A novel wind speed forecasting based on hybrid decomposition and online sequential outlier robust extreme learning machine
D Zhang, X Peng, K Pan, Y Liu - Energy conversion and management, 2019 - Elsevier
As the wind energy developing, wind speed prediction is important for the reliability of wind
power system and the integration of wind energy into the power network. This paper …
power system and the integration of wind energy into the power network. This paper …
A deep learning based hybrid method for hourly solar radiation forecasting
Solar radiation forecasting is a key technology to improve the control and scheduling performance
of photovoltaic power plants. In this paper, a deep learning based hybrid method for 1-…
of photovoltaic power plants. In this paper, a deep learning based hybrid method for 1-…
Multi-view neural network ensemble for short and mid-term load forecasting
Accurate load forecasting is essential to the operation and planning of power systems and
electricity markets. In this paper, an ensemble of radial basis function neural networks (…
electricity markets. In this paper, an ensemble of radial basis function neural networks (…
MultiCycleNet: multiple cycles self-boosted neural network for short-term electric household load forecasting
Household load forecasting plays an important role in future grid planning and operation.
However, compared with aggregated load forecasting, household load forecasting faces the …
However, compared with aggregated load forecasting, household load forecasting faces the …
Deep autoencoder with localized stochastic sensitivity for short-term load forecasting
This paper presents a short-term electric load forecasting model based on deep autoencoder
with localized stochastic sensitivity (D-LiSSA). D-LiSSA can learn informative hidden …
with localized stochastic sensitivity (D-LiSSA). D-LiSSA can learn informative hidden …
An unsupervised data-driven approach for behind-the-meter photovoltaic power generation disaggregation
An increasing number of behind-the-meter (BtM) rooftop photovoltaic (PV) panels is being
installed and maintained by site owners. However, invisible PV power generation (PVPG) will …
installed and maintained by site owners. However, invisible PV power generation (PVPG) will …
A novel data-driven method for behind-the-meter solar generation disaggregation with cross-iteration refinement
Photovoltaic (PV) generation is increasing in distribution systems following policies and
incentives to promote zero-carbon emission societies. Most residential PV systems are installed …
incentives to promote zero-carbon emission societies. Most residential PV systems are installed …
A stochastic sensitivity-based multi-objective optimization method for short-term wind speed interval prediction
With the increasing penetration of wind power in renewable energy systems, it is important to
improve the accuracy of wind speed prediction. However, wind power generation has great …
improve the accuracy of wind speed prediction. However, wind power generation has great …
[HTML][HTML] Photovoltaic output power estimation and baseline prediction approach for a residential distribution network with behind-the-meter systems
Considering that most of the photovoltaic (PV) data are behind-the-meter (BTM), there is a
great challenge to implement effective demand response projects and make a precise …
great challenge to implement effective demand response projects and make a precise …
Shipborne radar servo control based on neural sliding mode variable structure
H Ji, Z Li, K Pan, Z Zhang - 2018 IEEE 3rd Advanced …, 2018 - ieeexplore.ieee.org
In this paper, in view of the complexity of shipborne radar servo system, the position loop is
controlled by sliding mode variable structure. In order to overcome the oscillation in sliding …
controlled by sliding mode variable structure. In order to overcome the oscillation in sliding …