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The electrical losses in power systems are divided into non-technical losses (NTLs) and technical losses (TLs). NTL is more harmful than TL because it includes electricity theft, faulty meters and billing errors. It is one of the major concerns in the power system worldwide and incurs a huge revenue loss for utility companies. Electricity theft detection (ETD) is the mechanism used by industry and academia to detect electricity theft. However, due to imbalanced data, overfitting issues and the handling of high-dimensional data, the ETD cannot be applied efficiently. Therefore, this paper proposes a solution to address the above limitations. A long short-term memory (LSTM) technique is applied to detect abnormal patterns in electricity consumption data along with the bat-based random under-sampling boosting (RUSBoost) technique for parameter optimization. Our proposed system model uses the normalization and interpolation methods to pre-process the electricity data. Afterwards, the pre-processed data are fed into the LSTM module for feature extraction. Finally, the selected features are passed to the RUSBoost module for classification. The simulation results show that the proposed solution resolves the issues of data imbalancing, overfitting and the handling of massive time series data. Additionally, the proposed method outperforms the state-of-the-art techniques; i.e., support vector machine (SVM), convolutional neural network (CNN) and logistic regression (LR). Moreover, the F1-score, precision, recall and receiver operating characteristics (ROC) curve metrics are used for the comparative analysis.
Muhammad Adil; Nadeem Javaid; Umar Qasim; Ibrar Ullah; Muhammad Shafiq; Jin-Ghoo Choi. LSTM and Bat-Based RUSBoost Approach for Electricity Theft Detection. Applied Sciences 2020, 10, 4378 .
AMA StyleMuhammad Adil, Nadeem Javaid, Umar Qasim, Ibrar Ullah, Muhammad Shafiq, Jin-Ghoo Choi. LSTM and Bat-Based RUSBoost Approach for Electricity Theft Detection. Applied Sciences. 2020; 10 (12):4378.
Chicago/Turabian StyleMuhammad Adil; Nadeem Javaid; Umar Qasim; Ibrar Ullah; Muhammad Shafiq; Jin-Ghoo Choi. 2020. "LSTM and Bat-Based RUSBoost Approach for Electricity Theft Detection." Applied Sciences 10, no. 12: 4378.
Energy consumption is increasing exponentially with the increase in electronic gadgets. Losses occur during generation, transmission, and distribution. The energy demand leads to increase in electricity theft (ET) in distribution side. Data analysis is the process of assessing the data using different analytical and statistical tools to extract useful information. Fluctuation in energy consumption patterns indicates electricity theft. Utilities bear losses of millions of dollar every year. Hardware-based solutions are considered to be the best; however, the deployment cost of these solutions is high. Software-based solutions are data-driven and cost-effective. We need big data for analysis and artificial intelligence and machine learning techniques. Several solutions have been proposed in existing studies; however, low detection performance and high false positive rate are the major issues. In this paper, we first time employ bidirectional Gated Recurrent Unit for ET detection for classification using real time-series data. We also propose a new scheme, which is a combination of oversampling technique Synthetic Minority Oversampling TEchnique (SMOTE) and undersampling technique Tomek Link: “Smote Over Sampling Tomik Link (SOSTLink) sampling technique”. The Kernel Principal Component Analysis is used for feature extraction. In order to evaluate the proposed model’s performance, five performance metrics are used, including precision, recall, F1-score, Root Mean Square Error (RMSE), and receiver operating characteristic curve. Experiments show that our proposed model outperforms the state-of-the-art techniques: logistic regression, decision tree, random forest, support vector machine, convolutional neural network, long short-term memory, hybrid of multilayer perceptron and convolutional neural network.
Hira Gul; Nadeem Javaid; Ibrar Ullah; Ali Qamar; Muhammad Khalil Afzal; Gyanendra Prasad Joshi. Detection of Non-Technical Losses Using SOSTLink and Bidirectional Gated Recurrent Unit to Secure Smart Meters. Applied Sciences 2020, 10, 3151 .
AMA StyleHira Gul, Nadeem Javaid, Ibrar Ullah, Ali Qamar, Muhammad Khalil Afzal, Gyanendra Prasad Joshi. Detection of Non-Technical Losses Using SOSTLink and Bidirectional Gated Recurrent Unit to Secure Smart Meters. Applied Sciences. 2020; 10 (9):3151.
