Data-driven probabilistic machine learning in sustainable smart energy/smart energy systems: Key developments, challenges, and future research opportunities in the …
The current trend indicates that energy demand and supply will eventually be controlled by
autonomous software that optimizes decision-making and energy distribution operations …
autonomous software that optimizes decision-making and energy distribution operations …
Data-driven next-generation smart grid towards sustainable energy evolution: techniques and technology review
Meteorological changes urge engineering communities to look for sustainable and clean
energy technologies to keep the environment safe by reducing CO 2 emissions. The …
energy technologies to keep the environment safe by reducing CO 2 emissions. The …
Prediction of heating and cooling loads based on light gradient boosting machine algorithms
J Guo, S Yun, Y Meng, N He, D Ye, Z Zhao, L Jia… - Building and …, 2023 - Elsevier
Abstract Machine learning models have been widely used to study the prediction of heating
and cooling loads in residential buildings. However, most of these methods use the default …
and cooling loads in residential buildings. However, most of these methods use the default …
A multigeneration cascade system using ground-source energy with cold recovery: 3E analyses and multi-objective optimization
A novel integrated energy system based on a geothermal heat source and a liquefied
natural gas heat sink is proposed in this study for providing heating, cooling, electricity …
natural gas heat sink is proposed in this study for providing heating, cooling, electricity …
Multi-scale solar radiation and photovoltaic power forecasting with machine learning algorithms in urban environment: A state-of-the-art review
J Tian, R Ooka, D Lee - Journal of Cleaner Production, 2023 - Elsevier
Solar energy has been rapidly utilized in urban environments owing to its significant
potential to fulfill the energy demand. The precise forecasting of solar energy, including solar …
potential to fulfill the energy demand. The precise forecasting of solar energy, including solar …
Principles, research status, and prospects of feature engineering for data-driven building energy prediction: A comprehensive review
With the rapid growth in the volume of relevant and available data, feature engineering is
emerging as a popular research subject in data-driven building energy prediction owing to …
emerging as a popular research subject in data-driven building energy prediction owing to …
Analysis of feature matrix in machine learning algorithms to predict energy consumption of public buildings
Y Ding, L Fan, X Liu - Energy and Buildings, 2021 - Elsevier
With the development of building information and energy consumption data, machine
learning methods are increasingly being used for predicting and analyzing building energy …
learning methods are increasingly being used for predicting and analyzing building energy …
Outlet water temperature prediction of energy pile based on spatial-temporal feature extraction through CNN–LSTM hybrid model
Energy pile is a novel ground heat exchanger for ground source heat pump (GSHP)
systems. Prediction of the energy pile outlet water temperature is essential for the efficient …
systems. Prediction of the energy pile outlet water temperature is essential for the efficient …
[HTML][HTML] A taxonomy of machine learning applications for virtual power plants and home/building energy management systems
A Virtual power plant is defined as an information and communications technology system
with the following primary functionalities: enhancing renewable power generation …
with the following primary functionalities: enhancing renewable power generation …
[HTML][HTML] Offshore wind power forecasting based on WPD and optimised deep learning methods
Accurate wind power forecasting is vital for (i) wind power management,(ii) penetration
increment of the power generated into the power grid, and (iii) making maintenance more …
increment of the power generated into the power grid, and (iii) making maintenance more …