Wind power generation: A review and a research agenda
SA Vargas, GRT Esteves, PM Maçaira… - Journal of Cleaner …, 2019 - Elsevier
The use of renewable energy resources, especially wind power, is receiving strong attention
from governments and private institutions, since it is considered one of the best and most …
from governments and private institutions, since it is considered one of the best and most …
Predicting electricity consumption for commercial and residential buildings using deep recurrent neural networks
This paper presents a recurrent neural network model to make medium-to-long term
predictions, ie time horizon of⩾ 1 week, of electricity consumption profiles in commercial and …
predictions, ie time horizon of⩾ 1 week, of electricity consumption profiles in commercial and …
Wind power forecasting: A systematic literature review
J Maldonado-Correa, JC Solano… - Wind …, 2021 - journals.sagepub.com
Accurate and reliable prediction of wind energy in the short term is of great importance for
the efficient operation of wind farms. One of the procedures to search for, summarize …
the efficient operation of wind farms. One of the procedures to search for, summarize …
Power transfer characteristics in fluctuation partition algorithm for wind speed and its application to wind power forecasting
M Yang, D Wang, C Xu, B Dai, M Ma, X Su - Renewable Energy, 2023 - Elsevier
Wind speed is the dominant meteorological factor affecting wind turbine power generation.
Existing wind speed fluctuation division algorithms only focus on the wind speed changing …
Existing wind speed fluctuation division algorithms only focus on the wind speed changing …
An investigation of wind power density distribution at location with low and high wind speeds using statistical model
V Katinas, G Gecevicius, M Marciukaitis - Applied energy, 2018 - Elsevier
The study represents wind characteristics and power density in the locations with different
wind speed conditions. The wind speed data measured at meteorological stations were …
wind speed conditions. The wind speed data measured at meteorological stations were …
[HTML][HTML] Reduced desalination carbon footprint on islands with weak electricity grids. The case of Gran Canaria
P Cabrera, JA Carta, C Matos, E Rosales-Asensio… - Applied Energy, 2024 - Elsevier
The aim of this paper is to present options to make low-carbon footprint large-scale
desalination a reality on arid islands with weak electrical grids. Through these options, the …
desalination a reality on arid islands with weak electrical grids. Through these options, the …
Comparative studies among machine learning models for performance estimation and health monitoring of thermal power plants
P Hundi, R Shahsavari - Applied Energy, 2020 - Elsevier
Estimating the performance of base load combined cycle power plants and detecting early-
stage malfunctions in equipment and processes is a difficult task that depends on complex …
stage malfunctions in equipment and processes is a difficult task that depends on complex …
Scene learning: Deep convolutional networks for wind power prediction by embedding turbines into grid space
Wind power prediction is of vital importance in wind power utilization. There have been a lot
of researches based on the time series of the wind power or speed. But in fact, these time …
of researches based on the time series of the wind power or speed. But in fact, these time …
[HTML][HTML] Optimal sizing of stand-alone wind-powered seawater reverse osmosis plants without use of massive energy storage
JA Carta, P Cabrera - Applied Energy, 2021 - Elsevier
A method, which involves genetic algorithms, is presented for the optimal sizing of a system
comprising a medium-scale modular seawater reverse osmosis desalination plant powered …
comprising a medium-scale modular seawater reverse osmosis desalination plant powered …
Design of machine learning models with domain experts for automated sensor selection for energy fault detection
Data-driven techniques that extract insights from sensor data reduce the cost of improving
system energy performance through fault detection and system health monitoring. To lower …
system energy performance through fault detection and system health monitoring. To lower …