State-of-the-art Technology and Development of Numerical Prediction Models and its Applications for Renewable Energy Fields

2017 ◽  
Vol 137 (7) ◽  
pp. 904-909
Author(s):  
Hideaki Ohtake
Keyword(s):  
Renewable Energy ◽  
Prediction Models ◽  
State Of The Art ◽  
Numerical Prediction

scholarly journals Progresses in Analytical Design of Distribution Grids and Energy Storage

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4270
Author(s):  
Gianpiero Colangelo ◽  
Gianluigi Spirto ◽  
Marco Milanese ◽  
Arturo de Risi
Keyword(s):  
Renewable Energy ◽  
Power Flow ◽  
State Of The Art ◽  
Renewable Energy Sources ◽  
Energy Sources ◽  
Distribution Grid ◽  
Mathematical Methods ◽  
Power Generators ◽  
Bidirectional Power Flow ◽  
Distribution Grids

In the last years, a change in the power generation paradigm has been promoted by the increasing use of renewable energy sources combined with the need to reduce CO2 emissions. Small and distributed power generators are preferred to the classical centralized and sizeable ones. Accordingly, this fact led to a new way to think and design distributions grids. One of the challenges is to handle bidirectional power flow at the distribution substations transformer from and to the national transportation grid. The aim of this paper is to review and analyze the different mathematical methods to design the architecture of a distribution grid and the state of the art of the technologies used to produce and eventually store or convert, in different energy carriers, electricity produced by renewable energy sources, coping with the aleatory of these sources.

scholarly journals A renewable energy forecasting and control approach to secured edge-level efficiency in a distributed micro-grid

Cybersecurity ◽  
2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Raphael Anaadumba ◽  
Qi Liu ◽  
Bockarie Daniel Marah ◽  
Francis Mawuli Nakoty ◽  
Xiaodong Liu ◽  
...  
Keyword(s):  
Renewable Energy ◽  
Mobile Devices ◽  
Prediction Models ◽  
Renewable Energy Sources ◽  
Electrical Network ◽  
Effective Control ◽  
Energy Sources ◽  
Forecasting Model ◽  
Communication Overhead ◽  
Energy Forecasting

AbstractEnergy forecasting using Renewable energy sources (RESs) is gradually gaining weight in the research field due to the benefits it presents to the modern-day environment. Not only does energy forecasting using renewable energy sources help mitigate the greenhouse effect, it also helps to conserve energy for future use. Over the years, several methods for energy forecasting have been proposed, all of which were more concerned with the accuracy of the prediction models with little or no considerations to the operating environment. This research, however, proposes the uses of Deep Neural Network (DNN) for energy forecasting on mobile devices at the edge of the network. This ensures low latency and communication overhead for all energy forecasting operations since they are carried out at the network periphery. Nevertheless, the cloud would be used as a support for the mobile devices by providing permanent storage for the locally generated data and a platform for offloading resource-intensive computations that exceed the capabilities of the local mobile devices as well as security for them. Electrical network topology was proposed which allows seamless incorporation of multiple RESs into the distributed renewable energy source (D-RES) network. Moreover, a novel grid control algorithm that uses the forecasting model to administer a well-coordinated and effective control for renewable energy sources (RESs) in the electrical network is designed. The electrical network was simulated with two RESs and a DNN model was used to create a forecasting model for the simulated network. The model was trained using a dataset from a solar power generation company in Belgium (elis) and was experimented with a different number of layers to determine the optimum architecture for performing the forecasting operations. The performance of each architecture was evaluated using the mean square error (MSE) and the r-square.

scholarly journals Demand Prediction with Machine Learning Models; State of the Art and a Systematic Review of Advances

2019 ◽  
Author(s):  
Amir Mosavi ◽  
Sina Faizollahzadeh Ardabili ◽  
Shahabodin Shamshirband
Keyword(s):  
Machine Learning ◽  
Energy Production ◽  
Production Management ◽  
Prediction Models ◽  
State Of The Art ◽  
Electricity Demand ◽  
Original Research ◽  
Review Of The Literature ◽  
Future Prospects ◽  
Demand Prediction

