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The Green Deal and increased nutritional needs are driving factors in human activities nowadays. Agriculture is an essential economic sector that can profit from the application of renewable energy sources by the assimilation of off-grid, arid and barren terrains. Power supplied by hybrid systems for water pumping is a solution for overcoming the stochastic character of the renewable energy sources. This paper presents a sizing methodology for a hybrid system with wind and PV generation and water tank storage, based on the consideration of the entire energy conversion chain with energy models and a one-year operation simulation. The PV generator is modeled using a reduced Durisch’s model, while for the wind generator a piecewise interpolation is used. The methodology is applied for sites in Bulgaria with specific agricultural crops and meteorological data. Combinations of PV (different technologies) and wind (different types) generators and water tank capacities are considered and discussed. The combinations are compared on the basis of three criteria: the investment cost, the satisfaction of crop requirements and system oversizing. The possibility for the introduction of battery storage is also examined. The results show some trends in the hybrid system sizing and the possibility to apply the proposed methodology for various sites, generators and crops.
Ludmil Stoyanov; Ivan Bachev; Zahari Zarkov; Vladimir Lazarov; Gilles Notton. Multivariate Analysis of a Wind–PV-Based Water Pumping Hybrid System for Irrigation Purposes. Energies 2021, 14, 3231 .
AMA StyleLudmil Stoyanov, Ivan Bachev, Zahari Zarkov, Vladimir Lazarov, Gilles Notton. Multivariate Analysis of a Wind–PV-Based Water Pumping Hybrid System for Irrigation Purposes. Energies. 2021; 14 (11):3231.
Chicago/Turabian StyleLudmil Stoyanov; Ivan Bachev; Zahari Zarkov; Vladimir Lazarov; Gilles Notton. 2021. "Multivariate Analysis of a Wind–PV-Based Water Pumping Hybrid System for Irrigation Purposes." Energies 14, no. 11: 3231.
Modeling photovoltaic modules using the single-diode model presents an adequate balance between complexity and accuracy; however, the current–voltage characteristic has to be calculated every time the operating condition changes. In turn, IEC-60891 provides procedures for direct translation from one operating condition to another, allowing a more straightforward application, provided that specific parameters are known. That international standard describes a procedure to quantify such parameters; however, their calculation requires complex equipment or extensive experimental tests. This article presents an alternative and simplified method to compute the parameters of IEC-60891, specifically the second procedure presented in the standard. It requires a reduced amount of measurements to be used as references. The validation of the method has been carried out taking extensive experimental data as reference, considering thousands of measurements taken for 16 months. The application of the proposed method to translate the reference curves presented mean absolute error and root-mean-square error below 3%, considering two different photovoltaic modules.
Caio Felippe Abe; Joao Batista Dias; Fernanda Haeberle; Gilles Notton; Ghjuvan Antone Faggianelli. Simplified Approach to Adjust IEC-60891 Equation Coefficients From Experimental Measurements With Long-Term Validation. IEEE Journal of Photovoltaics 2020, 11, 496 -503.
AMA StyleCaio Felippe Abe, Joao Batista Dias, Fernanda Haeberle, Gilles Notton, Ghjuvan Antone Faggianelli. Simplified Approach to Adjust IEC-60891 Equation Coefficients From Experimental Measurements With Long-Term Validation. IEEE Journal of Photovoltaics. 2020; 11 (2):496-503.
Chicago/Turabian StyleCaio Felippe Abe; Joao Batista Dias; Fernanda Haeberle; Gilles Notton; Ghjuvan Antone Faggianelli. 2020. "Simplified Approach to Adjust IEC-60891 Equation Coefficients From Experimental Measurements With Long-Term Validation." IEEE Journal of Photovoltaics 11, no. 2: 496-503.
