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Cloud Computing (CC) is a promising technology due to its pervasive features, such as online storage, high scalability, and seamless accessibility, in that it plays an important role in reduction of the capital cost and workforce, which attracts organizations to conduct their businesses and financial activities over the cloud. Even though CC is a great innovation in the aspect of computing with ease of access, it also has some drawbacks. With the increase of cloud usage, security issues are proportional to the increase. To address these, there has been much work done in this domain, whereas research work considering the growing constrained applications provided by the Internet of Things (IoT) and smart city networks are still lacking. In this survey, we provide a comprehensive security analysis of CC-enabled IoT and present state-of-the-art in the research area. Finally, future research work and possible areas of implementation and consideration are given to discuss open issues.
Abeer Tahirkheli; Muhammad Shiraz; Bashir Hayat; Muhammad Idrees; Ahthasham Sajid; Rahat Ullah; Nasir Ayub; Ki-Il Kim. A Survey on Modern Cloud Computing Security over Smart City Networks: Threats, Vulnerabilities, Consequences, Countermeasures, and Challenges. Electronics 2021, 10, 1811 .
AMA StyleAbeer Tahirkheli, Muhammad Shiraz, Bashir Hayat, Muhammad Idrees, Ahthasham Sajid, Rahat Ullah, Nasir Ayub, Ki-Il Kim. A Survey on Modern Cloud Computing Security over Smart City Networks: Threats, Vulnerabilities, Consequences, Countermeasures, and Challenges. Electronics. 2021; 10 (15):1811.
Chicago/Turabian StyleAbeer Tahirkheli; Muhammad Shiraz; Bashir Hayat; Muhammad Idrees; Ahthasham Sajid; Rahat Ullah; Nasir Ayub; Ki-Il Kim. 2021. "A Survey on Modern Cloud Computing Security over Smart City Networks: Threats, Vulnerabilities, Consequences, Countermeasures, and Challenges." Electronics 10, no. 15: 1811.
The 2019 novel coronavirus (COVID-19) originating from China, has spread rapidly among people living in other countries. According to the World Health Organization (WHO), by the end of January, more than 104 million people have been affected by COVID-19, including more than 2 million deaths. The number of COVID-19 test kits available in hospitals is reduced due to the increase in regular cases. Therefore, an automatic detection system should be introduced as a fast, alternative diagnostic to prevent COVID-19 from spreading among humans. For this purpose, three different BiT models: DenseNet, InceptionV3, and Inception-ResNetV4 have been proposed in this analysis for the diagnosis of patients infected with coronavirus pneumonia using X-ray radiographs in the chest. These three models give and examine Receiver Operating Characteristic (ROC) analyses and uncertainty matrices, using 5-fold cross-validation. We have performed the simulations which have visualized that the pre-trained DenseNet model has the best classification efficiency with 92% among two other models proposed (83.47% accuracy for inception V3 and 85.57% accuracy for Inception-ResNetV4).
Saleh Albahli; Nasir Ayub; Muhammad Shiraz. Coronavirus disease (COVID-19) detection using X-ray images and enhanced DenseNet. Applied Soft Computing 2021, 110, 107645 -107645.
AMA StyleSaleh Albahli, Nasir Ayub, Muhammad Shiraz. Coronavirus disease (COVID-19) detection using X-ray images and enhanced DenseNet. Applied Soft Computing. 2021; 110 ():107645-107645.
Chicago/Turabian StyleSaleh Albahli; Nasir Ayub; Muhammad Shiraz. 2021. "Coronavirus disease (COVID-19) detection using X-ray images and enhanced DenseNet." Applied Soft Computing 110, no. : 107645-107645.
