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Towards Energy Efficiency in Smart Buildings Exploiting Dynamic Coordination among Appliances and Homes- Final Defense Presentation

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Towards Energy Efficiency in Smart Buildings Exploiting Dynamic Coordination among Appliances and Homes- Final Defense Presentation

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In residential energy management (REM), Time of Use (ToU) of devices scheduling based on user-defined preferences is an essential task performed by the home energy management controller. This paper devised a robust REM technique capable of monitoring and controlling residential loads within a smart home. In this paper, a new distributed multi-agent framework based on the cloud layer computing architecture is developed for real-time microgrid economic dispatch and monitoring. In this paper the grey wolf optimizer (GWO), artificial bee colony (ABC) optimization algorithm-based Time of Use (ToU) pricing model is proposed to define the rates for shoulder-peak and on-peak hours. The results illustrate the effectiveness of the proposed the grey wolf optimizer (GWO), artificial bee colony (ABC) optimization algorithm based ToU pricing scheme. A Raspberry Pi3 based model of a well-known test grid topology is modified to support real-time communication with open-source IoE platform Node-Red used for cloud computing. Two levels communication system connects microgrid system, implemented in Raspberry Pi3, to cloud server. The local communication level utilizes IP/TCP and MQTT is used as a protocol for global communication level. The results demonstrate and validate the effectiveness of the proposed technique, as well as the capability to track the changes of load with the interactions in real-time and the fast convergence rate.
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An increase in the world's population results in high energy demand, which is mostly fulfilled by consuming fossil fuels (FFs). By nature, FFs are scarce, depleted, and non-eco-friendly. Renewable energy sources (RESs) photovoltaics (PVs) and wind turbines (WTs) are emerging alternatives to the FFs. The integration of an energy storage system with these sources provides promising and economical results to satisfy the user's load in a stand-alone environment. Due to the intermittent nature of RESs, their optimal sizing is a vital challenge when considering cost and reliability parameters. In this paper, three meta-heuristic algorithms: teaching-learning based optimization (TLBO), enhanced differential evolution (EDE), and the salp swarm algorithm (SSA), along with two hybrid schemes (TLBO + EDE and TLBO + SSA) called enhanced evolutionary sizing algorithms (EESAs) are proposed for solving the unit sizing problem of hybrid RESs in a stand-alone environment. The objective of this work is to minimize the user's total annual cost (TAC). The reliability is considered via the maximum allowable loss of power supply probability (LPSP max) concept. The simulation results reveal that EESAs provide better results in terms of TAC minimization as compared to other algorithms at four LPSP max values of 0%, 0.5%, 1%, and 3%, respectively, for a PV-WT-battery hybrid system. Further, the PV-WT-battery hybrid system is found as the most economical scenario when it is compared to PV-battery and WT-battery systems.
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The communication between the Internet of Things (IoT) devices is not secure and reliable. A large number of security risks are involved. The existing security mechanisms are not easy to manage because they require extra resources and thus, increases the overall cost of the system. The IoT devices are resource-limited devices and they do not perform many computations. The services requested from the IoT devices maybe malicious and have severe consequences when they are not being tackled by any efficient security mechanism. The reliability of the services is an important aspect of the IoT network. The blockchain in this sense provides a solution that is secure and effective in terms of reliability and cost. We proposed a system model through which we protect the IoT devices from unreliable services. The services are provided by cloud service providers.The proposed system allows IoT devices to know about the ratings of the service providers before requesting the service.Smart contracts are introduced to store ratings of the service providers into the blockchain. IoT devices invoke the smart contract to acquire ratings of the service providers by providing service codes. Performance analysis and the experimental results show that the proposed model protects the IoT devices from unreliable services in a reasonable time and affordable cost.
