With the incorporation of distributed energy systems in the electric grid,transactive energy market(TEM)has become popular in balancing the demand as well as supply adaptively over the *** classical grid can be update...
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With the incorporation of distributed energy systems in the electric grid,transactive energy market(TEM)has become popular in balancing the demand as well as supply adaptively over the *** classical grid can be updated to the smart grid by the integration of information and Communication Technology(ICT)over the *** TEM allows the Peerto-Peer(P2P)energy trading in the grid that effectually connects the consumer and prosumer to trade energy among *** the same time,there is a need to predict the load for effectual P2P energy trading and can be accomplished by the use of machine learning(DML)*** some of the short term load prediction techniques have existed in the literature,there is still essential to consider the intrinsic features,parameter optimization,*** *** this aspect,this study devises new deep learning enabled short term load forecasting model for P2P energy trading(DLSTLF-P2P)in *** proposed model involves the design of oppositional coyote optimization algorithm(OCOA)based feature selection technique in which the OCOA is derived by the integration of oppositional based learning(OBL)concept with COA for improved convergence ***,deep belief networks(DBN)are employed for the prediction of load in the P2P energy trading *** order to additional improve the predictive performance of the DBN model,a hyperparameter optimizer is introduced using chicken swarm optimization(CSO)algorithm is applied for the optimal choice of DBN parameters to improve the predictive *** simulation analysis of the proposed DLSTLF-P2P is validated using the UK Smart Meter dataset and the obtained outcomes demonstrate the superiority of the DLSTLF-P2P technique with the maximum training,testing,and validation accuracy of 90.17%,87.39%,and 87.86%.
Recently, semi-supervised learning methods are being actively developed to increase the performance of neural networks by using large amounts of unlabeled data. Among these techniques, pseudo-labeling methods have the...
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Using dispersed data and training, federated learning (FL) moves AI capabilities to edge devices or does tasks locally. Many consider FL the start of a new era in AI, yet it is still immature. FL has not garnered the ...
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Due to the increased usage of mobile devices, there are now diverse application needs that favor the use of Fog computing architecture over the Cloud's centralized architecture. Fog Computing arises in this circum...
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The Wireless Sensor Networks(WSN)is a self-organizing network with random deployment of wireless nodes that connects each other for effective monitoring and data *** clustering technique employed to group the collecti...
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The Wireless Sensor Networks(WSN)is a self-organizing network with random deployment of wireless nodes that connects each other for effective monitoring and data *** clustering technique employed to group the collection of nodes for data transmission and each node is assigned with a cluster *** major concern with the identification of the cluster head is the consideration of energy consumption and hence this paper proposes an hybrid model which forms an energy efficient cluster head in the Wireless Sensor *** proposed model is a hybridization of Glowworm Swarm Optimization(GSO)and Artificial Bee Colony(ABC)algorithm for the better identification of cluster *** performance of the proposed model is compared with the existing techniques and an energy analysis is performed and is proved to be more efficient than the existing model with normalized energy of 5.35%better value and reduction of time complexity upto 1.46%.Above all,the proposed model is 16%ahead of alive node count when compared with the existing methodologies.
In this paper, we propose a new entropy measure of Pythagorean fuzzy sets (PFSs). The proposed entropy measure of PFSs can conquer the shortcomings of the existing entropy measure of PFSs. We also propose the Pythagor...
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Current training pipelines in object recognition neglect Hue Jittering when doing data augmentation as it not only brings appearance changes that are detrimental to classification, but also the implementation is ineff...
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The research being presented here looks into the problems related to cyberbullying using data from Twitter and Wikipedia. To try and understand why people behave in this way online, the research included several chall...
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Recommendation systems are going to be an integral part of any E-Business in near *** in any other E-business,recommendation systems also play a key role in the travel business where the user has to be recommended wit...
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Recommendation systems are going to be an integral part of any E-Business in near *** in any other E-business,recommendation systems also play a key role in the travel business where the user has to be recommended with a restaurant that best suits *** general,the recommendations to a user are made based on similarity that exists between the intended user and the other *** similarity can be calculated either based on the similarity between the user profiles or the similarity between the ratings made by the *** phase of this work concentrates on experimentally analyzing both these models and get a deep insight of these *** the lessons learned from the insights,second phase of the work concentrates on developing a deep learning *** model does not depend on the other user's profile or rating made by *** model is tested with a small restaurant dataset and the model can predict whether a user likes the restaurant or *** model is trained with different users and their *** system learns from it and in order to predict whether a new user likes or not a restaurant that he/she has not visited earlier,all the data the trained model needed is the rating made by the same user for different *** model is deployed in a cloud environment in order to extend it to be more realistic product in *** evaluated with dataset,it achieves 74.6%is accurate prediction of results,where as existing techniques achieves only 64%.
In recent years, Perovskites have been a very interesting breakthrough in the field of photovoltaics because of their excellent performance with easy and less complex synthesizability, and cost-effectiveness. This ins...
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