scholarly journals Synthesis, structural and electrochemical properties of sodium nickel phosphate for energy storage devices

Nanoscale ◽  
2016 ◽  
Vol 8 (21) ◽  
pp. 11291-11305 ◽  
Author(s):  
Manickam Minakshi ◽  
David Mitchell ◽  
Rob Jones ◽  
Feraih Alenazey ◽  
Teeraphat Watcharatharapong ◽  
...  
Keyword(s):  
Renewable Energy ◽  
Energy Production ◽  
Large Scale ◽  
Low Cost ◽  
Energy Systems ◽  
Energy Storage Devices ◽  
Renewable Energy Systems ◽  
Storage Devices ◽  
Nickel Phosphate ◽  
And Storage

Electrochemical energy production and storage at large scale and low cost, is a critical bottleneck in renewable energy systems.

scholarly journals Is Investing in Companies Manufacturing Solar Components a Lucrative Business? A Decision Tree Based Analysis

Energies ◽  
2020 ◽  
Vol 13 (2) ◽  
pp. 499
Author(s):  
Sebastian Klaudiusz Tomczak ◽  
Anna Skowrońska-Szmer ◽  
Jan Jakub Szczygielski
Keyword(s):  
Renewable Energy ◽  
Energy Production ◽  
Manufacturing Sector ◽  
Energy Systems ◽  
Product Portfolio ◽  
New Approach ◽  
Renewable Energy Systems ◽  
Pv Systems ◽  
Depth Analysis ◽  
A Company

In an era of increasing energy production from renewable sources, the demand for components for renewable energy systems has dramatically increased. Consequently, managers and investors are interested in knowing whether a company associated with the semiconductor and related device manufacturing sector, especially the photovoltaic (PV) systems manufacturers, is a money-making business. We apply a new approach that extends prior research by applying decision trees (DTs) to identify ratios (i.e., indicators), which discriminate between companies within the sector that do (designated as “green”) and do not (“red”) produce elements of PV systems. Our results indicate that on the basis of selected ratios, green companies can be distinguished from the red companies without an in-depth analysis of the product portfolio. We also find that green companies, especially operating in China are characterized by lower financial performance, thus providing a negative (and unexpected) answer to the question posed in the title.

scholarly journals Solar Power System Planning and Design

2020 ◽  
Vol 10 (1) ◽  
pp. 367
Author(s):  
Yosoon Choi
Keyword(s):  
Renewable Energy ◽  
Power System ◽  
Greenhouse Gas ◽  
Energy Production ◽  
Energy Systems ◽  
Environmental Safety ◽  
System Planning ◽  
Renewable Energy Systems ◽  
Planning And Design ◽  
Production Techniques

With growing concerns about greenhouse gas emissions, the security of conventional energy supplies, and the environmental safety of conventional energy production techniques, renewable energy systems are becoming increasingly important and are receiving much political attention [...]

Spatio-temporal ensemble predictions for wind and solar energy combining dispersion modelling methods and machine learning

2021 ◽  
Author(s):  
Irene Schicker ◽  
Petrina Papazek ◽  
Elisa Perrone ◽  
Delia Arnold
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Renewable Energy ◽  
Random Forest ◽  
Energy Production ◽  
Energy Systems ◽  
Processing Algorithm ◽  
Post Processing ◽  
Processing Methods ◽  
Renewable Energy Systems

<p>With the increasing usage of renewable energy systems to meet the climate agreement aims accurate predictions of the possible amount of energy production stemming from renewable energy systems are needed. The need for such predictions and their uncertainty is manifold: to estimate the load on the power grid, to take measures in case of too much/not enough renewable energy with reduced nuclear energy availability, rescheduling/adjusting of energy production,  maintenance, trading, and more. Furthermore, TSOs and energy providers need the information as finegrained, spatially and temporarily, as possible, on third level hub or even on solar farm / wind turbine level for a comparatively large area.</p><p>These needs pose a challenge to numerical weather prediction (NWP) post-processing methods. Typically, one uses selected NWP fields aswell as observations, if available, as input in post-processing methods. Here, we combine two post-processing methods namely a neural network and random forest approach with the Flex_extract algorithm. Flex_extract is the pre-processing algorithm for the langrangian particle dispersion model FLEXPART and the trajectory model FLEXTRA. Flex_extract uses the three-dimensional wind fields of the NWP model and calculates additionally the instantaneous surfaces fluxes. Thus, coupling Flex_extract with a machine learning post-processing algorithm enables the usage of native NWP fields with a higher vertical accuracy than pressure levels. To generate an ensmeble in post-processing from deterministic sources different tools are available. Here, we will apply the Schaake Shuffle. </p><p>In this study a neural network and random forest approach for probabilistic forecasting with a high horizontal grid resolution (1 km ) as well as a high temporal forecasting frequency of wind speed and global horizontal irradiance for Austria will be presented. Evaluation will be carried out against gridded analysis fields and observations.</p>

