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.

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Since the last few decades, a class imbalance has been one of the most challenging problems in various fields, such as data mining and machine learning. The particular state of an imbalanced dataset, where each class associated with a given dataset is distributed unevenly. This happens when the positive class is much smaller than the negative class. In this case, most standard classification algorithms do not identify examples related to the positive class. A positive class usually refers to the key interest of the classification task. In order to solve this problem, several solutions were proposed such as sampling-based over-sampling and under-sampling, changes at the classifier level or the combination of two or more classifiers. However the main problem is that most solutions are biased towards negative class, computationally expensive, have storage issues or taking long training time. An alternative approach to this problem is the genetic algorithm (GA), which has shown the promising results. The GA is an evolutionary learning algorithm that uses the principles of Darwinian evolution, it is a powerful global search algorithm. Moreover, the fitness function is a key parameter in GA. It determines how well a solution can solve the given problem. In this paper, we propose a solution which uses entropy and information gain as a fitness function in GA with an objective to improve the impurity and gives a more balanced result without changing the original dataset. The experiments conducted on different datasets demonstrate the effectiveness of the proposed solution in comparison with the several other state-of-the-art algorithms in term of Accuracy (Acc), geometric mean (GM), F-measure (FM), kappa, and Matthews correlation coefficient (MCC).

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A smart grid (SG) is an emerging technology that provides electricity in a cost-efficient and eco-friendly way. SG combined with distributed energy resources (DERs) plays a crucial role in extending the existing grid's capacity while mitigating carbon emissions. The potential sources of DERs include solar, wind, and tidal energy. Usually, these DERs are located far away from the grid and not necessarily tied to the grid system. However, the energy trading capabilities of a grid-tied DERs are getting attention, both from academia and industry. This bonding of grid-tied DERs helps to decrease the loss of surplus energy, build an energy storage capacity, and other operational charges. Energy-consuming flexible home tasks can be optimized coordinately with the operations of DERs to minimize the economic cost and CO 2 emissions. In this work, our problem is multi-objective and we aim to reduce both electricity price and CO 2 emission. We proposed a multi-objective self-adaptive multi-population based Jaya algorithm (multi-objective SAMP-Jaya) to schedule the operations of flexible home tasks. Different pricing schemes have been applied to uncover the correlation between CO 2 emission, economic cost, and pricing schemes. We assume a smart building, including 30 smart homes with PV and energy storage system (ESS) as DERs. Promising results have shown the effectiveness of our proposed scheme.

Summary Fog computing has revolutionized the computing domain by enabling resource sharing, such as online storage, and providing applications and software as services in near vicinity to the edge nodes through the Internet. Small‐ to large‐sized companies, like Amazon, Google, Facebook, Twitter, and LinkedIn, have started switching to fog‐computing–enabled infrastructures. Fog computing being distributed in nature and in near vicinity gives rises to security and privacy issues. Although mostly now a days, user identification is adopted via single sign‐in process, such as simple password‐based authentications, which is not a secure process. Several multi‐tier authentication techniques are proposed to overcome single sign‐in process limitations. In this article, we go through state‐of‐the‐art schemes proposed over the period of 2011‐2018 for multi‐tier authentication, their weaknesses and security issues, and finally their solutions for fog‐computing environment. We performed the comparison of available multi‐tier authentication techniques based on three factors, ie, level of security, cost of deployment, and usability. Multi‐tier authentication techniques are classified into categories in accordance with the aspects that are concerned with the authentication process. We are optimistic that this work will provide useful information to the researchers about the architectures of fog enabled systems and the underlying authentication models in a consolidated form.