Prediction bands for solar energy: New short-term time series forecasting techniques

[1]  R. Geary,et al.  Testing for Normality , 2003 .

[2]  A W Gillies,et al.  Einfuhrung In Theorie Und Anwendung Der Laplace-Transformation , 1959 .

[3]  C. Lanczos Applied Analysis , 1961 .

[4]  Edward Nelson Internal set theory: A new approach to nonstandard analysis , 1977 .

[5]  J. Harthong Le moiré , 1981 .

[6]  R. Engle Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation , 1982 .

[7]  T. Bollerslev,et al.  Generalized autoregressive conditional heteroskedasticity , 1986 .

[8]  Anil K. Bera,et al.  A Test for Normality of Observations and , 1987 .

[9]  Anil K. Bera,et al.  A test for normality of observations and regression residuals , 1987 .

[10]  F Diener,et al.  Analyse non standard , 1989 .

[11]  E. Ziegel Introduction to the Theory and Practice of Econometrics , 1989 .

[12]  Guy Melard,et al.  Méthodes de prévision à court terme , 1990 .

[13]  David Williams,et al.  Probability with Martingales , 1991, Cambridge mathematical textbooks.

[14]  Richard A. Davis,et al.  Time Series: Theory and Methods , 2013 .

[15]  P. Cartier,et al.  Integration over finite sets , 1995 .

[16]  John Boland,et al.  Time-series analysis of climatic variables , 1995 .

[17]  J. Bollinger Bollinger on Bollinger Bands , 2001 .

[18]  M. Fliess,et al.  An algebraic framework for linear identification , 2003 .

[19]  W. Härdle Nonparametric and Semiparametric Models , 2004 .

[20]  R. Kuhlemann,et al.  Rethinking satellite-based solar irradiance modelling: The SOLIS clear-sky module , 2004 .

[21]  B. A. Shenoi,et al.  Introduction to Digital Signal Processing and Filter Design , 2005 .

[22]  C. Willmott,et al.  Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance , 2005 .

[23]  L. Wasserman All of Nonparametric Statistics , 2005 .

[24]  P. Campbell The (mis)Behavior of Markets: A Fractal View of Risk, Ruin, and Reward/Fractals and Scaling in Finance: Discontinuity, Concentration, Risk/Yale Alumni Magazine , 2005 .

[25]  Michel Fliess,et al.  Analyse non standard du bruit , 2006, ArXiv.

[26]  Rob J. Hyndman,et al.  Another Look at Forecast Accuracy Metrics for Intermittent Demand , 2006 .

[27]  C. Kirkpatrick,et al.  Technical Analysis: The Complete Resource for Financial Market Technicians , 2006 .

[28]  Daniel G. Goldstein,et al.  We Don't Quite Know What We Are Talking About , 2007 .

[29]  Hebertt Sira-Ramírez,et al.  Closed-loop parametric identification for continuous-time linear systems via new algebraic techniques , 2007 .

[30]  Tewfik Sari,et al.  Non-standard analysis and representation of reality , 2008, Int. J. Control.

[31]  Cédric Join,et al.  Numerical differentiation with annihilators in noisy environment , 2009, Numerical Algorithms.

[32]  John Boland,et al.  Time Series Modelling of Solar Radiation , 2008 .

[33]  Cédric Join,et al.  Non-linear estimation is easy , 2007, Int. J. Model. Identif. Control..

[34]  Peter Lynch,et al.  The origins of computer weather prediction and climate modeling , 2008, J. Comput. Phys..

[35]  P. Ineichen A broadband simplified version of the Solis clear sky model , 2008 .

[36]  C. Lobry La méthode des élucidations successives , 2008 .

[37]  Henrik Madsen,et al.  Online short-term solar power forecasting , 2009 .

[38]  Gordon Reikard Predicting solar radiation at high resolutions: A comparison of time series forecasts , 2009 .

[39]  Cédric Join,et al.  A Mathematical Proof of the Existence of Trends in Financial Time Series , 2009, ArXiv.

[40]  A. Ghanbarzadeh,et al.  The potential of different artificial neural network (ANN) techniques in daily global solar radiation modeling based on meteorological data , 2010 .

[41]  L. Zarzalejo,et al.  Prediction of global solar irradiance based on time series analysis: Application to solar thermal power plants energy production planning , 2010 .

[42]  Cyril Voyant,et al.  Forecasting of preprocessed daily solar radiation time series using neural networks , 2010 .

[43]  Michel Fliess,et al.  Une "commande sans modèle" pour aménagements hydroélectriques en cascade , 2013 .

[44]  N. Vakil Real Analysis through Modern Infinitesimals: Internal set theory , 2011 .

[45]  C. K. Chan,et al.  Prediction of hourly solar radiation using a novel hybrid model of ARMA and TDNN , 2011 .

[46]  Cyril Voyant,et al.  Optimization of an artificial neural network dedicated to the multivariate forecasting of daily glob , 2011 .

[47]  Claude E. Duchon,et al.  Time Series Analysis in Meteorology and Climatology: An Introduction , 2011 .

[48]  Nelson A. Kelly,et al.  Increasing the solar photovoltaic energy capture on sunny and cloudy days , 2011 .

[49]  Cédric Join,et al.  Model-free control , 2013, Int. J. Control.