Sentiment analysis is one of the most prominent sub-areas of research in Natural Language Processing (NLP), where it is important to consider implicit or explicit emotions conveyed by review material. Researchers also recognized that the generic feelings derived from the textual material are insufficient, so the sentiment analysis aspect based was coined to extract the emotions from textual data. Multi-labeling based on aspects data can resolve the issue of extracting emotion aspect based. In this work, a novel approach namely: Evolutionary Ensembler (EEn) is proposed to effectively boost the accuracy and diversity of multi-label learners. Unlike traditional multi-label training methods, EEn emphasizes the accuracy and diversity of multi-label-based models. We have used seven datasets (medical, hotel, movies, automobiles, proteins, birds, emotions, news). At first, we applied a pre-processing step to gain the refined and clean data. Second, we have applied the Vader tool with Bag of Words (BoW) for the feature extraction. Third, the word2vec method is applied to draw an association between words. Moreover, the SVM model (tuned with GA) is trained and tested on the refined data. The accuracy of the aspect-based multi-labeling using the SVM-GA on medical, hotel, movies, automobiles, proteins, birds, emotions, news are 93.13%, 94.32%, 94.0%, 95.10%, 90.20%, 93.22%, 90.0%, and 94.0%, respectively. Our proposed model with different dimensions of multi-label datasets shows that EEn is vastly superior to other popular techniques. Experimental outcomes validate the success of the implemented approach among existing benchmark techniques.
Khursheed Aurangzeb; Nasir Ayub; Musaed Alhussein. Aspect Based Multi-Labeling Using SVM Based Ensembler. IEEE Access 2021, 9, 26026 -26040.
AMA StyleKhursheed Aurangzeb, Nasir Ayub, Musaed Alhussein. Aspect Based Multi-Labeling Using SVM Based Ensembler. IEEE Access. 2021; 9 (99):26026-26040.
Chicago/Turabian StyleKhursheed Aurangzeb; Nasir Ayub; Musaed Alhussein. 2021. "Aspect Based Multi-Labeling Using SVM Based Ensembler." IEEE Access 9, no. 99: 26026-26040.
Besides the non-technical losses of power companies, theft of electricity is the most serious and dangerous one. The fraudulent power consumption degrades the quality of supply and increases the energy generation that impacts the whole grid system, which causes the legitimate users to pay a huge amount of electricity bills. Through data analysis methods, Smart Grid (SG) adaptation can significantly reduce this loss. SG infrastructure produces large amounts of data, including electricity consumer consumption. Machine learning and deep learning methods are using this historical record of user's data and can identify who steals electricity. Theft detection system is proposed in this paper, which consists of Convolutional Neural Network (CNN) and Gated Recurrent Unit (GRU) architecture. CNN is a widely used technology that can perform feature extraction automatically. Moreover, CNN also performs classification process with the power consumption of time series data. To perform classification in smart grid, we established a CNN-based GRU (CNN-GRU) model. Also, the hyper parameters of CNN-GRU are set tuned with a swarm based optimization algorithm Manta Ray Foraging Optimization (MRFO). Further, a pre-processing algorithm is implemented to calculate the missing values in the dataset, which is based on the local value associated with the missing data point. Also in this dataset, relatively few users have power theft and the model may not be able to effectively identify the steal users. Such imbalances were solved by comprehensive data generation using Synthetic Minority Over-sampling Technique (SMOTE). In the end, the obtained results show that our proposed technique can better classify most categories (normal users) and most minorities (electric theft users). The accuracy of CNN-GRU-MRFO is 91.1%, which is 6% higher than actual CNN-GRU.
Nasir Ayub; Khursheed Aurangzeb; Muhammad Awais; Usman Ali. Electricity Theft Detection using CNN-GRU and Manta Ray Foraging Optimization Algorithm. 2020 IEEE 23rd International Multitopic Conference (INMIC) 2020, 1 -6.
AMA StyleNasir Ayub, Khursheed Aurangzeb, Muhammad Awais, Usman Ali. Electricity Theft Detection using CNN-GRU and Manta Ray Foraging Optimization Algorithm. 2020 IEEE 23rd International Multitopic Conference (INMIC). 2020; ():1-6.