Chicago/Turabian StyleHira Gul; Nadeem Javaid; Ibrar Ullah; Ali Qamar; Muhammad Khalil Afzal; Gyanendra Prasad Joshi. 2020. "Detection of Non-Technical Losses Using SOSTLink and Bidirectional Gated Recurrent Unit to Secure Smart Meters." Applied Sciences 10, no. 9: 3151.
With the increase in local energy generation from Renewable Energy Sources (RESs), the concept of decentralized peer-to-peer Local Energy Market (LEM) is becoming popular. In this paper, a blockchain-based LEM is investigated, where consumers and prosumers in a small community trade energy without the need for a third party. In the proposed model, a Home Energy Management (HEM) system and demurrage mechanism are introduced, which allow both the prosumers and consumers to optimize their energy consumption and to minimize electricity costs. This method also allows end-users to shift their load to off-peak hours and to use cheap energy from the LEM. The proposed solution shows how energy consumption and electricity cost are optimized using HEM and demurrage mechanism. It also provides economic benefits at both the community and end-user levels and provides sufficient energy to the LEM. The simulation results show that electricity cost is reduced up to 44.73% and 28.55% when the scheduling algorithm is applied using the Critical Peak Price (CPP) and Real-Time Price (RTP) schemes, respectively. Similarly, 65.15% and 35.09% of costs are reduced when CPP and RTP are applied with demurrage mechanism. Moreover, 51.80% and 44.37% electricity costs reduction is observed when CPP and RTP are used with both demurrage and scheduling algorithm. We also carried out security vulnerability analysis to ensure that our energy trading smart contract is secure and bug-free against the common vulnerabilities and attacks.
Adamu Sani Yahaya; Nadeem Javaid; Fahad A. Alzahrani; Amjad Rehman; Ibrar Ullah; Affaf Shahid; Muhammad Shafiq. Blockchain Based Sustainable Local Energy Trading Considering Home Energy Management and Demurrage Mechanism. Sustainability 2020, 12, 3385 .
AMA StyleAdamu Sani Yahaya, Nadeem Javaid, Fahad A. Alzahrani, Amjad Rehman, Ibrar Ullah, Affaf Shahid, Muhammad Shafiq. Blockchain Based Sustainable Local Energy Trading Considering Home Energy Management and Demurrage Mechanism. Sustainability. 2020; 12 (8):3385.
Chicago/Turabian StyleAdamu Sani Yahaya; Nadeem Javaid; Fahad A. Alzahrani; Amjad Rehman; Ibrar Ullah; Affaf Shahid; Muhammad Shafiq. 2020. "Blockchain Based Sustainable Local Energy Trading Considering Home Energy Management and Demurrage Mechanism." Sustainability 12, no. 8: 3385.
Due to the rapid increase in human population, the use of energy in daily life is increasing day by day. One solution is to increase the power generation in the same ratio as the human population increase. However, that is usually not possible practically. Thus, in order to use the existing resources of energy efficiently, smart grids play a significant role. They minimize electricity consumption and their resultant cost through demand side management (DSM). Universities and similar organizations consume a significant portion of the total generated energy; therefore, in this work, using DSM, we scheduled different appliances of a university campus to reduce the consumed energy cost and the probable peak to average power ratio. We have proposed two nature-inspired algorithms, namely, the multi-verse optimization (MVO) algorithm and the sine-cosine algorithm (SCA), to solve the energy optimization problem. The proposed schemes are implemented on a university campus load, which is divided into two portions, morning session and evening session. Both sessions contain different shiftable and non-shiftable appliances. After scheduling of shiftable appliances using both MVO and SCA techniques, the simulations showed very useful results in terms of energy cost and peak to average ratio reduction, maintaining the desired threshold level between electricity cost and user waiting time.
Ibrar Ullah; Irshad Hussain; Peerapong Uthansakul; M. Riaz; M. Naeem Khan; Jaime Lloret. Exploiting Multi-Verse Optimization and Sine-Cosine Algorithms for Energy Management in Smart Cities. Applied Sciences 2020, 10, 2095 .