The use of renewable energy sources, and in particular photovoltaics, can effectively reduce the supply of household energy from the main grid, contributing to a more sustainable community. In this paper, several energy management strategies were applied to an existing microgrid with photovoltaic (PV) production and battery storage in view to supply in electricity a building and an electric vehicle located in Ajaccio, France. The purpose was to determine how the choice of a management strategy can impact the cost and the energy share in the microgrid, using the actual electricity tariff in France as well as an over-cost due to the island situation. For some strategies, a forecasting tool was introduced and its influence on the performances of the microgrid was discussed. It appears that the performance of the strategy increased with its complexity and the use of PV forecasting.
Sarah Ouédraogo; Ghjuvan Antone Faggianelli; Guillaume Pigelet; Jean Laurent Duchaud; Gilles Notton. Application of Optimal Energy Management Strategies for a Building Powered by PV/Battery System in Corsica Island. Energies 2020, 13, 4510 .
AMA StyleSarah Ouédraogo, Ghjuvan Antone Faggianelli, Guillaume Pigelet, Jean Laurent Duchaud, Gilles Notton. Application of Optimal Energy Management Strategies for a Building Powered by PV/Battery System in Corsica Island. Energies. 2020; 13 (17):4510.
Chicago/Turabian StyleSarah Ouédraogo; Ghjuvan Antone Faggianelli; Guillaume Pigelet; Jean Laurent Duchaud; Gilles Notton. 2020. "Application of Optimal Energy Management Strategies for a Building Powered by PV/Battery System in Corsica Island." Energies 13, no. 17: 4510.
The utilization of multi-junction solar cells with high efficiency is still not widespread for terrestrial power applications. These solar cells, composed by several material layers, reach high efficiencies but its cost is expensive, more of 100 higher than classical silicon solar cells. Thus, one solution consists to use reduced sizing solar cells associated with optics mounted on solar tracker to concentrate the solar beam. Numerous meteorological parameters such as beam solar irradiance, ambient temperature and air mass and especially spectral characteristics of sun radiation are involved in the conversion process and are generally used as inputs in power models. Several models from literature, different by their form and by the number and type of input variables, are presented; based on this state-of-art, some similar models are selected and tested on two experimental CPV systems located on two different sites, Ajaccio and Le Bourget du Lac. Then, an operational model of electrical power using inputs easily measured and available for a solar CPV plant operator is developed. It could be used as a decision-aided tool for investors in providing an estimation of the energy production capacity of the CPV systems on the future implantation site. This established model based on data measured on the CPV system in Ajaccio estimates the produced power with a root mean square error of about 5% on the two sites using only a reduced number of inputs.
Mousaab Benhammane; Gilles Notton; Grégoire Pichenot; Philippe Voarino; David Ouvrard. Overview of electrical power models for concentrated photovoltaic systems and development of a new operational model with easily accessible inputs. Renewable and Sustainable Energy Reviews 2020, 135, 110221 .
AMA StyleMousaab Benhammane, Gilles Notton, Grégoire Pichenot, Philippe Voarino, David Ouvrard. Overview of electrical power models for concentrated photovoltaic systems and development of a new operational model with easily accessible inputs. Renewable and Sustainable Energy Reviews. 2020; 135 ():110221.
Chicago/Turabian StyleMousaab Benhammane; Gilles Notton; Grégoire Pichenot; Philippe Voarino; David Ouvrard. 2020. "Overview of electrical power models for concentrated photovoltaic systems and development of a new operational model with easily accessible inputs." Renewable and Sustainable Energy Reviews 135, no. : 110221.
With the development of micro-grids including PV production and storage, the need for efficient energy management strategies arises. One of their key components is the forecast of the energy production from very short to long term. The forecast time-step is an important parameter affecting not only its accuracy but also the optimal control time discretization, hence its efficiency and computational burden. To quantify this trade-off, four machine learning forecast models are tested on two geographical locations for time-steps varying from 2 to 60 min and horizons from 10 min to 6 h, on global irradiance horizontal and tilted when data was available. The results are similar for all the models and indicate that the error metric can be reduced up to 0.8% per minute on the time-step for forecasts below one hour and up to 1.7% per ten minutes for forecasts between one and six hours. In addition, it is shown that for short term horizons, it may be advantageous to forecast with a high resolution then average the results at the time-step needed by the energy management system.