Chicago/Turabian StyleNasir Ayub; Khursheed Aurangzeb; Muhammad Awais; Usman Ali. 2020. "Electricity Theft Detection using CNN-GRU and Manta Ray Foraging Optimization Algorithm." 2020 IEEE 23rd International Multitopic Conference (INMIC) , no. : 1-6.
Cloud computing is rapidly taking over the information technology industry because it makes computing a lot easier without worries of buying the physical hardware needed for computations, rather, these services are hosted by companies with provide the cloud services. These companies contain a lot of computers and servers whose main source of power is electricity, hence, design and maintenance of these companies is dependent on the availability of steady and cheap electrical power supply. Cloud centers are energy-hungry. With recent spikes in electricity prices, one of the main challenges in designing and maintenance of such centers is to minimize electricity consumption of data centers and save energy. Efficient data placement and node scheduling to offload or move storage are some of the main approaches to solve these problems. In this paper, we propose an Extreme Gradient Boosting (XGBoost) model to offload or move storage, predict electricity price, and as a result reduce energy consumption costs in data centers. The performance of this method is evaluated on a real-world dataset provided by the Independent Electricity System Operator (IESO) in Ontario, Canada, to offload data storage in data centers and efficiently decrease energy consumption. The data is split into 70% training and 30% testing. We have trained our proposed model on the data and validate our model on the testing data. The results indicate that our model can predict electricity prices with a mean squared error (MSE) of 15.66 and mean absolute error (MAE) of 3.74% respectively, which can result in 25.32% cut in electricity costs. The accuracy of our proposed technique is 91% while the accuracy of benchmark algorithms RF and SVR is 89% and 88%, respectively.
Saleh Albahli; Muhammad Shiraz; Nasir Ayub. Electricity Price Forecasting for Cloud Computing Using an Enhanced Machine Learning Model. IEEE Access 2020, 8, 200971 -200981.
AMA StyleSaleh Albahli, Muhammad Shiraz, Nasir Ayub. Electricity Price Forecasting for Cloud Computing Using an Enhanced Machine Learning Model. IEEE Access. 2020; 8 (99):200971-200981.
Chicago/Turabian StyleSaleh Albahli; Muhammad Shiraz; Nasir Ayub. 2020. "Electricity Price Forecasting for Cloud Computing Using an Enhanced Machine Learning Model." IEEE Access 8, no. 99: 200971-200981.
Electrical load forecasting provides knowledge about future consumption and generation of electricity. There is a high level of fluctuation behavior between energy generation and consumption. Sometimes, the energy demand of the consumer becomes higher than the energy already generated, and vice versa. Electricity load forecasting provides a monitoring framework for future energy generation, consumption, and making a balance between them. In this paper, we propose a framework, in which deep learning and supervised machine learning techniques are implemented for electricity-load forecasting. A three-step model is proposed, which includes: feature selection, extraction, and classification. The hybrid of Random Forest (RF) and Extreme Gradient Boosting (XGB) is used to calculate features’ importance. The average feature importance of hybrid techniques selects the most relevant and high importance features in the feature selection method. The Recursive Feature Elimination (RFE) method is used to eliminate the irrelevant features in the feature extraction method. The load forecasting is performed with Support Vector Machines (SVM) and a hybrid of Gated Recurrent Units (GRU) and Convolutional Neural Networks (CNN). The meta-heuristic algorithms, i.e., Grey Wolf Optimization (GWO) and Earth Worm Optimization (EWO) are applied to tune the hyper-parameters of SVM and CNN-GRU, respectively. The accuracy of our enhanced techniques CNN-GRU-EWO and SVM-GWO is 96.33% and 90.67%, respectively. Our proposed techniques CNN-GRU-EWO and SVM-GWO perform 7% and 3% better than the State-Of-The-Art (SOTA). In the end, a comparison with SOTA techniques is performed to show the improvement of the proposed techniques. This comparison showed that the proposed technique performs well and results in the lowest performance error rates and highest accuracy rates as compared to other techniques.
