Face recognition aims to establish the identity of a person based on facial characteristics and is a challenging problem due to complex nature of the facial manifold. A wide range of face recognition applications are based on classification techniques and a class label is assigned to the test image that belongs to the unknown class. In this paper, a pose invariant deeply learned multiview 3D face recognition approach is proposed and aims to address two problems: face alignment and face recognition through identification and verification setups. The proposed alignment algorithm is capable of handling frontal as well as profile face images. It employs a nose tip heuristic based pose learning approach to estimate acquisition pose of the face followed by coarse to fine nose tip alignment using L2 norm minimization. The whole face is then aligned through transformation using knowledge learned from nose tip alignment. Inspired by the intrinsic facial symmetry of the Left Half Face (LHF) and Right Half Face (RHF), Deeply learned (d) Multi-View Average Half Face (d-MVAHF) features are employed for face identification using deep convolutional neural network (dCNN). For face verification d-MVAHF-Support Vector Machine (d-MVAHF-SVM) approach is employed. The performance of the proposed methodology is demonstrated through extensive experiments performed on four databases: GavabDB, Bosphorus, UMB-DB, and FRGC v2.0. The results show that the proposed approach yields superior performance as compared to existing state-of-the-art methods.
Rising energy demand and the disproportionate utilization of fossil fuels not only result in power imbalance and economic drain but also raise environmental concerns. Under these challenging circumstances, microgrids provide a tactical solution by adopting distributed energy resources at user end. However, this solution is not effective without enough participation by these end users (prosumers) for sustainable energy growth in microgrids. This paper presents a behavioral control theory and various psychological motivational models to improve prosumers' participation up to the desired level. A framework for peers' management within a community is also presented. The coalition-based game theory is employed for fair and trustworthy inter-trading which lead to the formation of grand coalition by satisfying all the defined motivational models. Various trading systems i.e. feed in tariff system, peer-topeer trading with and without storage systems, and demand-side management-based peer-to-peer trading systems are used for energy inter-trading with minimum involvement with the grid. Finally, the proposed system is validated through simulations of various game theoretic-based peer-to-peer trading systems. Simulation results show a considerable reduction in average expenses for energy demand and carbon emissions with improved earnings for peers. INDEX TERMS peer-to-peer energy trading system, prosumers, demand-side management, game theory, motivational models I. INTRODUCTION Carbon emissions caused by disproportionate utilization of cost-inefficient fossil fuels have become a global concern. The world population is expected to rise by 50% in the next decade [1], resulting in a 25% upsurge in energy demand and a consequent huge gap between demand and supply. It seems difficult for the energy sector to meet such a high demand without the exploration of new generation techniques, the use of efficient plants, and adoption of bidirectional communication based power exchanges [1]. One of the possible solutions is to increase fossil fuel based-centralized generation; however, high capital cost, relocation problems, carbon emissions, socio-political pressure, energy security, and several other constraints make this choice less feasible. Currently, in the European Union (EU), buildings are responsible for 40% of carbon emissions, which can be reduced by using efficient home energy management system (HEMS) [2]. In HEMS, smart and energy-efficient appliances which have an impact on customer's preferences are used at the user end [3]. These energy efficiency improvements (EEI) will reduce energy demand and emissions. However, sometimes EEI has a motivational rebound effect, and raises energy demand [4]. Microgrids (MG) can also be used to provide a tactical solution by adopting distributed energy resources (DER) at user end [5]. The MG is a distributed grid having various distributed generators i.e. renewable energy resources (RERs), along with energy storage systems and interconnected loads to meet user demand. Traditional grids ar...
The establishment of grid-connected prosumer communities to bridge the demand-supply gap in developing nations, especially in rural areas will assist to minimize the use of carbon enriched fossil fuels and the resulting economic pressure. In the promoted study, an economic and ecosystem-friendly hybrid energy model is proposed for grid-connected prosumer community of 147 houses in district Kotli, AJK. The grid search algorithm-based HOMER software is used to simulate and analyze the load demand and biomass sources-based onsite collected data through a survey for an optimal proposed design. The research objectives are to minimize the net present cost (USD) of design, the per unit cost of energy (USD/kWh), and the carbon emissions (kgs/year). A sensitivity analysis based on photovoltaic module lifetime is also performed. The simulations show that the per unit cost of energy is reduced from 0.1 USD/kWh to 0.001 USD/kWh for the annual energy demand (kWh/year) of the community. The number of carbon emissions is also minimized from 122056 kgs/year to 1628 kgs/year through the proposed optimal energy model.
Rapid population growth, the ongoing fourth industrial revolution, rising energy demands, global environmental concerns, and uneconomic non-renewable energy drain intensify the need for prosumer communities to bridge the demand-supply gap in electrical energy. To this end, in this paper, we propose a priority-based energy sharing and management model, considering the prosumer community equipped with a renewable energy manager for power production and energy storage by limiting the dependency on available resources. In the proposed model, surplus power is shared within the community due to constraints of power line capacity for traditional grids. In addition, energy sharing is based on two distinct criteria: single-use and multiple-uses. The objective of the proposed model is to maximize the utilization of surplus power at a low per unit energy cost and obtain the optimum economic advantage. We employ a binary integer programming (BIP) technique to solve the formulated problem. The proficiency of the prosumer is the primary focus of the employed strategy. A weight factor is introduced to estimate the intra-dependency of the demand-supply gap and energy cost. Simulation results show that the demandsupply gap in the prosumer community reduces up to 41.8 % without energy storage system. Additionally, the results also validate that including the energy storage system in the prosumer community further reduces the demand-supply gap to 67.33 %.INDEX TERMS prosumer, energy management, renewable energy resources, Carbon emissions, demand side management, energy storage systems
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