A focused review of ANN-based models for Predicting Absorption Maxima (λmax) of Dyes

G Rani, N Tomar, VS Dhaka, PK Surolia… - … Conference on Big …, 2023 - ieeexplore.ieee.org
The rapidly increasing demand for energy and the consequent depletion of non-renewable
energy sources pose significant challenges. Seeking alternatives, renewable sources like …

Molecular structure-based prediction of absorption maxima of dyes using ann model

N Tomar, G Rani, VS Dhaka, PK Surolia… - Big Data and Cognitive …, 2023 - mdpi.com
The exponentially growing energy requirements and, in turn, extensive depletion of non-
restorable sources of energy are a major cause of concern. Restorable energy sources such …

Influence of the Cell Temperature on the Performance of a Dye Sensitized Solar Cell

Z Varga, E Rácz - 2021 IEEE 19th World Symposium on …, 2021 - ieeexplore.ieee.org
A great deal of acts have been done for the renewable and green energy to fulfil the global
energy demand. The European Union has been working on reducing the carbon-dioxide …

How much chemistry does a deep neural network need to know to make accurate predictions?

GB Goh, C Siegel, A Vishnu, N Hodas… - 2018 IEEE Winter …, 2018 - ieeexplore.ieee.org
The meteoric rise of deep learning models in computer vision research, having achieved
human-level accuracy in image recognition tasks is firm evidence of the impact of …

Solar Irradiance Prediction using Deep Learning-Based Approaches

NG Hari, G Jisha - 2022 IEEE Asia-Pacific Conference on …, 2022 - ieeexplore.ieee.org
The worst effects of pollution from greenhouse gas emissions are increase in atmospheric
temperature, sea level rise, and a variety of diseases. The main source of greenhouse gases …

A Solar Module Power Production Model Predictor for Automated Solar Module Manufacturing

C Santoro, FF Santoro, E Arena… - 2022 IEEE Intl Conf on …, 2022 - ieeexplore.ieee.org
Sunlight is one of the most abundant and freely available energy resources on our planet.
The capacity to transform this solar energy in electrical power depends on the quality and …

Solar irradiance prediction using transformer-based machine learning models

A Demir, LF Gutiérrez, AS Namin… - 2022 IEEE International …, 2022 - ieeexplore.ieee.org
This paper presents a study of irradiance prediction using a transformer-based machine
learning model for the photovoltaic (PV) renewable energy system. We explore the forecast …

Predicting solar radiation using machine learning techniques

A Moosa, H Shabir, H Ali, R Darwade… - … on intelligent computing …, 2018 - ieeexplore.ieee.org
Today, energy is something that is taken for granted. Though non-renewable energy has a
harmful impact on environment., its usage is all time high and is soon going to deplete. As …

Forecast of solar energy production-A deep learning approach

R Zhang, M Feng, W Zhang, S Lu… - 2018 IEEE International …, 2018 - ieeexplore.ieee.org
Solar energy penetration both at utility scale and residential scale has been increasing at an
exponential rate. However, its stochastic nature poses great challenge to power grid …

Energy Efficient Mobile Application for Solar Energy using Machine Learning

S Rathee, A Yadav, R Choudhary - 2024 1st International …, 2024 - ieeexplore.ieee.org
As individuals search for environmentally acceptable ways to preserve the environment,
solar panels are a great option. However, the utility sector requires intelligent technologies …