Nasir Ayub; Muhammad Irfan; Muhammad Awais; Usman Ali; Tariq Ali; Mohammed Hamdi; Abdullah Alghamdi; Fazal Muhammad. Big Data Analytics for Short and Medium-Term Electricity Load Forecasting Using an AI Techniques Ensembler. Energies 2020, 13, 5193 .
AMA StyleNasir Ayub, Muhammad Irfan, Muhammad Awais, Usman Ali, Tariq Ali, Mohammed Hamdi, Abdullah Alghamdi, Fazal Muhammad. Big Data Analytics for Short and Medium-Term Electricity Load Forecasting Using an AI Techniques Ensembler. Energies. 2020; 13 (19):5193.
Chicago/Turabian StyleNasir Ayub; Muhammad Irfan; Muhammad Awais; Usman Ali; Tariq Ali; Mohammed Hamdi; Abdullah Alghamdi; Fazal Muhammad. 2020. "Big Data Analytics for Short and Medium-Term Electricity Load Forecasting Using an AI Techniques Ensembler." Energies 13, no. 19: 5193.
Forecasting the electricity load provides its future trends, consumption patterns and its usage. There is no proper strategy to monitor the energy consumption and generation; and high variation among them. Many strategies are used to overcome this problem. The correct selection of parameter values of a classifier is still an issue. Therefore, an optimization algorithm is applied with deep learning and machine learning techniques to select the optimized values for the classifier’s hyperparameters. In this paper, a novel deep learning-based method is implemented for electricity load forecasting. A three-step model is also implemented, including feature selection using a hybrid feature selector (XGboost and decision tee), redundancy removal using feature extraction technique (Recursive Feature Elimination) and classification/forecasting using improved Support Vector Machine (SVM) and Extreme Learning Machine (ELM). The hyperparameters of ELM are tuned with a meta-heuristic algorithm, i.e., Genetic Algorithm (GA) and hyperparameters of SVM are tuned with the Grid Search Algorithm. The simulation results are shown in graphs and the values are shown in tabular form and they clearly show that our improved methods outperform State Of The Art (SOTA) methods in terms of accuracy and performance. The forecasting accuracy of Extreme Learning Machine based Genetic Algo (ELM-GA) and Support Vector Machine based Grid Search (SVM-GS) is 96.3% and 93.25%, respectively. The accuracy of our improved techniques, i.e., ELM-GA and SVM-GS is 10% and 7%, respectively, higher than the SOTA techniques.
Waqas Ahmad; Nasir Ayub; Tariq Ali; Muhammad Irfan; Muhammad Awais; Muhammad Shiraz; Adam Glowacz. Towards Short Term Electricity Load Forecasting Using Improved Support Vector Machine and Extreme Learning Machine. Energies 2020, 13, 2907 .
AMA StyleWaqas Ahmad, Nasir Ayub, Tariq Ali, Muhammad Irfan, Muhammad Awais, Muhammad Shiraz, Adam Glowacz. Towards Short Term Electricity Load Forecasting Using Improved Support Vector Machine and Extreme Learning Machine. Energies. 2020; 13 (11):2907.
Chicago/Turabian StyleWaqas Ahmad; Nasir Ayub; Tariq Ali; Muhammad Irfan; Muhammad Awais; Muhammad Shiraz; Adam Glowacz. 2020. "Towards Short Term Electricity Load Forecasting Using Improved Support Vector Machine and Extreme Learning Machine." Energies 13, no. 11: 2907.
In the last few years, carbon emissions and energy demand have increased dramatically around the globe due to a surge in population and energy-consuming devices. The integration of renewable energy resources (RERs) in a power supply system provides an efficient solution in terms of low energy cost with lower carbon emissions. However, renewable sources like solar panels have irregular nature of power generation because of their dependence on weather conditions, such as solar radiation, humidity, and temperature. Therefore, to tackle this intermittent nature of solar energy, power prediction is necessary for efficient energy management. Deep learning and machine learning-based methods have frequently been implemented for energy forecasting in the literature. The current work summarizes the state-of-theart deep learning-based methods that are proposed to forecast the solar power for proper energy management. We also explain the methodologies of solar energy forecasting along with their outcomes. At the end, future challenges and opportunities are uncovered in the application of deep and machine learning in this area.
