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Towards Green World: Renewable Energy Source Based Energy Management in Residential Sector Making Appliances, Homes and Buildings Smart - without Source Code
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The transformation of conventional grid into Smart Grid (SG) requires strategic implementation of the demand-sensitive programs while considering the varying fluctuations in the consumers’ load. The core challenges faced by existing electric system are that how to utilize electrical devices, how to tackle large amount of data generated by end devices and how to meet energy demands of consumers in limited resources. This dissertation is focused on the energy management of residential sector in the SG. For this purpose, we have proposed the Energy Management Controllers (EMCs) at three levels: at home level (including the single and multiple homes), at building level and at regional level. In addition, cloud and fog based environments are integrated to provide on-demand services according to the consumers’ demands and are used to tackle the problems in existing electric system. At first level, heuristic algorithms based EMC is developed for the energy management of single and multiple homes in residential sector. Five heuristic algorithms: genetic algorithm, binary particle swarm optimization algorithm, bacterial foraging optimization algorithm, wind driven optimization algorithm and our proposed hybrid genetic wind driven algorithm are used to develop the EMC. These algorithms are used for scheduling of the residential load during peak and off peak hours in a real time pricing environment for minimizing both the electricity cost and peak to average ratio while maximizing the user comfort. In addition, the advancements in the electrical system, smart meters and implementation of Renewable Energy Sources (RESs) have yielded extensive changes to the current power grid for meeting the consumers’ demand. For integrating RESs and Energy Storage System (ESS) in existing EMCs, we have proposed another Home EMC (HEMC) that manages the residential sector’s load. The proposed HEMC is developed using the earliglow algorithm for electricity cost reduction. At second level, a fuzzy logic based approach is proposed and implemented for the hot and cold regions of the world using the world-wide adaptive thermostat for the residential buildings. Results show that the proposed approach achieves a maximum energy savings of 6.5% as compared to the earlier techniques. In addition, two EMCs: binary particle
swarm optimization fuzzy mamdani and binary particle swarm optimization fuzzy sugeno
are proposed for energy management of daily and seasonally used appliances. The comfort evaluation of these loads is also performed using the Fanger’s Predicted Mean Vote method. For increasing the system automation and on-demand availability of the resources, we have proposed a cloud-fog-based model for intelligent resource management in SG for multiple regions at next level. To implement this model, we have proposed a new hybrid approach of Ant Colony Optimization (ACO) and artificial bee colony known as Hybrid Artificial Bee ACO (HABACO). Moreover, a new Cloud to Fog to Consumer (C2F2C) based framework is also proposed for efficiently managing the resources in the residential buildings. C2F2C is a three layered framework having cloud, fog and consumer layers, which are used for the efficient resource management in six regions of the world. In order to efficiently manage the computation of the large amount of data of the residential consumers, we have also proposed and implemented the deep neuro-fuzzy optimizer. The simulation results of the proposed techniques show that they have outperformed the previous techniques in terms of energy consumption, user comfort, peak to average ratio and cost optimization in the residential sector.
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In this paper, two energy management controllers: Binary Particle Swarm Optimization Fuzzy Mamdani (BPSOF-MAM) and BPSOF Sugeno (BPSOFSUG) are proposed and implemented. Daily and seasonally used appliances are considered for the analysis of the efficient energy management through these controllers. Energy management is performed using the two Demand Side Management (DSM) strategies: load scheduling and load curtailment. In addition, these DSM strategies are evaluated using the meta-heuristic and artificially intelligent algorithms as BPSO and fuzzy logic. BPSO is used for scheduling of the daily used appliances, whereas fuzzy logic is applied for load curtailment of seasonally used appliances, i.e., Heating, Ventilation and Air Conditioning (HVAC) systems. Two fuzzy inference systems are applied in this work: fuzzy Mamdani and fuzzy Sugeno. This work is proposed for the energy management of the hottest areas of the world. The input parameters are: indoor temperature, outdoor temperature, occupancy, price, decision control variables, priority and length of operation times of the appliances, whereas the output parameters are: energy consumption, cost and thermal and appliance usage comfort. Moreover, the comfort level of the consumers regarding the usage of the appliances is computed using Fanger's predictive mean vote method. The comfort is further investigated by incorporating the renewable energy sources, i.e., photovoltaic systems. Simulation results show the effectiveness of the the proposed controllers as compared to the unscheduled case. BPSOFSUG outperforms to the BPSOFMAM in terms of energy consumption and cost of the proposed scenario.
