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Renewable Energy

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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...

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...

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 ...

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...

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...

Machine learning-informed and synthetic biology-enabled semi-continuous algal cultivation to unleash renewable fuel productivity.

Nature communications
Algal biofuel is regarded as one of the ultimate solutions for renewable energy, but its commercialization is hindered by growth limitations caused by mutual shading and high harvest costs. We overcome these challenges by advancing machine learning t...

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...

A review on proliferation of artificial intelligence in wind energy forecasting and instrumentation management.

Environmental science and pollution research international
Energy is the source of economic growth, and energy consumption indicates the country's state of development. Energy engineering is a relatively new technical discipline. It is increasingly considered as a significant step in meeting carbon reduction...

Investigating a Dual-Channel Network in a Sustainable Closed-Loop Supply Chain Considering Energy Sources and Consumption Tax.

Sensors (Basel, Switzerland)
This paper proposes a dual-channel network of a sustainable Closed-Loop Supply Chain (CLSC) for rice considering energy sources and consumption tax. A Mixed Integer Linear Programming (MILP) model is formulated for optimizing the total cost, the amou...

Prediction of Electric Power Production and Consumption for the CETATEA Building Using Neural Networks.

Sensors (Basel, Switzerland)
Economic and social development is hardly influenced by electric power production and consumption. In this context of the energy supply pressure, energy production and consumption must be monitored and controlled in an intelligent way. Due to the ava...