-5.
Chicago/Turabian StyleNoman Shabbir; Lauri Kutt; Muhammad Jawad; Roya Amadiahanger; Muhammad N. Iqbal; Argo Rosin. 2019. "Wind Energy Forecasting Using Recurrent Neural Networks." 2019 Big Data, Knowledge and Control Systems Engineering (BdKCSE) , no. : 1-5.
The bi-directional energy flow between prosumers (wind energy) and smart grid (SG) provides pertinent benefits, such as (i) load-sharing, (ii) peak-load shaving, (iii) load reduction with energy market programs, (iv) ancillary services-based energy transactions, and (v) mutual beneficial frameworks based on rewards and penalties. However, the load variations of SG, intermittent wind speed in energy district (ED) of prosumers, and stochastic energy price are the major constraints that must be considered in wind energy prosumers (WEPs) interaction with utility. Further, the interfacing and interactions of WEPs with SG incur an enormous volume of data to be processed, stored, accessed, and managed. Therefore, the authors proposed a stochastic bi-directional energy management model (BEMM) to manage the aforementioned constraints. Moreover, the BEMM is empowered with cloud-based service level agreement (C-SLA) that provides massive storage capabilities to the enormous data incurred due to WEPs interactions with SG. Two sub-models of BEMM are incorporated, namely stochastic wind estimation model and stochastic energy pricing model. The wind estimation model deals the stochasticity of wind speed for energy generation, while energy price model manages and controls the uncertainty of pricing tariffs based on real-time pricing and day-a-head pricing mechanisms for efficient energy trade between SG and WEPs under the principle of C-SLA.
Sahibzada Muhammad Ali; Zahid Ullah; Geev Mokryani; Bilal Khan; Iqrar Hussain; Chaudhry Arshad Mehmood; Umar Farid; Muhammad Jawad. Smart grid and energy district mutual interactions with demand response programs. IET Energy Systems Integration 2019, 2, 1 -8.
AMA StyleSahibzada Muhammad Ali, Zahid Ullah, Geev Mokryani, Bilal Khan, Iqrar Hussain, Chaudhry Arshad Mehmood, Umar Farid, Muhammad Jawad. Smart grid and energy district mutual interactions with demand response programs. IET Energy Systems Integration. 2019; 2 (1):1-8.
Chicago/Turabian StyleSahibzada Muhammad Ali; Zahid Ullah; Geev Mokryani; Bilal Khan; Iqrar Hussain; Chaudhry Arshad Mehmood; Umar Farid; Muhammad Jawad. 2019. "Smart grid and energy district mutual interactions with demand response programs." IET Energy Systems Integration 2, no. 1: 1-8.
In near future, the Electric Vehicles (EVs) will have a high level of penetration and their charging will be necessary to support daily operation. In such case, there will be three charging scenarios, such as (a) charging at commercial stations, (b) charging at home, and (c) charging at workplace. Therefore, during the working hours, the synchronized EV charging of the employee would experience an added demand charge on the data center operator. To reduce the impact of such demand charge, a joint power management strategy for the cloud data centers and its EVs parking-lot is required. However, the parked EVs is an energy source, such as battery bank that can participate in the power management problem. Considering the hybrid needs, in this paper a joint power management and energy cost minimization model for the data center and EV parking-lot is developed and solved as a conditional constraint optimization problem using Mixed Integer Linear Programming (MILP).
Sara Sajid; Muhammad Jawad; Muhammad Qureshi; M. Usman Shahid Khan; Sahibzada Muhammad Ali; Samee U. Khan. A Conditional-Constraint Optimization for Joint Energy Management of Data Center and Electric Vehicle Parking-Lot. 2019 Tenth International Green and Sustainable Computing Conference (IGSC) 2019, 1 -6.
AMA StyleSara Sajid, Muhammad Jawad, Muhammad Qureshi, M. Usman Shahid Khan, Sahibzada Muhammad Ali, Samee U. Khan. A Conditional-Constraint Optimization for Joint Energy Management of Data Center and Electric Vehicle Parking-Lot. 2019 Tenth International Green and Sustainable Computing Conference (IGSC). 2019; ():1-6.
