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.
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.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.