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 …

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 …

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 …

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 …

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 …

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 …

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 …

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 …

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 …

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 …