AMA StyleIbrar Ullah, Irshad Hussain, Peerapong Uthansakul, M. Riaz, M. Naeem Khan, Jaime Lloret. Exploiting Multi-Verse Optimization and Sine-Cosine Algorithms for Energy Management in Smart Cities. Applied Sciences. 2020; 10 (6):2095.
Chicago/Turabian StyleIbrar Ullah; Irshad Hussain; Peerapong Uthansakul; M. Riaz; M. Naeem Khan; Jaime Lloret. 2020. "Exploiting Multi-Verse Optimization and Sine-Cosine Algorithms for Energy Management in Smart Cities." Applied Sciences 10, no. 6: 2095.
Due to the exponential increase in the human population of this bio-sphere, energy resources are becoming scarce. Because of the traditional methods, most of the generated energy is wasted every year in the distribution network and demand side. Therefore, researchers all over the world have taken a keen interest in this issue and finally introduced the concept of the smart grid. Smart grid is an ultimate solution to all of the energy related problems of today’s modern world. In this paper, we have proposed a meta-heuristic optimization technique called the dragonfly algorithm (DA). The proposed algorithm is to a real-world problem of single and multiple smart homes. In our system model, two classes of appliances are considered; Shiftable appliances and Non-shiftable appliances. Shiftable appliances play a significant role in demand side load management because they can be scheduled according to real time pricing (RTP) signal from utility, while non-shiftable appliances are not much important in load management, as these appliances are fixed and cannot be scheduled according to RTP. On behalf of our simulation results, it can be concluded that our proposed algorithm DA has achieved minimum electricity cost with a tolerable waiting time. There is a trade-off between electricity cost and waiting time because, with a decrease in electricity cost, waiting time increases and vice versa. This trade-off is also obtained by our proposed algorithm DA. The stability of the grid is also maintained by our proposed algorithm DA because stability of the grid depends on peak-to-average ratio (PAR), while PAR is reduced by DA in comparison with an unscheduled case.
Irshad Hussain; Majid Ullah; Ibrar Ullah; Asima Bibi; Muhammad Naeem; Madhusudan Singh; Dhananjay Singh. Optimizing Energy Consumption in the Home Energy Management System via a Bio-Inspired Dragonfly Algorithm and the Genetic Algorithm. Electronics 2020, 9, 406 .
AMA StyleIrshad Hussain, Majid Ullah, Ibrar Ullah, Asima Bibi, Muhammad Naeem, Madhusudan Singh, Dhananjay Singh. Optimizing Energy Consumption in the Home Energy Management System via a Bio-Inspired Dragonfly Algorithm and the Genetic Algorithm. Electronics. 2020; 9 (3):406.
Chicago/Turabian StyleIrshad Hussain; Majid Ullah; Ibrar Ullah; Asima Bibi; Muhammad Naeem; Madhusudan Singh; Dhananjay Singh. 2020. "Optimizing Energy Consumption in the Home Energy Management System via a Bio-Inspired Dragonfly Algorithm and the Genetic Algorithm." Electronics 9, no. 3: 406.
The International Energy Agency has projected that the total energy demand for electricity in sub-Saharan Africa (SSA) is expected to rise by an average of 4% per year up to 2040. It implies that ~620 million people are living without electricity in SSA. Going with the 2030 vision of the United Nations that electricity should be accessible to all, it is important that new technology and methods are provided. In comparison to other nations worldwide, smart grid (SG) is an emerging technology in SSA. SG is an information technology-enhanced power grid, which provides a two-way communication network between energy producers and customers. Also, it includes renewable energy, smart meters, and smart devices that help to manage energy demands and reduce energy generation costs. However, SG is facing inherent difficulties, such as energy theft, lack of trust, security, and privacy issues. Therefore, this paper proposes a blockchain-based decentralized energy system (BDES) to accelerate rural and urban electrification by improving service delivery while minimizing the cost of generation and addressing historical antipathy and cybersecurity risk within SSA. Additionally, energy insufficiency and fixed pricing schemes may raise concerns in SG, such as the imbalance of order. The paper also introduces a blockchain-based energy trading system, which includes price negotiation and incentive mechanisms to address the imbalance of order. Moreover, existing models for energy planning do not consider the effect of fill rate (FR) and service level (SL). A blockchain levelized cost of energy (BLCOE) is proposed as the least-cost solution that measures the impact of energy reliability on generation cost using FR and SL. Simulation results are presented to show the performance of the proposed model and the least-cost option varies with relative energy generation cost of centralized, decentralized and BDES infrastructure. Case studies of Burkina Faso, Cote d’Ivoire, Gambia, Liberia, Mali, and Senegal illustrate situations that are more suitable for BDES. For other SSA countries, BDES can cost-effectively service a large population and regions. Additionally, BLCOE reduces energy costs by approximately 95% for battery and 75% for the solar modules. The future BLCOE varies across SSA on an average of about 0.049 $/kWh as compared to 0.15 $/kWh of an existing system in the literature.
