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Summary: Microgrid is a new era in the power system and it has more scope of investigation on research. Due to an increase in demand and future expansion of the power system, analyzing the complexities of the network becomes a challenging task. Artificial intelligence plays a vital role in resolving such issues in a microgrid in various aspects. Owing to the rapid growth of periodical update in computational cost reduction, enhanced data analysis‐based algorithm artificial intelligence enters into new epoch Artificial Intelligence AI 2.0. Based on such approach, machine learning has been evolved as AI 2.0 initially. Now, it develops branches like deep learning, reinforcement learning, and a combination of both deep reinforcement learning algorithms. These algorithms are precise to attain higher priority in decision‐making under a complex network. This paper deals with numerous challenges of the above algorithm to state the significance of AI 2.0 and summarization of their application toward microgrid is useful to analyze the power system. [ABSTRACT FROM AUTHOR]

Summary: Non‐technical loss (NTL) is detrimental to the smart grid. Intelligent application of advanced metering infrastructure (AMI) helps to solve NTL detection and classification. By using advanced learning algorithms, data analysis on the massive data generated by AMI is helpful in the detection and classification of electricity theft. Conventional data analysis algorithm, like Support Vector Machine (SVM), Random Forest Algorithm (RFA), and 1D‐ Conventional Neural Network (1D‐CNN), has low detection and classification accuracy of electricity theft. Because these methods failed to predict and classify multidimensional electricity consumption data by various consumers in AMI in the smart grid. In this research work, a multidimensional deep learning algorithm is proposed to learn and classify the non‐periodicity of electricity. This helps to detect electricity theft by a consumer from the periodic load curve. Both weekly load patterns and daily load patterns are processed as 2D electricity data samples. From the proposed multidimensional deep learning model, an average classification accuracy of 97.5% and a precision‐recall of 0.97 were obtained. This validates that the proposed deep learning model outperforms other conventional data analysis classification algorithm. [ABSTRACT FROM AUTHOR]