Chicago/Turabian StyleSara Sajid; Muhammad Jawad; Muhammad Qureshi; M. Usman Shahid Khan; Sahibzada Muhammad Ali; Samee U. Khan. 2019. "A Conditional-Constraint Optimization for Joint Energy Management of Data Center and Electric Vehicle Parking-Lot." 2019 Tenth International Green and Sustainable Computing Conference (IGSC) , no. : 1-6.
This paper aims to solve the problem of sliding mode control for an uncertain two-dimensional (2-D) systems with states having time-varying delays. The uncertainties in the system dynamics are constituted of mismatched uncertain parameters and the unknown nonlinear bounded function. The proposed problem utilizes the model transformation approach. By segregating the proper Lyapunov–Krasovskii functional in concert with the improved version of Wirtinger-based summation inequality, sufficient solvability conditions for the existence of linear switching surfaces have been put forward, which ensure the asymptotical stability of the reduced-order equivalent sliding mode dynamics. Then, we solve the controller synthesis problem by extending the recently proposed reaching law to 2-D systems, whose proportional part is appropriately scaled by the factor that does not depend on some constant terms but rather on current switching surface’s value, which in turn ensures the faster convergence and better robustness against uncertainties. Finally, the proposed results have been validated through an implementation to a suitable physical system.
Imran Ghous; Zhaoxia Duan; Jahanzeb Akhtar; Muhammad Jawad. Robust stabilization of uncertain 2-D discrete-time delayed systems using sliding mode control. Journal of the Franklin Institute 2019, 356, 9407 -9431.
AMA StyleImran Ghous, Zhaoxia Duan, Jahanzeb Akhtar, Muhammad Jawad. Robust stabilization of uncertain 2-D discrete-time delayed systems using sliding mode control. Journal of the Franklin Institute. 2019; 356 (16):9407-9431.
Chicago/Turabian StyleImran Ghous; Zhaoxia Duan; Jahanzeb Akhtar; Muhammad Jawad. 2019. "Robust stabilization of uncertain 2-D discrete-time delayed systems using sliding mode control." Journal of the Franklin Institute 356, no. 16: 9407-9431.
In this paper the unscented Kalman filter (UKF) is applied to a particle tracking problem in the high energy physics (HEP) experiments where determination of position and momentum associated to a particle with high level of desired accuracy, in the presence of multiple scattering and ionization energy loss, is crucial. The main issue that naturally arises due to such particle and detection dynamics is relative (marginal) observability which causes the existing track reconstruction algorithm, the Extended Kalman filter (EKF), to under-perform or even diverge which has been elaborated through a comparison with the UKF. We present this challenging problem and investigate the issue of relative observability in terms of its relationship with measurement redundancy in order to improve the performance of the UKF. We also present an interesting issue where no duplicate measurements are available due to the measurement setup used at the HEP system under consideration and how in that particular scenario redundancy can be introduced.
Zhaoxia Duan; Jahanzeb Akhtar; Imran Ghous; Muhammad Jawad; Ikramullah Khosa; Khurram Ali. Improving the Tracking of Subatomic Particles Using the Unscented Kalman Filter With Measurement Redundancy in High Energy Physics Experiments. IEEE Access 2019, 7, 61728 -61737.
AMA StyleZhaoxia Duan, Jahanzeb Akhtar, Imran Ghous, Muhammad Jawad, Ikramullah Khosa, Khurram Ali. Improving the Tracking of Subatomic Particles Using the Unscented Kalman Filter With Measurement Redundancy in High Energy Physics Experiments. IEEE Access. 2019; 7 (99):61728-61737.
Chicago/Turabian StyleZhaoxia Duan; Jahanzeb Akhtar; Imran Ghous; Muhammad Jawad; Ikramullah Khosa; Khurram Ali. 2019. "Improving the Tracking of Subatomic Particles Using the Unscented Kalman Filter With Measurement Redundancy in High Energy Physics Experiments." IEEE Access 7, no. 99: 61728-61737.
