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The Smart Grid operates autonomously, facilitating the smooth integration of diverse power generation sources into the grid, thereby ensuring a continuous, reliable, and high-quality supply of electricity to end users. One key focus within the realm of smart grid applications is the Home Energy Management System (HEMS), which holds significant importance given the fluctuating availability of generation and the dynamic nature of loading conditions. This paper presents an overview of HEMS and the methodologies utilized for load forecasting. It introduces a novel approach employing Quantum Support Vector Machine (QSVM) for predicting periodic power consumption, leveraging the AMPD2 dataset. In the establishment of a microgrid, various factors such as energy consumption patterns of household appliances, solar irradiance, and overall load are taken into account in dataset creation. In the realm of load forecasting in Home Energy Management Systems (HEMS), the Quantum Support Vector Machine (QSVM) stands out from other methods due to its unique approach and capabilities. Unlike traditional forecasting methods, QSVM leverages quantum computing principles to handle complex and nonlinear electricity consumption patterns. QSVM demonstrates superior accuracy by effectively capturing intricate relationships within the data, leading to more precise predictions. Its ability to adapt to diverse datasets and produce significantly low error values, such as RMSE and MAE, showcases its efficiency in forecasting electricity load consumption in smart grids. Moreover, the QSVM model's exceptional flexibility and performance, as evidenced by achieving an accuracy of 97.3% on challenging datasets like AMpds2, highlight its distinctive edge over conventional forecasting techniques, making it a promising solution for enhancing forecasting accuracy in HEMS.The article provides a brief summary of HEMS and load forecasting techniques, demonstrating and comparing them with deep learning models to showcase the efficacy of the proposed algorithms. [ABSTRACT FROM AUTHOR]

The integration of smart homes into smart grids presents numerous challenges, particularly in managing energy consumption efficiently. Non-intrusive load management (NILM) has emerged as a viable solution for optimizing energy usage. However, as smart grids incorporate more distributed energy resources, the complexity of demand-side management and energy optimization escalates. Various techniques have been proposed to address these challenges, but the evolving grid necessitates intelligent optimization strategies. This article explores the potential of data-driven NILM (DNILM) by leveraging multiple machine learning algorithms and neural network architectures for appliance state monitoring and predicting future energy consumption. It underscores the significance of intelligent optimization techniques in enhancing prediction accuracy. The article compares several data-driven mechanisms, including decision trees, sequence-to-point models, denoising autoencoders, recurrent neural networks, long short-term memory, and gated recurrent unit models. Furthermore, the article categorizes different forms of NILM and discusses the impact of calibration and load division. A detailed comparative analysis is conducted using evaluation metrics such as root-mean-square error, mean absolute error, and accuracy for each method. The proposed DNILM approach is implemented using Python 3.10.5 on the REDD dataset, demonstrating its effectiveness in addressing the complexities of energy optimization in smart grid environments. [ABSTRACT FROM AUTHOR]

The main contribution of this review work is to show how various control techniques are used to manage the speed of Permanent Magnet Synchronous Motor (PMSM). The PMSM's are mostly used in electric vehicles, electric traction and high performance industrial drive applications. In this article conventional sensorless techniques are compared with machine learning techniques such as fuzzy logic, artificial neural network and neuro-fuzzy controllers to control the speed of PMSM drive based on vector control approach. The benefits of machine learning techniques used in sensorless PMSM drive are easy to design, less execution time and fast access speed control. The various controlling techniques used in controller along with its complexity, advantages and drawbacks are discussed in this article. The above mentioned controlling techniques are implemented and simulated by using MATLAB R2019b/Simulink software based on sensorless Model Reference Adaptive System (MRAS) with the help of Field Oriented Control (FOC) strategy of PMSM drive. By comparing the all sensorless controlling techniques in simulation study, it is identified that the combination of neuro-fuzzy controller gives the best speed control performance than other controllers. [ABSTRACT FROM AUTHOR]

A key focus of the Sustainable Development Goals (SDGs) is to harvest electricity from renewables. The fields of renewable energy sources (RES) and harvesting methods have seen tremendous growth. Multiport power electronic converter circuits integrate multiple energy sources. This integration aids in powering the load with different required voltage ratings. A simple and reliable control scheme for this system can maintain high power transfer efficiency and reliability. To integrate RES, non-isolated (NI) DC-DC multiport converters (MPC) are introduced; however, the main objective is to concentrate on NI MPC which provide a reliable output. In this paper non-isolated MPC topologies are reviewed and analyzed based on difference parameters considering circuit configuration; operating principal; number of components; complexity; and efficiency. This comprehensive review can be used as a guide for the suitable selection of converter topology to RES integration. [ABSTRACT FROM AUTHOR]

The increased use of electronics for control and use of nonlinear type loads by consumers in the present power system network are injecting harmonics into the power signal, due to which the power quality issue has become more challenge for the present researchers. In this work, the reduced order generalized integrator (ROGI) and fuzzy logic control (FLC) are collectively used to reduce the current harmonics in the power system. FLC‐SVM technique is used to maintain the process of SAF—Shunt active filter with fixed switching frequency. This proposed control technique consists of control loop for current to have fast and effective control. The Proportional Resonant controller is used to have control on voltage for a slow and effective control process which is also used to compensate reactive power. The shunt active filter is designed with the help of ROGI by integrating PI controller for fuzzy‐based SVM to reduce the current harmonics on the load side. The results are achieved in MATLAB/SIMULINK software. [ABSTRACT FROM AUTHOR]