Peer-to-peer electricity transaction is predicted to play a substantial role in research into future power infrastructures as energy consumption in intelligent microgrids increases. However, the on-demand usage of Energy is a major issue for families to obtain the best cost. This article provides a machine learning predictive power trading framework for supporting distributed power resources in real-time, day-to-day monitoring, and generating schedules. Furthermore, the energy optimization algorithm used in machine learning (EOA-ML) is proposed in this article. The machine learning-based platform suggested two modules: fuel trading and intelligent contracts based on machine learning implemented predictive analytical components. The Blockchain module enables peers to track energy use in real-time, manage electricity trading, model rewards, and irreversible transaction records of electricity trading. A predictive analysis component based on previous power usage data is designed to anticipate short-term energy usage in the Intelligent Contracts. This study utilizes data from the provincial Jeju, Korea’s electricity department on true energy utilization. This study seeks to establish optimal electricity flow and crowdsourced, promoting electricity between consumers and prosumers. Power trading relies on day-to-day, practical environmental control and the planning of decentralized power capitals to satisfy the demands of smart grids. Furthermore, it employs data mining technologies to obtain and study time-series research from the past electricity utilization data. Thus, the time series analytics promotes power controllingto better future efficient planning and managingelectricity supplies. It utilized numerous statistical methods to assess the effectiveness of the suggested prediction model, mean square error in different models of machine learning, recurring neural networks. The efficacy of the proposed system regarding the delay, throughput, and resource using hyperleader caliper is shown. Finally, the suggested approach is successfully applied for power crowdsourcing between prosumer and customer to reach service reliability based on trial findings. The actual and predicted cost analysis has been increased (95%). It minimizes the delay rate to (40.3%) by improving the efficiency rate.

This paper investigates the effect of data integrity attacks on the central control of the microgrids (MGs), which can lead to severe blackouts and load shedding. It assesses this cyber attack from the steady state and optimal scheduling point of view. In order to stop the cyber hacking, a new deep learning-based framework has been developed based on the generative adversarial networks (GANs). In this framework, two networks compete with each other, wherein the first network generates fake data, and the second one is responsible for the data classification. In order to get into the most optimal features, a new optimization method based on a modified teaching-learning based optimization (TLBO) algorithm is also devised to reinforce the GAN model and help a better matching training process. In addition, a new modification is introduced for TLBO to avoid premature convergence and provide high population diversity. To show the effectiveness of the proposed framework, a real dataset of several smart metering devices in a MG has been tested. Results illustrate the high performance of the proposed framework, comparing to the well-known conventional detection frameworks with hit rate of 93.11%, miss rate of 6.89%, false alarm rate of 7.76% and correct reject rate of 92.24%.

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