Energy consumption (EC) of consumers primarily depends on comfort level (CL) affirmed by brain sensations of the central nervous system. Environmental parameters such as surroundings, relative humidity, air temperature, solar irradiance, air pressure, and cloud cover directly influence consumer body temperature that in return affect blood dynamics perturbing brain comfort sensations. This CL (either in summer, winter, autumn, or spring season) is a function of external environment and internal body variations that force a consumer toward EC. To develop a new concept of consumer's EC, first the authors described environment parameters in detail with relation to surroundings and EC. Considering this, they tabulated a generic relation of consumer's CL with EC and environment temperature. Second, to build an inter-related bond between the environmental effects on consumer body dynamics, they analysed theoretically and mathematically above mutual relations between medical and environmental sciences. Finally, they present their conceptual EC model based on a closed-loop feedback system. This model is a complex non-linear adaptive system with environmental and surrounding parameters as input to the system resulting in an optimised EC, considering consumer CL as a key parameter for the system.
Sahibzada Muhammad Ali; Bilal Khan; Geev Mokryani; Chaudhry Arshad Mehmood; Muhammad Jawad; Umar Farid. Environment driven consumer EC model incorporating complexities of consumer body dynamics. IET Energy Systems Integration 2019, 1, 53 -64.
AMA StyleSahibzada Muhammad Ali, Bilal Khan, Geev Mokryani, Chaudhry Arshad Mehmood, Muhammad Jawad, Umar Farid. Environment driven consumer EC model incorporating complexities of consumer body dynamics. IET Energy Systems Integration. 2019; 1 (2):53-64.
Chicago/Turabian StyleSahibzada Muhammad Ali; Bilal Khan; Geev Mokryani; Chaudhry Arshad Mehmood; Muhammad Jawad; Umar Farid. 2019. "Environment driven consumer EC model incorporating complexities of consumer body dynamics." IET Energy Systems Integration 1, no. 2: 53-64.
The earth's magnetosphere is an intricate input-output system, where the inputs are the solar wind parameters and the output measure are the geomagnetic. The disturbance storm time (DST) index is one such quantity measure for the intensity of disturbance in the geomagnetic field due to the magnetic storm. The geomagnetic activities have catastrophic effects on several communication networks and harmful for biological livings. Therefore, it is significant to develop a model for the prediction of disturbance in the geomagnetic field. This paper presents linear as well as nonlinear parametric techniques to model the complex magnetosphere dynamics. Neural network (NN)-based modeling techniques, such as feed-forward NN (FFNN), NN integrated with nonlinear autoregression with exogenous (NARX) inputs, adaptive neuro-fuzzy inference system (ANFIS), and recurrent NN are developed for the prediction of the magnetic storm. Global search algorithms, such as particle swarm optimization and genetic algorithm, also train the developed FFNNs. All the models are trained on 15 years' real-time series data of solar wind parameters and DST index on a frequency of 1 h. The accuracy of the predicted DST index for multiple intensity levels of the magnetic storm using all developed linear and nonlinear modeling techniques is presented, compared, and analyzed thoroughly. The performance of NN integrated with NARX inputs and ANFIS is higher than rest of methods developed in the paper. Both techniques have the capability to predict mild, moderate, and intense magnetic storm with high degree of accuracy. The comparative analysis with state of the art shows the enhanced accuracy and robustness of developed models in this paper.
Muhammad Jawad; Abubakar Rafique; Ikramullah Khosa; Imran Ghous; Jahanzeb Akhtar; Sahibzada Muhammad Ali. Improving Disturbance Storm Time Index Prediction Using Linear and Nonlinear Parametric Models: A Comprehensive Analysis. IEEE Transactions on Plasma Science 2018, 47, 1429 -1444.
AMA StyleMuhammad Jawad, Abubakar Rafique, Ikramullah Khosa, Imran Ghous, Jahanzeb Akhtar, Sahibzada Muhammad Ali. Improving Disturbance Storm Time Index Prediction Using Linear and Nonlinear Parametric Models: A Comprehensive Analysis. IEEE Transactions on Plasma Science. 2018; 47 (2):1429-1444.
Chicago/Turabian StyleMuhammad Jawad; Abubakar Rafique; Ikramullah Khosa; Imran Ghous; Jahanzeb Akhtar; Sahibzada Muhammad Ali. 2018. "Improving Disturbance Storm Time Index Prediction Using Linear and Nonlinear Parametric Models: A Comprehensive Analysis." IEEE Transactions on Plasma Science 47, no. 2: 1429-1444.