Omaji Samuel; Ahmad Almogren; Atia Javaid; Mansour Zuair; Ibrar Ullah; Nadeem Javaid. Leveraging Blockchain Technology for Secure Energy Trading and Least-Cost Evaluation of Decentralized Contributions to Electrification in Sub-Saharan Africa. Entropy 2020, 22, 226 .
AMA StyleOmaji Samuel, Ahmad Almogren, Atia Javaid, Mansour Zuair, Ibrar Ullah, Nadeem Javaid. Leveraging Blockchain Technology for Secure Energy Trading and Least-Cost Evaluation of Decentralized Contributions to Electrification in Sub-Saharan Africa. Entropy. 2020; 22 (2):226.
Chicago/Turabian StyleOmaji Samuel; Ahmad Almogren; Atia Javaid; Mansour Zuair; Ibrar Ullah; Nadeem Javaid. 2020. "Leveraging Blockchain Technology for Secure Energy Trading and Least-Cost Evaluation of Decentralized Contributions to Electrification in Sub-Saharan Africa." Entropy 22, no. 2: 226.
In this paper, a blockchain-based data sharing and access control system is proposed, for communication between the Internet of Things (IoT) devices. The proposed system is intended to overcome the issues related to trust and authentication for access control in IoT networks. Moreover, the objectives of the system are to achieve trustfulness, authorization, and authentication for data sharing in IoT networks. Multiple smart contracts such as Access Control Contract (ACC), Register Contract (RC), and Judge Contract (JC) are used to provide efficient access control management. Where ACC manages overall access control of the system, and RC is used to authenticate users in the system, JC implements the behavior judging method for detecting misbehavior of a subject (i.e., user). After the misbehavior detection, a penalty is defined for that subject. Several permission levels are set for IoT devices’ users to share services with others. In the end, performance of the proposed system is analyzed by calculating cost consumption rate of smart contracts and their functions. A comparison is made between existing and proposed systems. Results show that the proposed system is efficient in terms of cost. The overall execution cost of the system is 6,900,000 gas units and the transaction cost is 5,200,000 gas units.
Tanzeela Sultana; Ahmad Almogren; Mariam Akbar; Mansour Zuair; Ibrar Ullah; Nadeem Javaid. Data Sharing System Integrating Access Control Mechanism using Blockchain-Based Smart Contracts for IoT Devices. Applied Sciences 2020, 10, 488 .
AMA StyleTanzeela Sultana, Ahmad Almogren, Mariam Akbar, Mansour Zuair, Ibrar Ullah, Nadeem Javaid. Data Sharing System Integrating Access Control Mechanism using Blockchain-Based Smart Contracts for IoT Devices. Applied Sciences. 2020; 10 (2):488.
Chicago/Turabian StyleTanzeela Sultana; Ahmad Almogren; Mariam Akbar; Mansour Zuair; Ibrar Ullah; Nadeem Javaid. 2020. "Data Sharing System Integrating Access Control Mechanism using Blockchain-Based Smart Contracts for IoT Devices." Applied Sciences 10, no. 2: 488.
