Deep belief network based deterministic and probabilistic wind speed forecasting approach
With the rapid growth of wind power penetration into modern power grids, wind speed
forecasting (WSF) plays an increasingly significant role in the planning and operation of …
forecasting (WSF) plays an increasingly significant role in the planning and operation of …
Improving air quality prediction accuracy at larger temporal resolutions using deep learning and transfer learning techniques
As air pollution becomes more and more severe, air quality prediction has become an
important approach for air pollution management and prevention. In recent years, a number …
important approach for air pollution management and prevention. In recent years, a number …
A nonlinear hybrid wind speed forecasting model using LSTM network, hysteretic ELM and Differential Evolution algorithm
YL Hu, L Chen - Energy conversion and management, 2018 - Elsevier
Accurate and stable wind speed forecasting is essential for the planning, scheduling and
control of wind energy generation and conversion in wind power industry. In this paper, a …
control of wind energy generation and conversion in wind power industry. In this paper, a …
Deep belief network based k-means cluster approach for short-term wind power forecasting
K Wang, X Qi, H Liu, J Song - Energy, 2018 - Elsevier
Wind energy is the intermittent energy and its output has great volatility. How to accurately
predict wind power output is a problem that many researchers have been paying attention to …
predict wind power output is a problem that many researchers have been paying attention to …
Deterministic and probabilistic forecasting of photovoltaic power based on deep convolutional neural network
The penetration of photovoltaic (PV) energy into modern electric power and energy systems
has been gradually increased in recent years due to its benefits of being abundant …
has been gradually increased in recent years due to its benefits of being abundant …
[HTML][HTML] Improved daily SMAP satellite soil moisture prediction over China using deep learning model with transfer learning
The skillful soil moisture (SM) for the Soil Moisture Active Passive (SMAP) L4 product can
provide substantial value for many practical applications including ecosystem management …
provide substantial value for many practical applications including ecosystem management …
Wind power prediction using deep neural network based meta regression and transfer learning
An innovative short term wind power prediction system is proposed which exploits the
learning ability of deep neural network based ensemble technique and the concept of …
learning ability of deep neural network based ensemble technique and the concept of …
A new generation of AI: A review and perspective on machine learning technologies applied to smart energy and electric power systems
L Cheng, T Yu - International Journal of Energy Research, 2019 - Wiley Online Library
The new generation of artificial intelligence (AI), called AI 2.0, has recently become a
research focus. Data‐driven AI 2.0 will accelerate the development of smart energy and …
research focus. Data‐driven AI 2.0 will accelerate the development of smart energy and …
Deterministic wind energy forecasting: A review of intelligent predictors and auxiliary methods
Recent developments in renewable energy have highlighted the need for rational use of
wind energy. Accurate prediction of wind speed and wind power is recognized as an …
wind energy. Accurate prediction of wind speed and wind power is recognized as an …
A two-layer nonlinear combination method for short-term wind speed prediction based on ELM, ENN, and LSTM
As a typical kind of the Internet of Things, smart grid has attracted a lot of attentions. The
power energy management of smart grid is of great importance for energy distribution …
power energy management of smart grid is of great importance for energy distribution …