Summary This research work presents a thermoelectric energy harvesting system comprises of a double input DC‐DC converter with maximum power point tracking (MPPT) technique under varying temperature conditions (VTCs). The converter has two inputs with N stages of diode‐capacitor to boost the output voltage. It has the advantages of higher voltage gain and flexibility of power‐sharing by both the independent sources. The perturb and observe (P&O)‐based MPPT algorithm is an efficient and simple method to track the maximum power. However, the power‐current (P‐I) characteristics of the thermoelectric modules exhibit multiple peaks at VTCs; it fails to identify the global peak point (GPP) and gets track the local peak point. To overcome the drawback of the P&O technique, a particle swarm optimization (PSO)‐based MPPT technique is implemented to track the GPP. A comparison is performed between the P&O and PSO technique in terms of MPPT tracking efficiency and oscillation around the maximum power point. From the acquired results of simulation and experiment, it is recommended that the PSO‐based MPPT technique has furnished better overall performance.

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.

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Demand-side energy management increases the unpredictability and ambiguity of forecasting the load profiles of residential energy management. The energy management accuracy seems to be low by employing a traditional residential energy forecasting algorithm. This research work emphasizes on design and development of computer-assisted residential energy management by forecasting employing a deep learning algorithm. Hankel matrix is formed using copula function to process the collected automatic metering infrastructure (AMI) load data in the smart grid. From this data processing, model optimization was obtained by the proposed novel pooling-based deep neural network (PDNN). Moreover, this proposed PDNN avoid overfitting problem in training and testing by increasing AMI data variety and data size. The proposed PDNN is implemented in the TensorFlow platform. Based on real-time AMI southern grid data onto Tamil Nadu Electricity Board, India testing case studies was carried. Compared to traditional residential energy management techniques the proposed deep learning model outperforms support vector machine by 9.5% and 12.7%, deep belief network by 6.5% and 9.5%, and neural network auto aggressive integral moving average by 20.5% and 8.5% in terms of accuracy of energy forecasting and mean absolute error, respectively. Overall, the obtained results proved the effectiveness of the proposed deep learning algorithm for residential short-term load forecasting and management over other traditional methods. Highlights In this research work: • Novel pooling-based deep neural network is applied for residential energy management in a smart grid. • The copula fusion theory is adopted to improve the accuracy of load management in a smart grid. • Day-ahead and week-ahead prediction load on Tamil Nadu Electricity Board dataset in summer and winter season was used to validate the performance of the proposed method with other data-driven methods.