[50]  R. Inman,et al.  Solar forecasting methods for renewable energy integration , 2013 .

[51]  M. Diagne,et al.  Review of solar irradiance forecasting methods and a proposition for small-scale insular grids , 2013 .

[52]  Robin Willink,et al.  Measurement Uncertainty and Probability: References , 2013 .

[53]  Cyril Voyant,et al.  Multi-horizon solar radiation forecasting for Mediterranean locations using time series models , 2013, ArXiv.

[54]  Frederik Herzberg,et al.  Radically Elementary Probability Theory , 2013 .

[55]  T. Chai,et al.  Root mean square error (RMSE) or mean absolute error (MAE)? – Arguments against avoiding RMSE in the literature , 2014 .

[56]  Carlos García Rodríguez,et al.  Algebraic Identification and Estimation Methods in Feedback Control Systems , 2014 .

[57]  Cédric Join,et al.  Short-term solar irradiance and irradiation forecasts via different time series techniques: A preliminary study , 2014, 3rd International Symposium on Environmental Friendly Energies and Applications (EFEA).

[58]  K. Uma Rao,et al.  Development of statistical time series models for solar power prediction , 2015 .

[59]  Jean-François Balmat,et al.  A model-free control strategy for an experimental greenhouse with an application to fault accommodation , 2014, Comput. Electron. Agric..

[60]  Cédric Join,et al.  TOWARDS A NEW VIEWPOINT ON CAUSALITY FOR TIME SERIES , 2015 .

[61]  Nikos Kourentzes,et al.  Short-term solar irradiation forecasting based on Dynamic Harmonic Regression , 2015 .

[62]  Irena Koprinska,et al.  2D-interval forecasts for solar power production , 2015 .

[63]  Cyril Voyant,et al.  Statistical parameters as a means to a priori assess the accuracy of solar forecasting models , 2015 .

[64]  Bri-Mathias Hodge,et al.  A suite of metrics for assessing the performance of solar power forecasting , 2015 .

[65]  Cédric Join,et al.  On meteorological forecasts for energy management and large historical data: A first look , 2015 .

[66]  Ali Assi,et al.  Enhancing the performance of heaving wave energy converters using model-free control approach , 2015 .

[67]  Martial Haeffelin,et al.  Reliability of day-ahead solar irradiance forecasts on Reunion Island depending on synoptic wind and humidity conditions , 2015 .

[68]  T. Soubdhan,et al.  A benchmarking of machine learning techniques for solar radiation forecasting in an insular context , 2015 .

[69]  John Boland,et al.  Spatial-temporal forecasting of solar radiation , 2015 .

[70]  Lu Zhao,et al.  Forecasting of global horizontal irradiance by exponential smoothing, using decompositions , 2015 .

[71]  Ralf Mikut,et al.  Information and communication technology in energy lab 2.0: Smart energies system simulation and control center with an open-street-map-based power flow simulation example , 2016 .

[72]  R. Urraca,et al.  Review of photovoltaic power forecasting , 2016 .

[73]  Philippe Lauret,et al.  Probabilistic forecasting of the solar irradiance with recursive ARMA and GARCH models , 2016 .

[74]  John Boland,et al.  Nonparametric short-term probabilistic forecasting for solar radiation , 2016 .

[75]  Irena Koprinska,et al.  Neural Network Ensemble Based Approach for 2D-Interval Prediction of Solar Photovoltaic Power , 2016 .

[76]  Reward Book The Misbehavior Of Markets A Fractal View Of Risk Ruin And Reward , 2016 .

[77]  Cédric Join,et al.  Solar energy production: Short-term forecasting and risk management , 2016, ArXiv.

[78]  Rob J Hyndman,et al.  Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond , 2016 .

[79]  Luigi Fortuna,et al.  Nonlinear Modeling of Solar Radiation and Wind Speed Time Series , 2016 .

[80]  Markus Reischl,et al.  Photovoltaic power forecasting using simple data-driven models without weather data , 2017, Computer Science - Research and Development.

[81]  Juan R. Trapero,et al.  Calculation of solar irradiation prediction intervals combining volatility and kernel density estimates , 2016 .

[82]  M. Paolone,et al.  Ultra-short-term prediction intervals of photovoltaic AC active power , 2016, 2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS).

[83]  J. A. Somolinos,et al.  Online signal filtering based on the algebraic method and its experimental validation , 2016 .

[84]  Michel Fliess,et al.  Model-free load control for high penetration of solar photovoltaic generation , 2017, 2017 North American Power Symposium (NAPS).

[85]  Ralf Mikut,et al.  Nearest-Neighbor Based Non-Parametric Probabilistic Forecasting with Applications in Photovoltaic Systems , 2017, ArXiv.

[86]  Francisco Beltran-Carbajal,et al.  A fast parametric estimation approach of signals with multiple frequency harmonics , 2017 .

[87]  Mathieu David,et al.  Probabilistic Solar Forecasting Using Quantile Regression Models , 2017 .

[88]  Kazuyuki Aihara,et al.  Improving time series prediction of solar irradiance after sunrise: Comparison among three methods for time series prediction , 2017 .

[89]  Ralf Mikut,et al.  On the use of probabilistic forecasts in scheduling of renewable energy sources coupled to storages , 2018 .