Load forecasting techniques and their applications in smart grids
The growing success of smart grids (SGs) is driving increased interest in load forecasting
(LF) as accurate predictions of energy demand are crucial for ensuring the reliability …
(LF) as accurate predictions of energy demand are crucial for ensuring the reliability …
Taxonomy research of artificial intelligence for deterministic solar power forecasting
With the world-wide deployment of solar energy for a sustainable and renewable future, the
stochastic and volatile nature of solar power pose significant challenges to the reliable …
stochastic and volatile nature of solar power pose significant challenges to the reliable …
A review of hydrogen production processes by photocatalytic water splitting–From atomistic catalysis design to optimal reactor engineering
Abstract 'Renewable energy is an essential part of our strategy of decarbonization,
decentralization, as well as digitalization of energy.'–Isabelle Kocher. Current climate, health …
decentralization, as well as digitalization of energy.'–Isabelle Kocher. Current climate, health …
Convolutional neural networks for intra-hour solar forecasting based on sky image sequences
Accurate and timely solar forecasts play an increasingly critical role in power systems.
Compared to longer forecasting timescales, very short-term solar forecasting has lagged …
Compared to longer forecasting timescales, very short-term solar forecasting has lagged …
An interpretable probabilistic model for short-term solar power forecasting using natural gradient boosting
G Mitrentsis, H Lens - Applied Energy, 2022 - Elsevier
PV power forecasting models are predominantly based on machine learning algorithms
which do not provide any insight into or explanation about their predictions (black boxes) …
which do not provide any insight into or explanation about their predictions (black boxes) …
An effective hybrid NARX-LSTM model for point and interval PV power forecasting
This paper proposes an effective Photovoltaic (PV) Power Forecasting (PVPF) technique
based on hierarchical learning combining Nonlinear Auto-Regressive Neural Networks with …
based on hierarchical learning combining Nonlinear Auto-Regressive Neural Networks with …
Short-term forecasting of photovoltaic solar power production using variational auto-encoder driven deep learning approach
The accurate modeling and forecasting of the power output of photovoltaic (PV) systems are
critical to efficiently managing their integration in smart grids, delivery, and storage. This …
critical to efficiently managing their integration in smart grids, delivery, and storage. This …
PV-Net: An innovative deep learning approach for efficient forecasting of short-term photovoltaic energy production
M Abdel-Basset, H Hawash, RK Chakrabortty… - Journal of Cleaner …, 2021 - Elsevier
Although photovoltaic (PV) energy production offers several environmental and commercial
advantages, the irregular nature of PV energy can challenge the design and development of …
advantages, the irregular nature of PV energy can challenge the design and development of …
A deep generative model for probabilistic energy forecasting in power systems: normalizing flows
Greater direct electrification of end-use sectors with a higher share of renewables is one of
the pillars to power a carbon-neutral society by 2050. However, in contrast to conventional …
the pillars to power a carbon-neutral society by 2050. However, in contrast to conventional …
A holistic review on energy forecasting using big data and deep learning models
J Devaraj, R Madurai Elavarasan… - … journal of energy …, 2021 - Wiley Online Library
With the growth of forecasting models, energy forecasting is used for better planning,
operation, and management in the electric grid. It is important to improve the accuracy of …
operation, and management in the electric grid. It is important to improve the accuracy of …