Industries are consuming more than 27% of the total generated energy in the world, out of which 50% is used by different machines for processing, producing, and assembling various goods. Energy shortage is a major issue of this biosphere. To overcome energy scarcity, a challenging task is to have optimal use of existing energy resources. An efficient and effective mechanism is essential to optimally schedule the load units to achieve three objectives: minimization of the consumed energy cost, peak-to-average power ratio, and consumer waiting time due to scheduling of the load. To achieve the aforementioned objectives, two bio-inspired heuristic techniques—Grasshopper-Optimization Algorithm and Cuckoo Search Optimization Algorithm—are analyzed and simulated for efficient energy use in an industry. We considered a woolen mill as a case study, and applied our algorithms on its different load units according to their routine functionality. Then we scheduled these load units by proposing an efficient energy management system (EMS). We assumed automatic operating machines and day-ahead pricing schemes in our EMS.
Ibrar Ullah; Irshad Hussain; Madhusudan Singh. Exploiting Grasshopper and Cuckoo Search Bio-Inspired Optimization Algorithms for Industrial Energy Management System: Smart Industries. Electronics 2020, 9, 105 .
AMA StyleIbrar Ullah, Irshad Hussain, Madhusudan Singh. Exploiting Grasshopper and Cuckoo Search Bio-Inspired Optimization Algorithms for Industrial Energy Management System: Smart Industries. Electronics. 2020; 9 (1):105.
Chicago/Turabian StyleIbrar Ullah; Irshad Hussain; Madhusudan Singh. 2020. "Exploiting Grasshopper and Cuckoo Search Bio-Inspired Optimization Algorithms for Industrial Energy Management System: Smart Industries." Electronics 9, no. 1: 105.
Energy is one of the valuable resources in this biosphere. However, with the rapid increase of the population and increasing dependency on the daily use of energy due to smart technologies and the Internet of Things (IoT), the existing resources are becoming scarce. Therefore, to have an optimum usage of the existing energy resources on the consumer side, new techniques and algorithms are being discovered and used in the energy optimization process in the smart grid (SG). In SG, because of the possibility of bi-directional power flow and communication between the utility and consumers, an active and optimized energy scheduling technique is essential, which minimizes the end-user electricity bill, reduces the peak-to-average power ratio (PAR) and reduces the frequency of interruptions. Because of the varying nature of the power consumption patterns of consumers, optimized scheduling of energy consumption is a challenging task. For the maximum benefit of both the utility and consumers, to decide whether to store, buy or sale extra energy, such active environmental features must also be taken into consideration. This paper presents two bio-inspired energy optimization techniques; the grasshopper optimization algorithm (GOA) and bacterial foraging algorithm (BFA), for power scheduling in a single office. It is clear from the simulation results that the consumer electricity bill can be reduced by more than 34.69% and 37.47%, while PAR has a reduction of 56.20% and 20.87% with GOA and BFA scheduling, respectively, as compared to unscheduled energy consumption with the day-ahead pricing (DAP) scheme.
Ibrar Ullah; Zar Khitab; Muhammad Naeem Khan; Sajjad Hussain. An Efficient Energy Management in Office Using Bio-Inspired Energy Optimization Algorithms. Processes 2019, 7, 142 .
AMA StyleIbrar Ullah, Zar Khitab, Muhammad Naeem Khan, Sajjad Hussain. An Efficient Energy Management in Office Using Bio-Inspired Energy Optimization Algorithms. Processes. 2019; 7 (3):142.
Chicago/Turabian StyleIbrar Ullah; Zar Khitab; Muhammad Naeem Khan; Sajjad Hussain. 2019. "An Efficient Energy Management in Office Using Bio-Inspired Energy Optimization Algorithms." Processes 7, no. 3: 142.
