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Intelligent Transport System (ITS) intentions to attain traffic efficiency by diminishing traffic difficulties. It supplies information like traffic issues, real-time traveling information, parking availability, etc., in advance to the users who are connected with the smart cities that ensure travelers' safety and comfort. This ITS technique should merge with Electric Vehicles (EVs) because nowadays, EVs have become familiar in the last decade owing to the requirement to cut greenhouse gas emissions and fossil fuels. However, traffic jams caused by EVs driven to the charging stations (CSs) can result in the complex charging scheduling of EVs. Therefore, an effective algorithm is developed for optimal charging scheduling using the proposed Grey Sail Fish Optimization (GSFO). The proposed charging scheduling algorithm integrates Grey Wolf Optimizer (GWO) and Sail Fish Optimization (SFO). For each EV, the demand when charging is computed. The path used by the EV to travel to the charging station is determined by computing the path decision factor. In comparison to existing techniques, the proposed GSFO-based charging algorithm schedules EVs to charging stations based on the fitness function, and the performance was improved with a traffic density of 26.11 km, a distance of 0.0278 kW, and a power of 2.3377. To be more specific, the proposed GSFO improved when many vehicles were considered. WOS:000794281100001

In order to formulate the long-term and short-term development plans to meet the energy needs, there is a great demand for accurate energy forecasting. Energy autonomy helps to decompose a large-scale grid control into a small sized decisions to attain robustness and scalability through energy independence level of a country. Most of the existing energy demand forecasting models predict the amount of energy at a regional or national scale and failed to forecast the demand for power generation for small-scale decentralized energy systems, like micro grids, buildings, and energy communities. A novel model called Sailfish Whale Optimization-based Deep Long Short- Term memory (SWO-based Deep LSTM) to forecast electricity demand in the distribution systems is proposed. The proposed SWO is designed by integrating the Sailfish Optimizer (SO) with the Whale Optimization Algorithm (WOA). The Hilbert-Schmidt Independence Criterion (HSIC) is applied on the dataset, which is collected from the Central electricity authority, Government of India, for selecting the optimal features using the technical indicators. The proposed algorithm is implemented in MATLAB software package and the study was done using real-time data. The optimal features are trained using Deep LSTM model. The results of the proposed model in terms of install capacity prediction, village electrified prediction, length of R & D lines prediction, hydro, coal, diesel, nuclear prediction, etc. are compared with the existing models. The proposed model achieves percentage improvements of 10%, 9.5%,6%, 4% and 3% in terms of Mean Squared Error (MSE) and 26%, 21%, 16%, 12% and 6% in terms of Root Mean Square Error (RMSE) for Bootstrap-based Extreme Learning Machine approach (BELM), Direct Quantile Regression (DQR), Temporally Local Gaussian Process (TLGP), Deep Echo State Network (Deep ESN) and Deep LSTM respectively. The hybrid approach using the optimization algorithm with the deep learning model leads to faster convergence rate during the training process and enables the small-scale decentralized systems to address the challenges of distributed energy resources. The time series datasets of different utilities are trained using the hybrid model and the temporal dependencies in the sequence of data are predicted with point of interval as 5 years-head. Energy autonomy of the country till the year 2048 is assessed and compared.

The proposed work outlines the architecture and implementation of an autonomous style of gardening, which operates by itself with the support of autonomous robots. The robot operates inside the garden with the help of sensors to monitor and maintain a database of the plants such as soil content, nutrients, environmental conditions and fruits location. In this paper, the architecture of the system along with experimental results for object recognition, navigation, and manipulation is presented and discussed. The work is carried out using cherry tomatoes which are fitted with sensors to keep track of the plant’s well-being. The proposed work reduces manual labour and increases the efficiency of the system.