Predicting Solar Irradiance Using Time Series Neural Networks
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TLDR
A Nonlinear Autoregressive Network with Exogenous Inputs (NARX) approach was applied to the Vichy-Rolla National Airport's photovoltaic station and results show that the NARX neural network notably outperformed the other models and is better than the linear regression model.About:
This article is published in Procedia Computer Science.The article was published on 2014-01-01 and is currently open access. It has received 61 citations till now. The article focuses on the topics: Nonlinear autoregressive exogenous model & Solar irradiance.read more
Citations
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Journal ArticleDOI
Solar Irradiance Forecasting Using Deep Neural Networks
TL;DR: This paper presents a method to predict the solar irradiance using deep recurrent neural networks (DRNNs), which can outperform all other methods, as the performance tests indicate.
Journal ArticleDOI
Prediction of hourly solar radiation in Abu Musa Island using machine learning algorithms
TL;DR: The results demonstrated that for the N1, SVR and MLFFNN models have the maximum performance to predict the solar irradiance with R = 0.9999 and 0.9795, respectively.
Proceedings ArticleDOI
Forecasting of photovoltaic power using extreme learning machine
TL;DR: Simulation results show that, the proposed neural network model forecasts the photovoltaic power with high accuracy.
Journal ArticleDOI
Photovoltaic yield prediction using an irradiance forecast model based on multiple neural networks
TL;DR: In this paper, a PV yield prediction system is presented based on an irradiance forecast model and a PV model using multiple feed-forward neural networks, which has a mean absolute percentage error of 3.4% on sunny day and 23% on a cloudy day for Stuttgart.
Journal ArticleDOI
A comparison study based on artificial neural network for assessing PV/T solar energy production
TL;DR: In this paper, a comparison study of PV/T energy data prediction systems using different ANNs techniques using different evaluation factors such as MSE, MAPE, R2, RSME, MBE, and MPE is presented.
References
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Comprehensive Approach to Modeling and Simulation of Photovoltaic Arrays
TL;DR: In this article, the authors proposed a method of modeling and simulation of photovoltaic arrays by adjusting the curve at three points: open circuit, maximum power, and short circuit.
Book
Phoneme recognition using time-delay neural networks
TL;DR: The authors present a time-delay neural network (TDNN) approach to phoneme recognition which is characterized by two important properties: using a three-layer arrangement of simple computing units, a hierarchy can be constructed that allows for the formation of arbitrary nonlinear decision surfaces, which the TDNN learns automatically using error backpropagation.
Journal ArticleDOI
Phoneme recognition using time-delay neural networks
TL;DR: In this article, the authors presented a time-delay neural network (TDNN) approach to phoneme recognition, which is characterized by two important properties: (1) using a three-layer arrangement of simple computing units, a hierarchy can be constructed that allows for the formation of arbitrary nonlinear decision surfaces, which the TDNN learns automatically using error backpropagation; and (2) the time delay arrangement enables the network to discover acoustic-phonetic features and the temporal relationships between them independently of position in time and therefore not blurred by temporal shifts in the input
Proceedings ArticleDOI
24-hour-ahead forecasting of energy production in solar PV systems
TL;DR: A one day-ahead forecasting model based on an artificial neural network with tapped delay lines is developed, using time series analysis and neural networks to predict energy production in solar photovoltaic (PV) installations.
Proceedings ArticleDOI
Cloud motion estimation for short term solar irradiation prediction
TL;DR: A solar prediction system that can detect cloud movements from the TSI (total sky imager) images, and then estimate the future cloud position over solar panels and subsequent solar irradiance fluctuations incurred by cloud transients is proposed.