Sheraz Aslam; Herodotos Herodotou; Nasir Ayub; Syed Muhammad Mohsin. Deep Learning Based Techniques to Enhance the Performance of Microgrids: A Review. 2019 International Conference on Frontiers of Information Technology (FIT) 2019, 116 -1165.
AMA StyleSheraz Aslam, Herodotos Herodotou, Nasir Ayub, Syed Muhammad Mohsin. Deep Learning Based Techniques to Enhance the Performance of Microgrids: A Review. 2019 International Conference on Frontiers of Information Technology (FIT). 2019; ():116-1165.
Chicago/Turabian StyleSheraz Aslam; Herodotos Herodotou; Nasir Ayub; Syed Muhammad Mohsin. 2019. "Deep Learning Based Techniques to Enhance the Performance of Microgrids: A Review." 2019 International Conference on Frontiers of Information Technology (FIT) , no. : 116-1165.
One of the key issues in the Smart Grid (SG) is accurate electric load forecasting. Energy generation and consumption have highly varying. Accurate forecasting of electric load can decrease the fluctuating behavior between energy generation and consumption. By knowing the upcoming electricity load consumption, we can control the extra energy generation. To solve this issue, we have proposed a forecasting model, which consists of a two-stage process; feature engineering and classification. Feature engineering consists of feature selection and extraction. By combining Extreme Gradient Boosting (XGBoost) and Decision Tree (DT) techniques, we have proposed a hybrid feature selector to minimize the feature redundancy. Furthermore, Recursive Feature Elimination (RFE) technique is applied for dimension reduction and improve feature selection. To forecast electric load, we have applied Support Vector Machine (SVM) set tuned with three super parameters, i.e., kernel parameter, cost penalty, and incentive loss function parameter. Electricity market data is used in our proposed model. Weekly and months ahead forecasting experiments are conducted by proposed model. Forecasting performance is assessed by using RMSE and MAPE and their values are 1.682 and 12.364. The simulation results show 98% load forecasting accuracy.
Nasir Ayub; Nadeem Javaid; Sana Mujeeb; Maheen Zahid; Wazir Zada Khan; Muhammad Umar Khattak. Electricity Load Forecasting in Smart Grids Using Support Vector Machine. Advances in Intelligent Systems and Computing 2019, 1 -13.
AMA StyleNasir Ayub, Nadeem Javaid, Sana Mujeeb, Maheen Zahid, Wazir Zada Khan, Muhammad Umar Khattak. Electricity Load Forecasting in Smart Grids Using Support Vector Machine. Advances in Intelligent Systems and Computing. 2019; ():1-13.
Chicago/Turabian StyleNasir Ayub; Nadeem Javaid; Sana Mujeeb; Maheen Zahid; Wazir Zada Khan; Muhammad Umar Khattak. 2019. "Electricity Load Forecasting in Smart Grids Using Support Vector Machine." Advances in Intelligent Systems and Computing , no. : 1-13.
The present strategies for the prediction of price and load may be difficult to deal with huge amount of load and price data. To resolve the problem, three modules are incorporated within the model. Firstly, the fusion of Decision Tree (DT) and Random Forest (RF) are used for feature selection and to remove the redundancy among feature. Secondly, Recursive Feature Elimination (RFE) is taken for feature extraction purpose that extracts the principle components and also used for dimensionality reduction. Finally, to forecast load and price, Support Vector Machine (SVM) and Logistic Regression (LR) as a classifiers are used through which we achieve good accuracy results in load and price prediction.
