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The forecasting of home energy consumption is a crucial and challenging topic within the realm of artificial intelligence (AI)-enhanced energy management in smart grids (SGs). The primary goal of this study is to provide accurate energy consumption forecasts for a smart home. Two deep learning models are implemented: ConvLSTM, which combines convolutional operations with Long Short-Term Memory (LSTM), and the CNN-LSTM model, which synergizes Convolutional Neural Networks (CNN) and LSTM networks. Both hybrid models offer a comprehensive approach to modeling complex relationships in spatial and temporal patterns. Additionally, two baseline models -LSTM and CNN -are employed for comparative analysis. Utilizing real data from a smart home in Houston, Texas, the results demonstrate that both the hybrid models deliver highly accurate predictions for energy consumption. However, the ConvLSTM model outperforms all proposed models, improving predictions in terms of mean absolute percentage error by 4.52%, 9.59%, and 10.53% for 1 day, 3 days, and 6 days in advance, respectively. [ABSTRACT FROM AUTHOR]

As the number of individuals who drive electric vehicles increases, it is becoming increasingly important to ensure that charging infrastructure is both dependable and conveniently accessible. Methodology: In this paper, a recommendation system is proposed with the purpose of assisting users of electric vehicles in locating charging stations that are closer to them, improving the charging experience, and lowering range anxiety. The proposed method is based on restricted Boltzmann machine learning to collect and evaluate real-time data on a variety of aspects, including the availability of charging stations and historical patterns of consumption. To optimize the parameters of the restricted Boltzmann machine, a new optimization algorithm is proposed and referred to as parallel greylag goose (PGGO) algorithm. The recommendation algorithm takes into consideration a variety of user preferences. These preferences include charging speed, cost, network compatibility, amenities, and proximity to the user's present location. By addressing these preferences, the proposed approach reduces the amount of irritation experienced by users, improves charging performance, and increases customer satisfaction. Results: The findings demonstrate that the method is effective in recommending charging stations that are close to drivers of electric vehicles. On the other hand, the Wilcoxon rank-sum and Analysis of Variance tests are utilized in this work to investigate the statistical significance of the proposed parallel greylag goose optimization method and restricted Boltzmann machine model. The proposed methodology could achieve a recommendation accuracy of 99% when tested on the adopted dataset. Conclusion: Based on the achieved results, the proposed method is effective in recommending systems for the best charging stations for electric vehicles. [ABSTRACT FROM AUTHOR]

Climate change is a global issue that requires collective action to address. One of the most pressing concerns is reducing emissions resulting from combustion processes. The use of renewable energy sources and green energy has become a trend worldwide. Solar energy is one of the most promising sources due to its abundance and simplicity of implementation. The city of Aswan, located in the South of Egypt, has a high solar radiation that makes it ideal for utilizing solar power. The current study investigates the optimal design for a sustainable building electricity system at the Aswan Campus of the Arab Academy for Science, Technology & Maritime Transport (AASTMT) in Egypt. The campus has four electricity sources: a utility grid, PV panels, batteries, and a diesel generator, along with a weather station. Experimental investigations have been carried out in this research paper to study the performance characteristics of solar power. Moreover, HOMER pro software is used to model various configurations of the campus grid, including different photovoltaic (PV) panel types and tracking systems. The simulations are compared with real-world data collected from a weather station on campus. Additionally, CO2 and NO2 emissions are measured to assess the environmental impact of different scenarios. The total net cost over the life cycle is also calculated for different cases. The results demonstrate that the addition of a PV renewable system can reduce traditional grid usage by 38% and emissions by 50%. A decrease in the Levelized Cost of Energy (LOCE) from USD 0.0647 to USD 0.0535 is reported. Moreover, the difference in NCP cost between dual-axis tracking and fixed zero angle is USD 143,488. The dual degree tracker for PV panels can further enhance energy production by 30% more, compared with fixed panels, while reducing carbon dioxide emissions by more than 20%. The simulation results reveal that tracking systems provide greater energy generation, and that a cost–benefit analysis may prioritize fixed panels in some cases. The results from the HOMER software simulations closely match those of the experimental data, which is that the total presentation error does not exceed 8%, demonstrating the software's effectiveness for optimizing renewable energy systems. This study demonstrates that a comprehensive analysis and optimization of a building's energy sources can significantly reduce costs, lower emissions, and promote the use of renewable energy, particularly solar power. [ABSTRACT FROM AUTHOR]

This article represents a novel study of the design and analysis of a wind turbine system that includes a line-side permanent magnet synchronous generator (PMSG) with an ultra-step-up DC-DC converter for voltage regulation. Integrating renewable energy sources such as wind power into the grid requires efficient and reliable power conversion systems to handle fluctuating power and ensure a stable power supply. The wind turbine system utilizes a PMSG, which offers several advantages over traditional induction generators, including higher efficiency, reduced maintenance, and better power quality. The line-side configuration allows for increased control and flexibility, allowing the system to respond dynamically to grid conditions. This wind turbine system involves the integration of a grid-side PMSG-fed DC-DC converter between the PMSG and the grid. The converter enables a seamless flow of electricity between the wind turbine and the grid. By actively controlling the intermediate circuit voltage, the converter efficiently regulates the output voltage of the wind turbine and thus enables constant power generation regardless of fluctuating wind speeds. The simulation outcomes illustrate the efficacy of the proposed system in achieving voltage regulation and seamless integration with the grid. Performance is evaluated under various operating conditions and compared to conventional wind turbines. [ABSTRACT FROM AUTHOR]