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The increasing load demand in residential area and irregular electricity load profile encouraged us to propose an efficient Home Energy Management System (HEMS) for optimal scheduling of home appliances. We propose a multi-objective optimization based solution that shifts the electricity load from On-peak to Off-peak hours according to the defined objective load curve for electricity. It aims to manage the trade-off between conflicting objectives: electricity bill, waiting time of appliances and electricity load shifting according to the defined electricity load pattern. The defined electricity load pattern helps in balancing the load during On-peak and Off-peak hours. Moreover, for real time rescheduling, concept of coordination among home appliances is presented. This helps the scheduler to optimally decide the ON/OFF status of appliances to reduce the waiting time of the appliance. Whereas, electricity consumers have stochastic nature, for which, nature-inspired optimization techniques provide optimal solution. For optimal scheduling, we proposed two optimization techniques: binary multi-objective bird swarm optimization and a hybrid of bird swarm and cuckoo search algorithms to obtain the Pareto front. Moreover, dynamic programming is used to enable coordination among the appliances so that real-time scheduling can be performed by the scheduler on user's demand. To validate the performance of the proposed nature-based optimization techniques, we compare the results of proposed schemes with existing techniques such as multi-objective binary particle swarm optimization and multi-objective cuckoo search algorithms. Simulation results validate the performance of proposed techniques in terms of electricity cost reduction, peak to average ratio and waiting time minimization. Also, test functions for convex, non-convex and discontinuous Pareto front are implemented to prove the efficacy of proposed techniques.
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Nowadays, constrained battery life expectancy is an important issue for reliable data delivery in an Underwater Wireless Sensor Network (UWSN). Conventional transmission methodologies increase the transmission overhead, i.e., the collision of packets, which influence the data transmission. Replacement of the sensors' battery in brutal underwater environment is a difficult task. Therefore, to maintain a strategic distance from the unexpected failure of the network and to increase the life expectancy of the network, energy efficient routing protocols are required. At this end, in this paper, a proactive routing protocol with three different network types is proposed to solve the aforementioned issues. The proposed protocol adaptively changes its communication strategy depending on the type of the network, i.e., dense network, partially dense network and sparse network. This adaptive strategy helps the routing protocols to continue their transmission by avoiding the void holes. In the proposed protocol named Proactive routing Approach with energy efficient Path Selection (PA-EPS-Case I), vertical inter-transmission layering concept is introduced (using shortest and fastest path) in the dense and partially dense region. In addition, cluster formation concept is also appended to make transmission successful in the sparse regions. The Packet Delivery Ratio (PDR) is improved by the proposed protocol with minimum End to End (E2E) delay and packet drop ratio. Scalability of the proposed routing protocols is also analyzed by varying the number of nodes from 100-500. A comparative analysis is performed with two cutting edge routing protocols namely: Weighting Depth and Forwarding Area Division Depth Based Routing (WDFAD-DBR) and Cluster-based WDFAD-DBR (C-DBR). Simulation results demonstrate that proposed protocol achieved 12.64% higher PDR with 20% decrease in E2E delay than C-DBR-DBR. Furthermore, the proposed routing protocol outperformed C-DBR in terms of packet drop ratio up to 14.29% with an increase of EC up to 30%. INDEX TERMS Underwater wireless sensor networks, adaptive transmission, void hole, geographic and opportunistic routing, mobility prediction.
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For the better management of energy, a coordination based energy management system as-a-service on fog is presented. An efficient system model is introduced to handle a social networking problem in order to maintain the balance between the produced and the required energy. This social network problem is formulated as a game theory based coalition method. With the increase in number of electricity consumers, the computational complexity of energy management system is becoming a threat for system efficiency in real-time environment. To deal with this dilemma, the service providers shift their computational and storage units on cloud and fog. The fog is an intermittent layer between the cloud and the end user which helps to make the system faster as compared to the cloud. In this scenario, a building with multiple apartments is considered. Where, each apartment is taken as a player and the surplus power as pay-off. The surplus energy will be distributed among the energy deficient apartments using Shapley value that unevenly distributes the power according to the demand. The experimental results show that 13 kW extra power is saved and distributed among energy deficient apartments during different times of a day.