LoRa based low power wide area network. The objective of this paper is to build up a LoRa based smart agricultural management and monitoring system using Wireless Sensor Networks (WSN) in rural areas, in order to replace the current technology of the agricultural monitoring system. A private network server is created and interfaced with a gateway that collects data or signals from end nodes and transmits the data to the cloud without the use of routers. The data can be used for end user application. The network interface is fulfilled by LoRa by solving communication failure problems and energy saving data transmission. This intelligent agriculture platform improves the efficiency of agricultural techniques.

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.

Experimental set up of double basin solar still with evacuated tubes has been fabricated by locally available materials and then carry out research work by use of solid fins. Here 2.5 cm constant depth of water maintained and 3 cm depth mild steel fins are used. Experimental results have been obtained during month of March and April 2018 during clear sky days. Month of March 2018 is chosen for experiments on double basin solar still with evacuated tubes only and April 2018 is chosen for experiments on double basin solar still with evacuated tubes and fins. After series of experiments, it has been found that, the fins increase 25% distillate output compared with alone double basin solar still with evacuated tubes. It also increases distillate output during sunshine hours as well as off-sunshine hours.

This paper proposes an improved GA based new approach for the optimal design of Hybrid Energy Systems comprising of solar PV, wind, diesel generator and battery connected systems for supplying the electrical power. The proposed system uses the meteorological data of wind speed, wind velocities and solar radiations, which was collected from NASA website. Optimization of Hybrid Energy System includes minimization of total cost, net present cost, loss of power system probability, reduced greenhouse gas emissions, cost of energy, annualized system cost to make the system more economically for the domestic house hold applications. The optimization of results can be achieved by using improved Genetic Algorithm (GA), which uses the variables such as wind turbine capacity, PV array ratings, number of battery banks, diesel generator rated power, system initialization cost and operation and maintenance cost were considered as the input chromosomes for the algorithm. A suitable improved GA program has been formulated using MATLAB toolbox with the objective function of minimizing the total/net present cost of the proposed hybrid energy system for electrifying the rural areas in standalone applications.

Wind-Photovoltaic with backup energy storage system like battery bank and diesel generator based hybrid energy system becomes popular day by day, because it gives the reliable energy resources with decrease in price of turbines, PV array, battery banks and diesel generator with reduced emissions of fuel. The optimization techniques are mainly used to minimize the total cost of the system, which incorporates the initial investment cost, running cost, maintenance cost and get back prices of the each component, to supply reliable electricity to meet out the electrical energy demand of the remote area, where the transmission cost is very high for such areas consisting of huge dense forests, remote areas, etc. The comparison of the performance characteristics of each hybrid system has been carried out using Particle Swarm Optimization (PSO) and GA. The hybrid system configuration is derived mainly based on the meteorological data of both wind speed, solar radiation, and the ambient atmospheric temperature. This paper has been proposed to minimize the cost of energy and the loss of power supply probability using enhanced multiobjective PSO algorithm for the optimum utilization of hybrid energy systems to make the system economical and more reliable for the house hold applications.