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This research presents an algorithm based on Artificial Neural Networks (ANN), for estimating monthly mean daily and hourly values of solar global radiation. To effectively investigate solar energy consumption and estimate solar renewable energy resources, the Hourly Global Solar Radiation measurements are necessary. In order to predict monthly average daily global sun irradiance on a horizontal area of Kazaure- Nigeria, this study creates a model utilizing ANN to solve the problem of solar energy distribution. Five empirical correlations are developed using the data from 42 months to aid in the prediction of the solar energy distribution pattern. The software is constructed around the Multilayer Perceptron under categorized tabs, with Multilayer perception in neural network Toolbox in MATLAB 9.7 version as a feed forward ANN that maps sets of input data into a set of suitable output. It differs from conventional linear perception by employing three or more layers of neurons (nodes) with nonlinear activation functions. It is also more effective than perceptrons in identifying input that is not linearly separable by a linear hyper-plane. Results obtained utilizing the suggested structure reveals good agreement between the calculated and measured levels of global solar irradiation. The ANN model is shown to be superior when compared to empirical models, due to negligible noise margin.

Carbon-fiber-reinforced polymer (CFRP) is widely acknowledged as a leading advanced material structure, offering superior properties compared to traditional materials, and has found diverse applications in several industrial sectors, such as that of automobiles, aircrafts, and power plants. However, the production of CFRP composites is prone to fabrication problems, leading to structural defects arising from cycling and aging processes. Identifying these defects at an early stage is crucial to prevent service issues that could result in catastrophic failures. Hence, routine inspection and maintenance are crucial to prevent system collapse. To achieve this objective, conventional nondestructive testing (NDT) methods are utilized to inspect CFRP components. However, the restricted field penetration within the CFRP makes conventional NDT approaches ineffective. Recently, microwave techniques have been developed to address the challenges associated with CFRP inspection by providing better material penetration and more precise results. This paper offers a review of the primary NDT methods employed to inspect CFRP composites, emphasizing microwave-based NDT techniques and their key features.

Nigeria is a country in West African region of the world blessed with enormous potential of renewable energy resources such as wind, hydro, solar, animal waste and municipal waste. Despite the availability of these energy resources in large quantity, the country is still ranked among the countries in the world with very poor access to electricity. This paper tends to suggest an approach towards solving the problem of irregular supply of electricity in Hussaini Federal Polytechnic located in Jigawa, a state in northwestern part of Nigeria. This approach involves sectionalizing the polytechnic into two sections and integrating photovoltaic energy system to an already existing utility distribution network in each of the sections. These interconnected energy sources are to be used in charging the storage systems located within each of the sections. Electricity will be supplied to the load in a particular section from the storage system located within the section, through existing distribution network in the polytechnic. The sizing of the storage system, the inverter, the charge controller and the photovoltaic array were done by normal renewable energy system calculation. From the results obtained, each of the sections will require a set of 250kW 480V hybrid inverter, twenty thousand pieces of 250W/24V photovoltaic panels and 2,798.5kWh battery capacity.