Electricity demand prediction is vital for energy production management and proper exploitation of the present resources. Recently, several novel machine learning (ML) models have been employed for electricity demand prediction to estimate the future prospects of the energy requirements. The main objective of this study is to review the various ML models applied for electricity demand prediction. Through a novel search and taxonomy, the most relevant original research articles in the field are identified and further classified according to the ML modeling technique, perdition type, and the application area. A comprehensive review of the literature identifies the major ML models, their applications and a discussion on the evaluation of their performance. This paper further makes a discussion on the trend and the performance of the ML models. As the result, this research reports an outstanding rise in the accuracy, robustness, precision and the generalization ability of the prediction models using the hybrid and ensemble ML algorithms.

scholarly journals DeepPurpose: a deep learning library for drug–target interaction prediction

2020 ◽  
Author(s):  
Kexin Huang ◽  
Tianfan Fu ◽  
Lucas M Glass ◽  
Marinka Zitnik ◽  
Cao Xiao ◽  
...  
Keyword(s):  
Deep Learning ◽  
Drug Target ◽  
Prediction Models ◽  
State Of The Art ◽  
Supplementary Information ◽  
Target Interaction ◽  
Interaction Prediction ◽  
Computer Scientists ◽  
Benchmark Datasets ◽  
Biomedical Field

Abstract Summary Accurate prediction of drug–target interactions (DTI) is crucial for drug discovery. Recently, deep learning (DL) models for show promising performance for DTI prediction. However, these models can be difficult to use for both computer scientists entering the biomedical field and bioinformaticians with limited DL experience. We present DeepPurpose, a comprehensive and easy-to-use DL library for DTI prediction. DeepPurpose supports training of customized DTI prediction models by implementing 15 compound and protein encoders and over 50 neural architectures, along with providing many other useful features. We demonstrate state-of-the-art performance of DeepPurpose on several benchmark datasets. Availability and implementation https://github.com/kexinhuang12345/DeepPurpose. Contact [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals The Potential Role for Host Genetic Profiling in Screening for Chlamydia-Associated Tubal Factor Infertility (TFI)—New Perspectives

Genes ◽  
2019 ◽  
Vol 10 (6) ◽  
pp. 410
Author(s):  
Jelena Malogajski ◽  
Ivan Branković ◽  
Jolande A. Land ◽  
Pierre P. M. Thomas ◽  
Servaas A. Morré ◽  
...  
Keyword(s):  
Prediction Models ◽  
State Of The Art ◽  
Late Complications ◽  
The State ◽  
High Income ◽  
Genetic Profiling ◽  
Diagnostic Strategies ◽  
Urogenital Infections ◽  
Tubal Factor Infertility ◽  
Tubal Factor

Host immunogenetic factors can affect late complications of urogenital infections with Chlamydia trachomatis. These findings are creating new avenues for updating existing risk prediction models for C. trachomatis-associated tubal factor infertility (TFI). Research into host factors and its utilization may therefore have future implications for diagnosing C. trachomatis-induced infertility. We outline the epidemiological situation regarding C. trachomatis and TFI in high-income countries. Thereupon, we review the main characteristics of the population undergoing fertility work-up and identify screening and diagnostic strategies for TFI currently in place. The Netherlands is an exemplary model for the state of the art in high-income countries. Within the framework of existing clinical approaches, we propose a scenario for the translation of relevant genome-based information into triage of infertile women, with the objective of implementing genetic profiling in the routine investigation of TFI. Furthermore, we describe the state of the art in relevant gene- and single nucleotide polymorphism (SNP) based clinical prediction models and place our perspectives in the context of these applications. We conclude that the introduction of a genetic test of proven validity into the assessment of TFI should help reduce patient burden from invasive and costly examinations by achieving a more precise risk stratification.

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