This paper proposes two bio-inspired heuristic algorithms, the Moth-Flame Optimization (MFO) algorithm and Genetic Algorithm (GA), for an Energy Management System (EMS) in smart homes and buildings. Their performance in terms of energy cost reduction, minimization of the Peak to Average power Ratio (PAR) and end-user discomfort minimization are analysed and discussed. Then, a hybrid version of GA and MFO, named TG-MFO (Time-constrained Genetic-Moth Flame Optimization), is proposed for achieving the aforementioned objectives. TG-MFO not only hybridizes GA and MFO, but also incorporates time constraints for each appliance to achieve maximum end-user comfort. Different algorithms have been proposed in the literature for energy optimization. However, they have increased end-user frustration in terms of increased waiting time for home appliances to be switched ON. The proposed TG-MFO algorithm is specially designed for nearly-zero end-user discomfort due to scheduling of appliances, keeping in view the timespan of individual appliances. Renewable energy sources and battery storage units are also integrated for achieving maximum end-user benefits. For comparison, five bio-inspired heuristic algorithms, i.e., Genetic Algorithm (GA), Ant Colony Optimization (ACO), Cuckoo Search Algorithm (CSA), Firefly Algorithm (FA) and Moth-Flame Optimization (MFO), are used to achieve the aforementioned objectives in the residential sector in comparison with TG-MFO. The simulations through MATLAB show that our proposed algorithm has reduced the energy cost up to 32.25% for a single user and 49.96% for thirty users in a residential sector compared to unscheduled load.
Ibrar Ullah; Sajjad Hussain. Time-Constrained Nature-Inspired Optimization Algorithms for an Efficient Energy Management System in Smart Homes and Buildings. Applied Sciences 2019, 9, 792 .
AMA StyleIbrar Ullah, Sajjad Hussain. Time-Constrained Nature-Inspired Optimization Algorithms for an Efficient Energy Management System in Smart Homes and Buildings. Applied Sciences. 2019; 9 (4):792.
Chicago/Turabian StyleIbrar Ullah; Sajjad Hussain. 2019. "Time-Constrained Nature-Inspired Optimization Algorithms for an Efficient Energy Management System in Smart Homes and Buildings." Applied Sciences 9, no. 4: 792.
In a smart grid, several optimization techniques have been developed to schedule load in the residential area. Most of these techniques aim at minimizing the energy consumption cost and the comfort of electricity consumer. Conversely, maintaining a balance between two conflicting objectives: energy consumption cost and user comfort is still a challenging task. Therefore, in this paper, we aim to minimize the electricity cost and user discomfort while taking into account the peak energy consumption. In this regard, we implement and analyse the performance of a traditional dynamic programming (DP) technique and two heuristic optimization techniques: genetic algorithm (GA) and binary particle swarm optimization (BPSO) for residential load management. Based on these techniques, we propose a hybrid scheme named GAPSO for residential load scheduling, so as to optimize the desired objective function. In order to alleviate the complexity of the problem, the multi dimensional knapsack is used to ensure that the load of electricity consumer will not escalate during peak hours. The proposed model is evaluated based on two pricing schemes: day-ahead and critical peak pricing for single and multiple days. Furthermore, feasible regions are calculated and analysed to develop a relationship between power consumption, electricity cost, and user discomfort. The simulation results are compared with GA, BPSO and DP, and validate that the proposed hybrid scheme reflects substantial savings in electricity bills with minimum user discomfort. Moreover, results also show a phenomenal reduction in peak power consumption.
Nadeem Javaid; Fahim Ahmed; Ibrar Ullah; Samia Abid; Wadood Abdul; Atif Alamri; Ahmad S. Almogren. Towards Cost and Comfort Based Hybrid Optimization for Residential Load Scheduling in a Smart Grid. Energies 2017, 10, 1546 .
AMA StyleNadeem Javaid, Fahim Ahmed, Ibrar Ullah, Samia Abid, Wadood Abdul, Atif Alamri, Ahmad S. Almogren. Towards Cost and Comfort Based Hybrid Optimization for Residential Load Scheduling in a Smart Grid. Energies. 2017; 10 (10):1546.
Chicago/Turabian StyleNadeem Javaid; Fahim Ahmed; Ibrar Ullah; Samia Abid; Wadood Abdul; Atif Alamri; Ahmad S. Almogren. 2017. "Towards Cost and Comfort Based Hybrid Optimization for Residential Load Scheduling in a Smart Grid." Energies 10, no. 10: 1546.