Syeda Aimal; Nadeem Javaid; Amjad Rehman; Nasir Ayub; Tanzeela Sultana; Aroosa Tahir. Data Analytics for Electricity Load and Price Forecasting in the Smart Grid. Advances in Intelligent Systems and Computing 2019, 582 -591.
AMA StyleSyeda Aimal, Nadeem Javaid, Amjad Rehman, Nasir Ayub, Tanzeela Sultana, Aroosa Tahir. Data Analytics for Electricity Load and Price Forecasting in the Smart Grid. Advances in Intelligent Systems and Computing. 2019; ():582-591.
Chicago/Turabian StyleSyeda Aimal; Nadeem Javaid; Amjad Rehman; Nasir Ayub; Tanzeela Sultana; Aroosa Tahir. 2019. "Data Analytics for Electricity Load and Price Forecasting in the Smart Grid." Advances in Intelligent Systems and Computing , no. : 582-591.
Energy is the most needed commodity of the current era. Recently, the demand of energy is far higher than the available energy. By the incorporation of Demand Side Management (DSM) with the Smart Grid (SG) results in the solution of this problem. Different techniques are utilized in SG to minimize the electricity cost and manage load in industrial, residential areas, and commercial to minimize the Peak to Average Ratio (PAR) and decrease in the waiting time of appliances which leads to maximize user comfort. In this article, we propose six Meta heuristic techniques in Home Energy Management System (HEMS); Firefly Algorithm (FA), Bacterial foraging Algorithm (BFA), Earth Worm Optimization Algorithm (EWA), Genetic Algorithm (GA), Hybrid of Genetic and Bacterial foraging (HBG), and Harmony Search Algorithm (HSA). We have achieved minimization in PAR, electric cost, upturn user comfort through appliances scheduling using the optimization techniques.
Nasir Ayub; Nadeem Javaid; Assad Abbas; Adnan Ishaq; Anam Yousaf; Muhammad Awais Ishtiaq. Demand Side Management Scheduling of Appliances Using Meta Heuristic Algorithms. Advances on P2P, Parallel, Grid, Cloud and Internet Computing 2018, 405 -417.
AMA StyleNasir Ayub, Nadeem Javaid, Assad Abbas, Adnan Ishaq, Anam Yousaf, Muhammad Awais Ishtiaq. Demand Side Management Scheduling of Appliances Using Meta Heuristic Algorithms. Advances on P2P, Parallel, Grid, Cloud and Internet Computing. 2018; ():405-417.
Chicago/Turabian StyleNasir Ayub; Nadeem Javaid; Assad Abbas; Adnan Ishaq; Anam Yousaf; Muhammad Awais Ishtiaq. 2018. "Demand Side Management Scheduling of Appliances Using Meta Heuristic Algorithms." Advances on P2P, Parallel, Grid, Cloud and Internet Computing , no. : 405-417.
In this paper, appliance scheduling scheme is proposed for residential area. Different types of heuristic and meta-heuristic optimization techniques are being used to solve the general problem of electricity demand. In this paper, a unique swarm based optimization technique Elephant Herding Optimization (EHO) is used to manage the electricity demand in order to manage the single home appliances in such a way that reduction of electricity cost is achieved and certain point of user comfort. For this purpose Real Time Pricing (RTP) scheme is used in this paper for electricity cost. To validate the effectiveness of proposed scheme simulations are performed. The results of EHO are compared with the results of Enhanced Differential Evolution (EDE). The simulations show that proposed scheme i.e. EHO provide best optimal results in achieving the minimum electricity cost and user comfort at certain point.
Basit Amin; Adia Khalid; Muhammad Azeem Sarwar; Asad Ghaffar; Adnan Satti; Nasir Ayub; Nadeem Javaid. Real Time Pricing Based Appliance Scheduling in Home Energy Management Using Optimization Techniques. Advances on P2P, Parallel, Grid, Cloud and Internet Computing 2017, 3 -13.