Application of LiMn2O4 Nanostructures as Efficient Cathodes for Energy Storage Devices

2021 ◽  
pp. 204-228
Author(s):  
Shabir Ahmad Akhoon ◽  
Ashaq Hussain Sofi ◽  
Rayees Ahmad Khan ◽  
Ab. Mateen Tantray ◽  
Seemin Rubab
Keyword(s):  
Renewable Energy ◽  
Energy Storage ◽  
Large Scale ◽  
Fossil Fuels ◽  
Lithium Ion ◽  
Energy Utilization ◽  
Renewable Energy Resources ◽  
Energy Storage Devices ◽  
Storage Devices ◽  
End Use

Renewable energy resources have been investigated as alternatives to fossil fuels. Though the energy density of these renewable sources is not comparable to the fossil fuels, their abundance make them highly interesting. There are three main steps in the renewable energy utilization: harvesting, conversion, and storage. Thus, after harvesting renewable energy, storing this energy is an important aspect for its large-scale end use. Considering the fact that the energy is a basic need for life on earth, there has been a strong scientific temperament towards the renewable energy utilization. The electrical energy storage maintains the key to promote the use of renewable energy. Among the storage devices, the rechargeable lithium ion batteries (LIBs) are the most promising energy storage devices. Among various cathodes proposed for LIBs, the most promising one is the spinel lithium manganese oxide (LiMn2O4). Its non-toxicity, low cost, abundance, and ease of synthesis, besides being environmentally friendly, make it suitable for next generation green LIBs.

Incident detection and classification in renewable energy news using pre-trained language models on deep neural networks

2021 ◽  
pp. 1-20
Author(s):  
Qiqing Wang ◽  
Cunbin Li
Keyword(s):  
Neural Networks ◽  
Renewable Energy ◽  
Large Scale ◽  
Deep Neural Networks ◽  
Energy Systems ◽  
Language Models ◽  
Causal Effects ◽  
Incident Detection ◽  
Renewable Energy Systems ◽  
Learning Framework

The surge of renewable energy systems can lead to increasing incidents that negatively impact economics and society, rendering incident detection paramount to understand the mechanism and range of those impacts. In this paper, a deep learning framework is proposed to detect renewable energy incidents from news articles containing accidents in various renewable energy systems. The pre-trained language models like Bidirectional Encoder Representations from Transformers (BERT) and word2vec are utilized to represent textual inputs, which are trained by the Text Convolutional Neural Networks (TCNNs) and Text Recurrent Neural Networks. Two types of classifiers for incident detection are trained and tested in this paper, one is a binary classifier for detecting the existence of an incident, the other is a multi-label classifier for identifying different incident attributes such as causal-effects and consequences, etc. The proposed incident detection framework is implemented on a hand-annotated dataset with 5 190 records. The results show that the proposed framework performs well on both the incident existence detection task (F1-score 91.4%) and the incident attributes identification task (micro F1-score 81.7%). It is also shown that the BERT-based TCNNs are effective and robust in detecting renewable energy incidents from large-scale textual materials.

Solar driven renewable energy storage using rhenium disulfide nanostructure based rechargeable supercapacitors

2020 ◽  
Vol 4 (11) ◽  
pp. 3290-3301
Author(s):  
Parthiban Pazhamalai ◽  
Karthikeyan Krishnamoorthy ◽  
Vimal Kumar Mariappan ◽  
Arunprasath Sathyaseelan ◽  
Sang-Jae Kim
Keyword(s):  
Renewable Energy ◽  
Energy Storage ◽  
Electronic Devices ◽  
Energy Systems ◽  
Power Electronic ◽  
Two Dimensional ◽  
Energy Storage Devices ◽  
Storage Devices ◽  
Long Time ◽  
Renewable Energy Storage

Two-dimensional ReS2 nanostructures as an electrode for energy storage devices can be charged using solar cells which can efficiently power electronic devices for a long time, improving its effectiveness for the development of backup energy systems.

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