66 citations · 140 across the 14 of their papers we have counts for
14 papers
Hybrid Neuro-Evolutionary Method for Predicting Wind Turbine Power Output
Mehdi Neshat, Meysam Majidi Nezhad, Ehsan Abbasnejad +6
Reliable wind turbine power prediction is imperative to the planning, scheduling and control of wind energy farms for stable power production. In recent years Machine Learning (ML)…
Optimisation of Large Wave Farms using a Multi-strategy Evolutionary Framework
Mehdi Neshat, Bradley Alexander, Nataliia Y. Sergiienko +1
Wave energy is a fast-developing and promising renewable energy resource. The primary goal of this research is to maximise the total harnessed power of a large wave farm consisting…
Evolutionary Image Transition and Painting Using Random Walks
Aneta Neumann, Bradley Alexander, Frank Neumann
We present a study demonstrating how random walk algorithms can be used for evolutionary image transition. We design different mutation operators based on uniform and biased random…
An Evolutionary Deep Learning Method for Short-term Wind Speed Prediction: A Case Study of the Lillgrund Offshore Wind Farm
Mehdi Neshat, Meysam Majidi Nezhad, Ehsan Abbasnejad +4
Accurate short-term wind speed forecasting is essential for large-scale integration of wind power generation. However, the seasonal and stochastic characteristics of wind speed mak…
Design optimisation of a multi-mode wave energy converter
Nataliia Y. Sergiienko, Mehdi Neshat, Leandro S. P. da Silva +2
A wave energy converter (WEC) similar to the CETO system developed by Carnegie Clean Energy is considered for design optimisation. This WEC is able to absorb power from heave, surg…
A Hybrid Cooperative Co-evolution Algorithm Framework for Optimising Power Take Off and Placements of Wave Energy Converters
Mehdi Neshat, Bradley Alexander, Markus Wagner
Wave energy technologies have the potential to play a significant role in the supply of renewable energy on a world scale. One of the most promising designs for wave energy convert…