Smart grid is an emerging technology which is considered to be an ultimate solution to meet the increasing power demand challenges. Modern communication technologies have enabled the successful implementation of smart grid (SG), which aims at provision of demand side management mechanisms (DSM), such as demand response (DR). In this paper, we propose a hybrid technique named as teacher learning genetic optimization (TLGO) by combining genetic algorithm (GA) with teacher learning based optimization (TLBO) algorithm for residential load scheduling, assuming that electric prices are announced on a day-ahead basis. User discomfort is one of the key aspects which must be addressed along with cost minimization. The major focus of this work is to minimize consumer electricity bill at minimum user discomfort. Load scheduling is formulated as an optimization problem and an optimal schedule is achieved by solving the minimization problem. We also investigated the effect of power-flexible appliances on consumers’ bill. Furthermore, a relationship among power consumption, cost and user discomfort is also demonstrated by feasible region. Simulation results validate that our proposed technique performs better in terms of cost reduction and user discomfort minimization, and is able to obtain the desired trade-off between consumer electricity bill and user discomfort.
Awais Manzoor; Nadeem Javaid; Ibrar Ullah; Wadood Abdul; Ahmad Almogren; Atif Alamri. An Intelligent Hybrid Heuristic Scheme for Smart Metering based Demand Side Management in Smart Homes. Energies 2017, 10, 1258 .
AMA StyleAwais Manzoor, Nadeem Javaid, Ibrar Ullah, Wadood Abdul, Ahmad Almogren, Atif Alamri. An Intelligent Hybrid Heuristic Scheme for Smart Metering based Demand Side Management in Smart Homes. Energies. 2017; 10 (9):1258.
Chicago/Turabian StyleAwais Manzoor; Nadeem Javaid; Ibrar Ullah; Wadood Abdul; Ahmad Almogren; Atif Alamri. 2017. "An Intelligent Hybrid Heuristic Scheme for Smart Metering based Demand Side Management in Smart Homes." Energies 10, no. 9: 1258.
Today’s buildings are responsible for about 40% of total energy consumption and 30–40% of carbon emissions, which are key concerns for the sustainable development of any society. The excessive usage of grid energy raises sustainability issues in the face of global changes, such as climate change, population, economic growths, etc. Traditionally, the power systems that deliver this commodity are fuel operated and lead towards high carbon emissions and global warming. To overcome these issues, the recent concept of the nearly zero energy building (nZEB) has attracted numerous researchers and industry for the construction and management of the new generation buildings. In this regard, this paper proposes various demand side management (DSM) programs using the genetic algorithm (GA), teaching learning-based optimization (TLBO), the enhanced differential evolution (EDE) algorithm and the proposed enhanced differential teaching learning algorithm (EDTLA) to manage energy and comfort, while taking the human preferences into consideration. Power consumption patterns of shiftable home appliances are modified in response to the real-time price signal in order to get monetary benefits. To further improve the cost and user discomfort objectives along with reduced carbon emission, renewable energy sources (RESs) are also integrated into the microgrid (MG). The proposed model is implemented in a smart residential complex of multiple homes under a real-time pricing environment. We figure out two feasible regions: one for electricity cost and the other for user discomfort. The proposed model aims to deal with the stochastic nature of RESs while introducing the battery storage system (BSS). The main objectives of this paper include: (1) integration of RESs; (2) minimization of the electricity bill (cost) and discomfort; and (3) minimizing the peak to average ratio (PAR) and carbon emission. Additionally, we also analyze the tradeoff between two conflicting objectives, like electricity cost and user discomfort. Simulation results validate both the implemented and proposed techniques.
Nadeem Javaid; Sardar Mehboob Hussain; Ibrar Ullah; Muhammad Noor; Wadood Abdul; Ahmad Almogren; Atif Alamri. Demand Side Management in Nearly Zero Energy Buildings Using Heuristic Optimizations. Energies 2017, 10, 1131 .
AMA StyleNadeem Javaid, Sardar Mehboob Hussain, Ibrar Ullah, Muhammad Noor, Wadood Abdul, Ahmad Almogren, Atif Alamri. Demand Side Management in Nearly Zero Energy Buildings Using Heuristic Optimizations. Energies. 2017; 10 (8):1131.
Chicago/Turabian StyleNadeem Javaid; Sardar Mehboob Hussain; Ibrar Ullah; Muhammad Noor; Wadood Abdul; Ahmad Almogren; Atif Alamri. 2017. "Demand Side Management in Nearly Zero Energy Buildings Using Heuristic Optimizations." Energies 10, no. 8: 1131.