A Comparative Analysis of Different Methods for Predicting Solar Radiation Using Deep Neural Networks: An Innovative Approach to Sustainable Community Building

Abstract

In order to deal with the electrical crisis in an efficient manner, it is imperative to promote the use of renewable energy sources, with a specific emphasis on solar energy. Nevertheless, the challenge lies in the variable patterns of solar irradiance, which are influenced by seasonal weather variations, making it a complex factor to predict. The primary aim of this study is to predict the solar radiation on inclined surfaces, while considering the impact of meteorological variables like temperature, wind speed, humidity, and air pressure. The research used the Artificial Neural Network (ANN) methodology to examine the Douala metropolitan area. Consequently, the model may be used to estimate solar irradiance not only inside the specified study area but also across locations with similar climatic conditions, by using different combinations of input data. The model exhibited its proficiency in appropriately evaluating sun ray intensities by generating a noteworthy outcome via its application using (50 concealed-layer neural network networks along the logistic Sigmoid function. Keywords: solar radiation, neural networks, feed-forward neuron networks, and multilayer perceptron’s.

Introduction

The advancement of artificial intelligence (AI), particularly Artificial Neural Networks (ANNs) and Recurrent Neural Networks (RNNs), has significantly impacted renewable energy, especially solar power forecasting. Accurate solar radiation prediction is crucial for optimizing green energy systems, smart grids, and sustainable urban planning. ANNs and RNNs effectively analyze time-series solar data, enhancing the integration of solar energy into power grids and promoting environmental sustainability.

This study compares various ANN methodologies, focusing on RNNs, to assess their accuracy and adaptability in solar radiation forecasting. The research highlights the importance of solar irradiance forecasting for sustainable development, especially in energy-deficient regions like Central Africa, where solar energy is a promising solution.

The literature review emphasizes RNNs’ strength in short-term temporal predictions and ANNs’ flexibility in modeling nonlinear relationships. Both models are valuable, with their use depending on project requirements like data availability and forecasting needs.

The study introduces a novel ANN model incorporating urban parameters (e.g., building height, location) to improve solar radiation predictions for urban planning. Methodologically, it uses satellite and survey data, applying statistical and machine learning models (ARMA, SVM, deep learning) to forecast solar irradiance.

Comparative analysis shows ANN achieving higher accuracy (91.4%) than RNN (89.9%), underlining their complementary strengths in solar radiation forecasting. Overall, the research advances sustainable energy strategies by enhancing solar energy forecasting accuracy, contributing to ecological stewardship, smart grid reliability, and sustainable urban development.

Conclusion

This research introduces efficient artificial neural network (ANN) and recurrent neural network (RNN) methods for predicting solar radiation. These findings have implications for improving solar energy systems and promoting sustainable community development. Subsequent investigations might delve into supplementary environmental factors, hybrid deep neural network (DNN) models, and intelligent solar energy system configurations aimed at fostering sustainable communities

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