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As non-renewable energy sources are in the verge of exhaustion, the entire world turns towards renewable sources to fill its energy demand. In the near future, solar energy will be a major contributor of renewable energy, but the integration of unreliable solar energy sources directly into the grid makes the existing system complex. To reduce the complexity, a microgrid system is a better solution. Solar energy forecasting models improve the reliability of the solar plant in microgrid operations. Uncertainty in solar energy prediction is the challenge in generating reliable energy. Employing, understanding, training, and evaluating several forecasting models with available meteorological data will ensure the selection of an appropriate forecast model for any particular location. New strategies and approaches emerge day by day to increase the model accuracy, with an ultimate objective of minimizing uncertainty in forecasting. Conventional methods include a lot of differential mathematical calculations. Large data availability at solar stations make use of various Artificial Intelligence (AI) techniques for computing, forecasting, and predicting solar radiation energy. The recent evolution of ensemble and hybrid models predicts solar radiation accurately compared to all the models. This paper reviews various models in solar irradiance and power estimation which are tabulated by classification types mentioned.

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Drones of various types are currently in great demand because of their flexible applications to facilitate human life. At acceptable constant quality levels, they can perform tasks in a repetitive manner. A drone is intended and built in the current work to evaluate the area vulnerable to fire and its surface area at an altitude of 10 meters. In the event of a forest fire disaster, evaluating the impacted area is very complicated. This approach needs to be adaptable and easily controlled in order to solve it. Thus, both the manual and autopilot mode are built and controlled by a quad copter drone with an ardupilot, which drives the drone to the specified location. The drone is fed with the specification of the Global Positioning System ( GPS) and flies with the aid of an ardupilot to the spot. With the aid of a thermal imaging sensor, the drone senses the surface area with its captured image. With the aid of coding dumped in it, the image is sent to the base station and the vision building is achieved with the help of the thermal camera fitted in the front part of the drone and then it interacts with the base station where it is possible to view the surface area. This allows average individuals to recognize the region impacted by the tragedy and to predict the amount of impact they have made in a shorter period of time. Human interference is minimized by this detection method in the areas affected by fire with the extent of fire prediction.

A prototype Unmanned Aerial Vehicle (UAV) is designed for the purpose of last mile delivery to supply medicines and vaccines. This work aims to ascertain that UAVs can be employed to decrease transportation times, increased power efficiency, and improved safety for transportation of necessary products. The prototype model is design to deliver 1 kg maximum payload up to 5 km with an endurance time of 30 minutes. The model is fabricated with a wing span of 1.5 m, Aspect ratio 7, and contains NACA 6 series airfoil shape as its cross section. During the transportation of payload, it has to fly beyond the line of sight which requires an autonomous Pixhawk flight controller installed in the model. Autonomous module aids in the safe flight avoiding the obstacles during the operation. To comply with government regulations for flying the model cruises at an altitude of 350 feet. After reaching the desired GPS destination, the payload is delivered to the target customer via parachute. However the model requires a runway length of 200 m for its takeoff and landing operation.

Estimation of daily global solar radiation (GSR) for a particular location is a key parameter in modeling and designing any solar energy system. Very limited observatory stations are available to collect solar radiation data. This demands solar radiation estimation for new locations where there is no observatory station. In the present study, solar radiation models are developed for estimating daily GSR for sunny locations of India. Solar radiation data are collected for a period of three years from in situ measurements. Angstrom-Prescott linear correlation and nonlinear correlations such as quadratic, cubic, exponential, and power models are developed based on the bright sunshine duration. In this new approach, the “bright sunshine duration” is estimated by a new variable named “approximate bright sunshine duration.” The developed linear one input parameter model is applied to predict the daily GSR for five other Indian locations, namely, Allahabad, Bhopal, Chennai, Hyderabad, and Mumbai. The developed linear one input parameter model was further modified as a latitude dependent model to improve the accuracy of the model and named as the latitude model. The performance of all the models is analysed by using the statistical tools, namely, Relative Root Mean Square Error, Mean Percentage Error, and Mean Absolute Percentage Error. The latitude model resulted in fair to good estimations for Tiruchirappalli and the other five Indian locations of latitudes ranging from 10°N to 26°N. Further, other solar radiation models from the literature were considered to identify their applicability for the same selected locations. The models recommended for the selected Indian locations are presented. Out of all the universal models, the monthly mean daily GSR model of Page and out of all Indian models, the monthly mean daily GSR models of Katiyar and Mani showed satisfactory performance for the majority of selected Indian locations.