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Nowadays, there exists strong need to enable the Intelligent Vehicle (IV) communication for applications such as safety messaging, traffic monitoring and many other internet access purposes. In this work, we have introduced an Intelligent Vehicle Trust Points (IVTPs) sharing mechanism between vehicle to vehicle, vehicle to infrastructure and vehicle to roadside units.Existing models have already embeded Blockchain (BC), which is valuable for many purposes like security in different data transmission circumstances. However, our proposed scheme uses this BC feature along with IVTPs to ensure the trust worthiness in the communication environment. Performance of our proposed system is evaluated on the basis of IVs’ processing time, which are totally based on IVTPs. Our proposed system is efficient as compared to existing one which handles less number of vehicles at intersection point where IVTPs are shared between moving vehicles in a scalable architecture.
Short term electricity load and price accurate fore-casting are the key areas that need to be addressed in Smart Grids (SG). In this paper, a new model is proposed for accurate short term forecasting of load and price. First, irrelevant features are removed using Recursive Feature Elimination (RFE) and Decision Tree Regressor (DTR). Then, further dimensionality reduction is done by removing redundancy using Singular Value Decomposition (SVD) for feature extraction. Finally, remaining features are given to Enhanced K-Nearest Neighbor (EKNN)for forecasting of both load and price. The proposed EKNN achieved better results than Support Vector Regressor (SVR)and normal K-Nearest Neighbors (KNN). EKNN achieved 12%more accuracy in terms of forecasting than normal KNN and63% more than SVR.
The feature of bidirectional communication in a smart grid involves the interaction between consumer and utility for optimizing the energy consumption of the users. For optimal management of the energy at the end user, several demand side management techniques are implemented. This work proposes a home energy management system, where consumption of household appliances is optimized using a hybrid technique. This technique is developed from cuckoo search algorithm and earthworm algorithm. However, there is a problem in such home energy management systems, that is, an uncertain behavior of the user that can lead to force start or stop of an appliance, deteriorating the purpose of scheduling of appliances. In order to solve this issue, coordination among appliances for rescheduling is incorporated in home energy management system using game theory. The appliances of the home are categorized in three different groups and their electricity cost is computed through the real-time pricing signals. Optimization schemes are implemented and their performance is scrutinized with and without coordination among the appliances. Simulation outcomes display that our proposed technique has minimized the total electricity cost by 50.6% as compared to unscheduled cost. Moreover, coordination among appliances has helped in increasing the user comfort by reducing the waiting time of appliances. The Shapley value has outperformed the Nash equilibrium and zero sum by achieving the maximum reduction in waiting time of appliances.
This book chapter proposes use of Ant Colony Optimization (ACO), a novel computational intelligence technique for balancing loads of virtual machine in cloud computing. Computational intelligence(CI), includes study of designing bio-inspired artificial agents for finding out probable optimal solution. So the central goal of CI can be said as, basic understanding of the principal, which helps to mimic intelligent behavior from the nature for artifact systems. Basic strands of ACO is to design an intelligent multi-agent systems imputed by the collective behavior of ants. From the perspective of operation research, it's a meta-heuristic. Cloud computing is a one of the emerging technology. It's enables applications to run on virtualized resources over the distributed environment. Despite these still some problems need to be take care, which includes load balancing. The proposed algorithm tries to balance loads and optimize the response time by distributing dynamic workload in to the entire system evenly.
