A review on renewable energy and electricity requirement forecasting models for smart grid and buildings
The benefits of renewable energy are that it is sustainable and is low in environmental
pollution. Growing load requirement, global warming, and energy crisis need energy …
pollution. Growing load requirement, global warming, and energy crisis need energy …
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 survey of machine learning models in renewable energy predictions
The use of renewable energy to reduce the effects of climate change and global warming
has become an increasing trend. In order to improve the prediction ability of renewable …
has become an increasing trend. In order to improve the prediction ability of renewable …
High-quality prediction intervals for deep learning: A distribution-free, ensembled approach
This paper considers the generation of prediction intervals (PIs) by neural networks for
quantifying uncertainty in regression tasks. It is axiomatic that high-quality PIs should be as …
quantifying uncertainty in regression tasks. It is axiomatic that high-quality PIs should be as …
Neural network-based uncertainty quantification: A survey of methodologies and applications
Uncertainty quantification plays a critical role in the process of decision making and
optimization in many fields of science and engineering. The field has gained an …
optimization in many fields of science and engineering. The field has gained an …
Hybridization of hybrid structures for time series forecasting: A review
Z Hajirahimi, M Khashei - Artificial Intelligence Review, 2023 - Springer
Achieving the desired accuracy in time series forecasting has become a binding domain,
and developing a forecasting framework with a high degree of accuracy is one of the most …
and developing a forecasting framework with a high degree of accuracy is one of the most …
Autonomous tracking using a swarm of UAVs: A constrained multi-agent reinforcement learning approach
In this paper, we aim to design an autonomous tracking system for a swarm of unmanned
aerial vehicles (UAVs) to localize a radio frequency (RF) mobile target. In the system, UAVs …
aerial vehicles (UAVs) to localize a radio frequency (RF) mobile target. In the system, UAVs …
Multi-objective prediction intervals for wind power forecast based on deep neural networks
M Zhou, B Wang, S Guo, J Watada - Information Sciences, 2021 - Elsevier
Wind power forecast is playing a significant role in the operation and dispatch of modern
power systems. Compared with traditional point forecast methods, interval forecast is able to …
power systems. Compared with traditional point forecast methods, interval forecast is able to …
Solar radiation forecasting based on convolutional neural network and ensemble learning
Nowadays, we are moving forward to more sustainable energy production systems based
on renewable sources. Among all Photovoltaic (PV) systems are spreading in our cities. In …
on renewable sources. Among all Photovoltaic (PV) systems are spreading in our cities. In …
A simple approach for short-term wind speed interval prediction based on independently recurrent neural networks and error probability distribution
Improving the quality of Wind Speed Interval prediction is important to maximize the usage of
integrated wind energy as well as to reduce the adverse effects of the uncertainties …
integrated wind energy as well as to reduce the adverse effects of the uncertainties …