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Journal ArticleDOI

Very short-term temperature forecaster using MLP and N-nearest stations for calculating key control parameters in solar photovoltaic generation

TLDR
In this article, the authors developed a very short-term temperature forecaster that makes photovoltaic generation more reliable in order to provide not only power but also ancillary services.
About
This article is published in Sustainable Energy Technologies and Assessments.The article was published on 2021-06-01. It has received 19 citations till now. The article focuses on the topics: Photovoltaic system & Electrical grid.

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Citations
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Journal ArticleDOI

Microgrid Digital Twins: Concepts, Applications, and Future Trends

TL;DR: The goal is to explore different applications of DTs in MGs, namely in design, control, operator training, forecasting, fault diagnosis, expansion planning, and policy-making, and future trends in MGDTs are discussed.
Journal ArticleDOI

Microgrid Digital Twins: Concepts, Applications, and Future Trends

- 01 Jan 2022 - 
TL;DR: In this paper , the concept of the digital twin (DT) and its key characteristics are introduced, a workflow for establishing MGDTs is presented, and an up-to-date overview of studies that applied the DT concept to power systems and specifically MGs is provided.
Journal ArticleDOI

Using deep learning and meteorological parameters to forecast the photovoltaic generators intra-hour output power interval for smart grid control

TL;DR: A new model for predicting photovoltaic generators' output power confidence interval 10 min ahead is developed, based on deep learning, mathematical probability density functions and meteorological parameters, which has been validated with a real data series collected from Spanish meteorological stations.
Journal ArticleDOI

Forecasting intra-hour solar photovoltaic energy by assembling wavelet based time-frequency analysis with deep learning neural networks

TL;DR: In this article , a wavelet based time-frequency analysis of the used data with deep learning neural networks to forecast solar irradiation, in next 10 min, to compute solar photovoltaic generation.
References
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Book

Deep Learning

TL;DR: Deep learning as mentioned in this paper is a form of machine learning that enables computers to learn from experience and understand the world in terms of a hierarchy of concepts, and it is used in many applications such as natural language processing, speech recognition, computer vision, online recommendation systems, bioinformatics, and videogames.
Book

Neural Networks And Learning Machines

Simon Haykin
TL;DR: Refocused, revised and renamed to reflect the duality of neural networks and learning machines, this edition recognizes that the subject matter is richer when these topics are studied together.
Journal ArticleDOI

A 24-h forecast of solar irradiance using artificial neural network: Application for performance prediction of a grid-connected PV plant at Trieste, Italy

TL;DR: In this paper, a multilayer perceptron (MLP) model was proposed to forecast the solar irradiance on a base of 24h using the present values of the mean daily solar irradiances and air temperature.
Journal ArticleDOI

Hourly day-ahead solar irradiance prediction using weather forecasts by LSTM

TL;DR: A novel solar prediction scheme for hourly day-ahead solar irradiance prediction by using the weather forecasting data is proposed and it is demonstrated that the proposed algorithm outperforms these competitive algorithms for single output prediction.
Journal ArticleDOI

Calculation of the polycrystalline PV module temperature using a simple method of energy balance

TL;DR: In this paper, the performance of a photovoltaic module is studied versus environmental variables such as solar irradiance, ambient temperature and wind speed, and two types of simplified models are studied.
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