The evolvement of prosumers (energy producing consumers) in Smart Grid (SG) ensures reliable and efficient bi-directional power-flow. However, the prosumers interactions and interfacing within the SG system requires a bi-directional Energy Management Model, a demanding task to monitor, manage, and measure probabilistic prosumers activities. Considering weather parametric effects with Price-Based Programs and generation capacity of prosumers within Energy District (ED) is highly complex and stochastic problem, we propose Stochastic Wind Energy Management Model (SWEMM) with bi-directional energy flows between SG and Wind Energy Prosumers (WEPs). Moreover, our model incorporates an effective Service Level Agreement (SLA) design for efficient energy exchange structure between both parties (SG and WEPs). Furthermore, non-linear Stochastic price model is employed that overcomes the uncertainty of market price with SLA implementation, while wind energy estimation model within ED is employed for prosumer energy generation. The aforementioned models are incorporated for SWEMM that maximizes Prosumers Energy Surplus (PES) and minimizes Prosumer Energy Cost (PEC), while Grid Revenue (GR) maximizes for SG. Finally, data analysis (3D-plots) of Copano Bay (Texas US), model simulation and SLA validation with convergence and divergence plots with tabular statistics prove the effectiveness of our proposed model. We believe that our work is more versatile in modeling stochastic energy management model for SG and WEPs, compared to prior works.
I. Hussain; S.M. Ali; B. Khan; Z. Ullah; C.A. Mehmood; M. Jawad; U. Farid; A. Haider. Stochastic Wind Energy Management Model within smart grid framework: A joint Bi-directional Service Level Agreement (SLA) between smart grid and Wind Energy District Prosumers. Renewable Energy 2018, 134, 1017 -1033.
AMA StyleI. Hussain, S.M. Ali, B. Khan, Z. Ullah, C.A. Mehmood, M. Jawad, U. Farid, A. Haider. Stochastic Wind Energy Management Model within smart grid framework: A joint Bi-directional Service Level Agreement (SLA) between smart grid and Wind Energy District Prosumers. Renewable Energy. 2018; 134 ():1017-1033.
Chicago/Turabian StyleI. Hussain; S.M. Ali; B. Khan; Z. Ullah; C.A. Mehmood; M. Jawad; U. Farid; A. Haider. 2018. "Stochastic Wind Energy Management Model within smart grid framework: A joint Bi-directional Service Level Agreement (SLA) between smart grid and Wind Energy District Prosumers." Renewable Energy 134, no. : 1017-1033.
Power consumption cost constitutes significant portion of a data center's total operational cost. Variations in parameters, such as power demand, electricity price, and renewable power generation effects the data center's power consumption cost. Therefore, in this paper, a smart power management system based on a robust energy cost optimization algorithm is proposed for the data center. The designed robust optimization algorithm coordinates data center workload, battery bank, diesel generators, renewable power, and trade electricity price in real-time and day-ahead power market to reduce expected energy consumption cost. The uncertain parameters, such as data center workload and renewable power are computed using forecasting algorithms. The optimization algorithm is used to limit unbalance power purchase from real-time power market due to uncertainty of real-time electricity price. The algorithm formulation is obtained using mix-integer linear programming. Moreover, a model to calculate prices to be used in service level agreements with data centers' clients for on-demand cloud services is designed considering operational cost of the data center. Simulations are performed on actual data center workload, weather parameters, and electricity price. The results have shown that the proposed methodology is an effective tool to minimize data center operational cost.
Muhammad Jawad; Muhammad Bilal Qureshi; Muhammad U. S. Khan; Sahibzada M. Ali; Arshad Mehmood; Bilal Khan; Xiaoyu Wang; Samee U. Khan. A Robust Optimization Technique for Energy Cost Minimization of Cloud Data Centers. IEEE Transactions on Cloud Computing 2018, 9, 447 -460.
AMA StyleMuhammad Jawad, Muhammad Bilal Qureshi, Muhammad U. S. Khan, Sahibzada M. Ali, Arshad Mehmood, Bilal Khan, Xiaoyu Wang, Samee U. Khan. A Robust Optimization Technique for Energy Cost Minimization of Cloud Data Centers. IEEE Transactions on Cloud Computing. 2018; 9 (2):447-460.
