[HTML][HTML] Comparison of machine learning methods for photovoltaic power forecasting based on numerical weather prediction
D Markovics, MJ Mayer - Renewable and Sustainable Energy Reviews, 2022 - Elsevier
The increase of the worldwide installed photovoltaic (PV) capacity and the intermittent
nature of the solar resource highlights the importance of power forecasting for the grid …
nature of the solar resource highlights the importance of power forecasting for the grid …
Predicting industrial building energy consumption with statistical and machine-learning models informed by physical system parameters
The industrial sector consumes about one-third of global energy, making them a frequent
target for energy use reduction. Variation in energy usage is observed with weather …
target for energy use reduction. Variation in energy usage is observed with weather …
Acoustic beamforming for noise source localization–Reviews, methodology and applications
This paper is a review on acoustic beamforming for noise source localization and its
applications. The main concepts of beamforming, starting from the very basics and …
applications. The main concepts of beamforming, starting from the very basics and …
Dictionary fields: Learning a neural basis decomposition
We present Dictionary Fields, a novel neural representation which decomposes a signal into
a product of factors, each represented by a classical or neural field representation, operating …
a product of factors, each represented by a classical or neural field representation, operating …
A novel multi-modality image fusion method based on image decomposition and sparse representation
Z Zhu, H Yin, Y Chai, Y Li, G Qi - Information Sciences, 2018 - Elsevier
Multi-modality image fusion is an effective technique to fuse the complementary information
from multi-modality images into an integrated image. The additional information can not only …
from multi-modality images into an integrated image. The additional information can not only …
Visual domain adaptation: A survey of recent advances
VM Patel, R Gopalan, R Li… - IEEE signal processing …, 2015 - ieeexplore.ieee.org
In pattern recognition and computer vision, one is often faced with scenarios where the
training data used to learn a model have different distribution from the data on which the …
training data used to learn a model have different distribution from the data on which the …
Deep convolutional dictionary learning for image denoising
Inspired by the great success of deep neural networks (DNNs), many unfolding methods
have been proposed to integrate traditional image modeling techniques, such as dictionary …
have been proposed to integrate traditional image modeling techniques, such as dictionary …
On single image scale-up using sparse-representations
This paper deals with the single image scale-up problem using sparse-representation
modeling. The goal is to recover an original image from its blurred and down-scaled noisy …
modeling. The goal is to recover an original image from its blurred and down-scaled noisy …
Factor fields: A unified framework for neural fields and beyond
We present Factor Fields, a novel framework for modeling and representing signals. Factor
Fields decomposes a signal into a product of factors, each represented by a classical or …
Fields decomposes a signal into a product of factors, each represented by a classical or …
Post-landing major element quantification using SuperCam laser induced breakdown spectroscopy
The SuperCam instrument on the Perseverance Mars 2020 rover uses a pulsed 1064 nm
laser to ablate targets at a distance and conduct laser induced breakdown spectroscopy …
laser to ablate targets at a distance and conduct laser induced breakdown spectroscopy …