Review and prospect of data-driven techniques for load forecasting in integrated energy systems
With synergies among multiple energy sectors, integrated energy systems (IESs) have been
recognized lately as an effective approach to accommodate large-scale renewables and …
recognized lately as an effective approach to accommodate large-scale renewables and …
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 …
An improved Wavenet network for multi-step-ahead wind energy forecasting
Y Wang, T Chen, S Zhou, F Zhang, R Zou… - Energy Conversion and …, 2023 - Elsevier
Accurate multi-step-ahead wind speed (WS) and wind power (WP) forecasting are critical to
the scheduling, planning, and maintenance of wind farms. Previous forecasting methods …
the scheduling, planning, and maintenance of wind farms. Previous forecasting methods …
[HTML][HTML] Short-term electric net load forecasting for solar-integrated distribution systems based on Bayesian neural networks and statistical post-processing
The increasing integration of variable renewable technologies at distribution feeders, mainly
solar photovoltaic (PV) systems, presents new challenges to grid operators for accurately …
solar photovoltaic (PV) systems, presents new challenges to grid operators for accurately …
Accurate ultra-short-term load forecasting based on load characteristic decomposition and convolutional neural network with bidirectional long short-term memory …
Aiming at the continuous, periodic, and nonlinear characteristics of load changes, this paper
proposes a combined ultra-short-term load forecasting model based on improved complete …
proposes a combined ultra-short-term load forecasting model based on improved complete …
Data-driven tools for building energy consumption prediction: A review
The development of data-driven building energy consumption prediction models has gained
more attention in research due to its relevance for energy planning and conservation …
more attention in research due to its relevance for energy planning and conservation …
Short-term multivariate time series load data forecasting at low-voltage level using optimised deep-ensemble learning-based models
Increasing the renewable energy penetration, especially photovoltaic systems, requires
accurate and short-term load forecasting for every individual electricity customer. This can …
accurate and short-term load forecasting for every individual electricity customer. This can …
Experimental investigation of variational mode decomposition and deep learning for short-term multi-horizon residential electric load forecasting
With the booming growth of advanced digital technologies, it has become possible for users
as well as distributors of energy to obtain detailed and timely information about the electricity …
as well as distributors of energy to obtain detailed and timely information about the electricity …
A multi-step time-series clustering-based Seq2Seq LSTM learning for a single household electricity load forecasting
The deep learning (DL) approaches in smart grid (SG) describes the possibility of shifting
the energy industry into a modern era of reliable and sustainable energy networks. This …
the energy industry into a modern era of reliable and sustainable energy networks. This …
A review of data-driven building energy prediction
H Liu, J Liang, Y Liu, H Wu - Buildings, 2023 - mdpi.com
Building energy consumption prediction has a significant effect on energy control, design
optimization, retrofit evaluation, energy price guidance, and prevention and control of COVID …
optimization, retrofit evaluation, energy price guidance, and prevention and control of COVID …