AIMC Topic: Renewable Energy

Clear Filters Showing 21 to 26 of 26 articles

Neural network-based adaptive global sliding mode MPPT controller design for stand-alone photovoltaic systems.

PloS one
The increasing energy demand and the target to reduce environmental pollution make it essential to use efficient and environment-friendly renewable energy systems. One of these systems is the Photovoltaic (PV) system which generates energy subject to...

Artificial neural network-based output power prediction of grid-connected semitransparent photovoltaic system.

Environmental science and pollution research international
The solar photovoltaic system is an emerging renewable energy resource. The performance of the solar photovoltaic system is predicted based on the historical experimental dataset. In this work, the real-time prediction models are developed for the ou...

Perceptions of GHG emissions and renewable energy sources in Europe, Australia and the USA.

Environmental science and pollution research international
People's sentiments and perceptions of greenhouse gas emission and renewable energy are important information to understand their reaction to the planned mitigation policy. Therefore, this research analyzes people's perceptions of greenhouse gas emis...

Prediction of power generation and rotor angular speed of a small wind turbine equipped to a controllable duct using artificial neural network and multiple linear regression.

Environmental research
Wind power is one of the most popular sources of renewable energies with an ideal extractable value that is limited to 0.593 known as the Betz-Joukowsky limit. As the generated power of wind machines is proportional to cubic wind speed, therefore it ...

Metacognitive Octonion-Valued Neural Networks as They Relate to Time Series Analysis.

IEEE transactions on neural networks and learning systems
In this paper, a metacognitive octonion-valued neural network (Mc-OVNN) learning algorithm and its application to diverse time series prediction are presented. The Mc-OVNN is comprised of two components: the octonion-valued neural network that repres...

Application of artificial neural networks to co-combustion of hazelnut husk-lignite coal blends.

Bioresource technology
The artificial neural network (ANN) theory is applied to thermal data obtained by non-isothermal thermogravimetric analysis (TGA) from room temperature to 1000°C at different heating rates in air to study co-combustion of hazelnut husk (HH)-lignite c...