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 …
Energy consumption and carbon emissions forecasting for industrial processes: Status, challenges and perspectives
Y Hu, Y Man - Renewable and Sustainable Energy Reviews, 2023 - Elsevier
The industrial process consumes substantial energy and emits large amounts of carbon
dioxide. With the help of accurate energy consumption and carbon emissions forecasting …
dioxide. With the help of accurate energy consumption and carbon emissions forecasting …
Tackling climate change with machine learning
Climate change is one of the greatest challenges facing humanity, and we, as machine
learning (ML) experts, may wonder how we can help. Here we describe how ML can be a …
learning (ML) experts, may wonder how we can help. Here we describe how ML can be a …
Forecast energy demand, CO2 emissions and energy resource impacts for the transportation sector
Managing energy demand and reducing greenhouse gas emissions are among the most
significant challenges ahead for many countries. Accurate prediction of energy demand and …
significant challenges ahead for many countries. Accurate prediction of energy demand and …
Day-ahead electricity price prediction applying hybrid models of LSTM-based deep learning methods and feature selection algorithms under consideration of market …
W Li, DM Becker - Energy, 2021 - Elsevier
The availability of accurate day-ahead electricity price forecasts is pivotal for electricity
market participants. In the context of trade liberalisation and market harmonisation in the …
market participants. In the context of trade liberalisation and market harmonisation in the …
Real-time natural gas release forecasting by using physics-guided deep learning probability model
Natural gas release from oil and gas facilities contributes significantly to the greenhouse
effect and reduces the benefit of displacing heavy fossil fuels with natural gas. Real-time …
effect and reduces the benefit of displacing heavy fossil fuels with natural gas. Real-time …
Machine learning for sustainable energy systems
In recent years, machine learning has proven to be a powerful tool for deriving insights from
data. In this review, we describe ways in which machine learning has been leveraged to …
data. In this review, we describe ways in which machine learning has been leveraged to …
Accurate forecasts and comparative analysis of Chinese CO2 emissions using a superior time-delay grey model
S Ding, J Hu, Q Lin - Energy Economics, 2023 - Elsevier
In China's new development stage, reaching carbon peaking and neutrality has emerged as
a complicated and substantial task, highlighting the importance of forecasting CO 2 …
a complicated and substantial task, highlighting the importance of forecasting CO 2 …
[HTML][HTML] Grid-supported electrolytic hydrogen production: Cost and climate impact using dynamic emission factors
Hydrogen production based on a combination of intermittent renewables and grid electricity
is a promising approach for reducing emissions in hard-to-decarbonise sectors at lower …
is a promising approach for reducing emissions in hard-to-decarbonise sectors at lower …
Arima models in electrical load forecasting and their robustness to noise
The paper addresses the problem of insufficient knowledge on the impact of noise on the
auto-regressive integrated moving average (ARIMA) model identification. The work offers a …
auto-regressive integrated moving average (ARIMA) model identification. The work offers a …