AMA StyleBasit Amin, Adia Khalid, Muhammad Azeem Sarwar, Asad Ghaffar, Adnan Satti, Nasir Ayub, Nadeem Javaid. Real Time Pricing Based Appliance Scheduling in Home Energy Management Using Optimization Techniques. Advances on P2P, Parallel, Grid, Cloud and Internet Computing. 2017; ():3-13.
Chicago/Turabian StyleBasit Amin; Adia Khalid; Muhammad Azeem Sarwar; Asad Ghaffar; Adnan Satti; Nasir Ayub; Nadeem Javaid. 2017. "Real Time Pricing Based Appliance Scheduling in Home Energy Management Using Optimization Techniques." Advances on P2P, Parallel, Grid, Cloud and Internet Computing , no. : 3-13.
Smart grid based energy management system promises an efficient consumption of electricity. For optimized energy consumption, a bio inspired meta-heuristic algorithms: Earth Worm Algorithm (EWA) and Bacterial Foraging Algorithm (BFA) are presented in this paper. In this work, we targeted residential area. Our aim is to reduce the electricity cost and Peak to Average Ratio (PAR). We have used the Critical Peak Pricing (CPP) scheme for calculating electricity bill. Through simulations, we have compared the results of EWA, BFA and unscheduled appliances. After implementing our techniques, EWA based energy management controller gives more efficient results than BFA in term of cost, while for PAR reduction, BFA performs better than EWA.
Mudabbir Ali; Samia Abid; Asad Ghafar; Nasir Ayub; Hafsa Arshad; Sajawal Khan; Nadeem Javaid. Earth Worm Optimization for Home Energy Management System in Smart Grid. Advances on P2P, Parallel, Grid, Cloud and Internet Computing 2017, 583 -596.
AMA StyleMudabbir Ali, Samia Abid, Asad Ghafar, Nasir Ayub, Hafsa Arshad, Sajawal Khan, Nadeem Javaid. Earth Worm Optimization for Home Energy Management System in Smart Grid. Advances on P2P, Parallel, Grid, Cloud and Internet Computing. 2017; ():583-596.
Chicago/Turabian StyleMudabbir Ali; Samia Abid; Asad Ghafar; Nasir Ayub; Hafsa Arshad; Sajawal Khan; Nadeem Javaid. 2017. "Earth Worm Optimization for Home Energy Management System in Smart Grid." Advances on P2P, Parallel, Grid, Cloud and Internet Computing , no. : 583-596.
Nowadays, Energy become the most valued necessity. Energy crisis becomes a critical issue of this era. Energy demand is increasing day by day, due to which peak load creation occurs. In order to handle the critical situation of the energy crisis, many techniques and methods are implemented. This can be done by replacing the traditional grid with smart grid and scheduling of appliances at Demand Side Management (DSM). Our main focus is on load management and minimization of cost which can be done by load shifting from on peak hours to off peak hours. We have achieved objectives by using two meta-heuristic optimization techniques; Harmony Search Algorithm (HSA) and EarthWorm optimization Algorithm (EWA). Simulation results show that the approaches we adopted reduce the cost, reduce the Peak Average Ratio (PAR) by load shifting from on peak to off peak hours between the min and max interval with a low difference.
Nasir Ayub; Adnan Ishaq; Mudabbir Ali; Muhammad Azeem Sarwar; Basit Amin; Nadeem Javaid. An Efficient Scheduling of Power and Appliances Using Metaheuristic Optimization Technique. Advances on P2P, Parallel, Grid, Cloud and Internet Computing 2017, 178 -190.
AMA StyleNasir Ayub, Adnan Ishaq, Mudabbir Ali, Muhammad Azeem Sarwar, Basit Amin, Nadeem Javaid. An Efficient Scheduling of Power and Appliances Using Metaheuristic Optimization Technique. Advances on P2P, Parallel, Grid, Cloud and Internet Computing. 2017; ():178-190.