Chicago/Turabian StyleMuhammad Jawad; Muhammad Bilal Qureshi; Muhammad U. S. Khan; Sahibzada M. Ali; Arshad Mehmood; Bilal Khan; Xiaoyu Wang; Samee U. Khan. 2018. "A Robust Optimization Technique for Energy Cost Minimization of Cloud Data Centers." IEEE Transactions on Cloud Computing 9, no. 2: 447-460.
Electrical load and wind power forecasting are a demanding task for modern electrical power systems because both are closely linked with the weather parameters, such as temperature, humidity, and air pressure. The conventional methods of electrical load and wind power forecasting are useful to handle dynamic and uncertainties in un-regulated energy markets. However, there is still need of relative improvement by incorporating weather parameter dependencies. Considering above, a genetic algorithm-based non-linear auto-regressive neural network (GA-NARX-NN) model for short- and medium-term electrical load forecasting is presented with relative degree of accuracy. Causality, a new modelling technique, is employed for monthly and yearly wind speed patterns predictions and long-term wind speed forecasting. Real-time historical electrical load and weather parametric data are used to critically observe the performance of the proposed models compared to various state-of-the-art forecasting schemes. Numerical simulations are conducted that validates the proposed models based on various error calculation methods, such as mean absolute percentage error, root mean-square error, and variance (). The quantitative comparison with five traditional techniques for electrical load and wind speed forecasting reveals that the GA-NARX-NN method is more accurate and reliable.
Muhammad Jawad; Sahibzada M. Ali; Bilal Khan; Chaudry A. Mehmood; Umar Farid; Zahid Ullah; Saeeda Usman; Ahmad Fayyaz; Jabran Jadoon; Nauman Tareen; Abdul Basit; Muhammad A. Rustam; Irfan Sami. Genetic algorithm‐based non‐linear auto‐regressive with exogenous inputs neural network short‐term and medium‐term uncertainty modelling and prediction for electrical load and wind speed. The Journal of Engineering 2018, 2018, 721 -729.
AMA StyleMuhammad Jawad, Sahibzada M. Ali, Bilal Khan, Chaudry A. Mehmood, Umar Farid, Zahid Ullah, Saeeda Usman, Ahmad Fayyaz, Jabran Jadoon, Nauman Tareen, Abdul Basit, Muhammad A. Rustam, Irfan Sami. Genetic algorithm‐based non‐linear auto‐regressive with exogenous inputs neural network short‐term and medium‐term uncertainty modelling and prediction for electrical load and wind speed. The Journal of Engineering. 2018; 2018 (8):721-729.
Chicago/Turabian StyleMuhammad Jawad; Sahibzada M. Ali; Bilal Khan; Chaudry A. Mehmood; Umar Farid; Zahid Ullah; Saeeda Usman; Ahmad Fayyaz; Jabran Jadoon; Nauman Tareen; Abdul Basit; Muhammad A. Rustam; Irfan Sami. 2018. "Genetic algorithm‐based non‐linear auto‐regressive with exogenous inputs neural network short‐term and medium‐term uncertainty modelling and prediction for electrical load and wind speed." The Journal of Engineering 2018, no. 8: 721-729.
N. Shaukat; S.M. Ali; C.A. Mehmood; B. Khan; Muhammad Jawad; U. Farid; Z. Ullah; Syed Anwar; Muhammad Majid. A survey on consumers empowerment, communication technologies, and renewable generation penetration within Smart Grid. Renewable and Sustainable Energy Reviews 2018, 81, 1453 -1475.
AMA StyleN. Shaukat, S.M. Ali, C.A. Mehmood, B. Khan, Muhammad Jawad, U. Farid, Z. Ullah, Syed Anwar, Muhammad Majid. A survey on consumers empowerment, communication technologies, and renewable generation penetration within Smart Grid. Renewable and Sustainable Energy Reviews. 2018; 81 ():1453-1475.
Chicago/Turabian StyleN. Shaukat; S.M. Ali; C.A. Mehmood; B. Khan; Muhammad Jawad; U. Farid; Z. Ullah; Syed Anwar; Muhammad Majid. 2018. "A survey on consumers empowerment, communication technologies, and renewable generation penetration within Smart Grid." Renewable and Sustainable Energy Reviews 81, no. : 1453-1475.