Machine learning and deep learning in energy systems: A review
With population increases and a vital need for energy, energy systems play an important
and decisive role in all of the sectors of society. To accelerate the process and improve the …
and decisive role in all of the sectors of society. To accelerate the process and improve the …
Artificial intelligence in physical sciences: Symbolic regression trends and perspectives
D Angelis, F Sofos, TE Karakasidis - Archives of Computational Methods …, 2023 - Springer
Symbolic regression (SR) is a machine learning-based regression method based on genetic
programming principles that integrates techniques and processes from heterogeneous …
programming principles that integrates techniques and processes from heterogeneous …
Recent advances in machine learning research for nanofluid-based heat transfer in renewable energy system
Nanofluids have gained significant popularity in the field of sustainable and renewable
energy systems. The heat transfer capacity of the working fluid has a huge impact on the …
energy systems. The heat transfer capacity of the working fluid has a huge impact on the …
Forecasting of transportation-related energy demand and CO2 emissions in Turkey with different machine learning algorithms
Ü Ağbulut - Sustainable Production and Consumption, 2022 - Elsevier
Adverse impacts of the transportation sector on not only air quality but also economic growth
of a country are nowadays well-noticed, particularly by develo** countries. Today, the …
of a country are nowadays well-noticed, particularly by develo** countries. Today, the …
Electricity production based forecasting of greenhouse gas emissions in Turkey with deep learning, support vector machine and artificial neural network algorithms
MS Bakay, Ü Ağbulut - Journal of Cleaner Production, 2021 - Elsevier
Today, the world's primary energy demand has been met by the burning of fossil-based fuels
at a rate of 85%. This dominant use of fossil-based fuels has led to an accelerating increase …
at a rate of 85%. This dominant use of fossil-based fuels has led to an accelerating increase …
A review on global solar radiation prediction with machine learning models in a comprehensive perspective
Y Zhou, Y Liu, D Wang, X Liu, Y Wang - Energy Conversion and …, 2021 - Elsevier
Global solar radiation information is the basis for many solar energy utilizations as well as
for economic and environmental considerations. However, because solar-radiation …
for economic and environmental considerations. However, because solar-radiation …
How solar radiation forecasting impacts the utilization of solar energy: A critical review
N Krishnan, KR Kumar, CS Inda - Journal of Cleaner Production, 2023 - Elsevier
The demand for energy generation from solar energy resource has been exponentially
increasing in recent years. It is integral for a grid operator to maintain the balance between …
increasing in recent years. It is integral for a grid operator to maintain the balance between …
Machine learning-based time series models for effective CO2 emission prediction in India
China, India, and the USA are the countries with the highest energy consumption and CO 2
emissions globally. As per the report of datacommons. org, CO 2 emission in India is 1.80 …
emissions globally. As per the report of datacommons. org, CO 2 emission in India is 1.80 …
A novel approach based on integration of convolutional neural networks and deep feature selection for short-term solar radiation forecasting
H Acikgoz - Applied Energy, 2022 - Elsevier
In this study, a novel deep solar forecasting approach is proposed based on the complete
ensemble empirical mode decomposition with adaptive noise (CEEMDAN), continuous …
ensemble empirical mode decomposition with adaptive noise (CEEMDAN), continuous …
Short-term solar radiation forecasting using hybrid deep residual learning and gated LSTM recurrent network with differential covariance matrix adaptation evolution …
Develo** an accurate and robust prediction of long-term average global solar irradiation
plays a crucial role in industries such as renewable energy, agribusiness, and hydrology …
plays a crucial role in industries such as renewable energy, agribusiness, and hydrology …