Chicago/Turabian StyleNasir Ayub; Adnan Ishaq; Mudabbir Ali; Muhammad Azeem Sarwar; Basit Amin; Nadeem Javaid. 2017. "An Efficient Scheduling of Power and Appliances Using Metaheuristic Optimization Technique." Advances on P2P, Parallel, Grid, Cloud and Internet Computing , no. : 178-190.
Energy consumption demand is comparatively higher than available energy, new approaches are being discovered to fulfill energy demand. This problem can be solved by assimilating Demand Side Management (DSM) with Smart Grid (SG). In this work, we observe the working of Home Energy Management System (HEMS) by using three meta-heuristic techniques; Harmony Search Algorithm (HSA) and Firefly Algorithm (FA) and Bacterial Foraging Algorithm (BFA). Time Of Use (TOU) is used as a pricing signal for calculation of electricity bill. The main concern of this paper is to minimize cost, reduce Peak to Average Ratio (PAR), maximization of user comfort and load management. Load management can be done by shifting load from on-peak hours to off-peak hours. Simulation results show that implemented techniques successfully achieve the defined goals.
Adnan Ishaq; Nasir Ayub; Arje Saba; Asad Ghafar; Basit Amin; Nadeem Javaid. An Efficient Scheduling Using Meta Heuristic Algorithms for Home Demand-side Management in Smart Grid. Advances on P2P, Parallel, Grid, Cloud and Internet Computing 2017, 214 -227.
AMA StyleAdnan Ishaq, Nasir Ayub, Arje Saba, Asad Ghafar, Basit Amin, Nadeem Javaid. An Efficient Scheduling Using Meta Heuristic Algorithms for Home Demand-side Management in Smart Grid. Advances on P2P, Parallel, Grid, Cloud and Internet Computing. 2017; ():214-227.
Chicago/Turabian StyleAdnan Ishaq; Nasir Ayub; Arje Saba; Asad Ghafar; Basit Amin; Nadeem Javaid. 2017. "An Efficient Scheduling Using Meta Heuristic Algorithms for Home Demand-side Management in Smart Grid." Advances on P2P, Parallel, Grid, Cloud and Internet Computing , no. : 214-227.
In this study, problem of scheduling of appliances in Home Energy Management System (HEMS) is analyzed and a solution is proposed. Although there are many heuristic algorithms for solving the scheduling problem however we considered a swarm based heuristic algorithm Elephant Herding Optimisation (EHO). EHO uses the herding behaviour of elephants to handle the problem. To validate our research work, we simulate the single home with 12 appliances and scheduling is performed using EHO. We divided the appliances into two categories Interruptible and non-interruptible. Time of Use (TOU) pricing signal is used. Simulation results show that EHO is efficient as compare to Enhanced Differential Evolution (EDE) and unscheduled case. EHO technique is efficient in scheduling the appliances and reducing the waiting time.
Muhammad Azeem Sarwar; Basit Amin; Nasir Ayub; Syed Hassnain Faraz; Sajawal Ur Rehman Khan; Nadeem Javaid. Scheduling of Appliances in Home Energy Management System Using Elephant Herding Optimization and Enhanced Differential Evolution. Advances on P2P, Parallel, Grid, Cloud and Internet Computing 2017, 132 -142.
AMA StyleMuhammad Azeem Sarwar, Basit Amin, Nasir Ayub, Syed Hassnain Faraz, Sajawal Ur Rehman Khan, Nadeem Javaid. Scheduling of Appliances in Home Energy Management System Using Elephant Herding Optimization and Enhanced Differential Evolution. Advances on P2P, Parallel, Grid, Cloud and Internet Computing. 2017; ():132-142.
Chicago/Turabian StyleMuhammad Azeem Sarwar; Basit Amin; Nasir Ayub; Syed Hassnain Faraz; Sajawal Ur Rehman Khan; Nadeem Javaid. 2017. "Scheduling of Appliances in Home Energy Management System Using Elephant Herding Optimization and Enhanced Differential Evolution." Advances on P2P, Parallel, Grid, Cloud and Internet Computing , no. : 132-142.