Jean-Laurent Duchaud; Cyril Voyant; Alexis Fouilloy; Gilles Notton; Marie-Laure Nivet. Trade-Off between Precision and Resolution of a Solar Power Forecasting Algorithm for Micro-Grid Optimal Control. Energies 2020, 13, 3565 .
AMA StyleJean-Laurent Duchaud, Cyril Voyant, Alexis Fouilloy, Gilles Notton, Marie-Laure Nivet. Trade-Off between Precision and Resolution of a Solar Power Forecasting Algorithm for Micro-Grid Optimal Control. Energies. 2020; 13 (14):3565.
Chicago/Turabian StyleJean-Laurent Duchaud; Cyril Voyant; Alexis Fouilloy; Gilles Notton; Marie-Laure Nivet. 2020. "Trade-Off between Precision and Resolution of a Solar Power Forecasting Algorithm for Micro-Grid Optimal Control." Energies 13, no. 14: 3565.
Solar irradiance and cell temperature are the most significant aspects when assessing the production of a photovoltaic system. To avoid the need of specific sensors for quantifying such parameters, recent literature presents methods to estimate them through electrical measurements, using the photovoltaic module itself as a sensor. This work presents an application of such methods to data recorded using a research platform at University of Corsica, in France. The methods and the platform are briefly presented and the results are shown and discussed in terms of normalized mean absolute errors (nMAE) and root mean square errors (nRMSE) for various irradiance and cell temperature levels. The nMAE (and nRMSE) for solar irradiance are respectively between 3.5% and 3.9% (4.2% and 4.7%). Such errors on computed irradiance are in the same order of magnitude as those found in the literature, with a simple implementation. For cell temperatures estimation, the nMAE and nRMSE were found to be in the range 3.4%–8.2% and 4.3%–10.7%. These results show that using such methods could provide an estimation for the values of irradiance and cell temperature, even if the modules are not new and are not regularly cleaned, but of course not partially shaded.
Caio Felippe Abe; João Batista Dias; Gilles Notton; Ghjuvan Antone Faggianelli. Experimental Application of Methods to Compute Solar Irradiance and Cell Temperature of Photovoltaic Modules. Sensors 2020, 20, 2490 .
AMA StyleCaio Felippe Abe, João Batista Dias, Gilles Notton, Ghjuvan Antone Faggianelli. Experimental Application of Methods to Compute Solar Irradiance and Cell Temperature of Photovoltaic Modules. Sensors. 2020; 20 (9):2490.
Chicago/Turabian StyleCaio Felippe Abe; João Batista Dias; Gilles Notton; Ghjuvan Antone Faggianelli. 2020. "Experimental Application of Methods to Compute Solar Irradiance and Cell Temperature of Photovoltaic Modules." Sensors 20, no. 9: 2490.
The electricity produced by photovoltaic sources is mainly influenced by the solar irradiance and temperature of the cells. The literature presents methods to estimate these parameters through the measurements of open-circuit voltage and short-circuit current of the array. However, in an actual photovoltaic system, changing the operating point of the module to short circuit or open circuit requires the array to be disconnected from the inverter, thus causing an impact on the electrical energy production. Such a problem could be avoided if the irradiance and the cell temperature were computed by means of the maximum power point coordinates, which is the focus of this article. To perform the calculation of the irradiance and the temperature of a photovoltaic module, a study of the relationship between the electric parameters and the operating conditions has been carried out. This concerns the modeling of the photovoltaic cell with respect to the identification and translation of parameters. In addition, IEC-60 891, which proposes equations for the voltage and current translation of photovoltaic generators, has been considered. Finally, application and performance evaluation of a new equation using the thermal coefficient of power, which relates the maximum power to the irradiance and cell temperature, is also presented. Experimental verification of the methods has been carried out, presenting consistent results.
Caio Felippe Abe; Joao Batista Dias; Gilles Notton; Philippe Poggi. Computing Solar Irradiance and Average Temperature of Photovoltaic Modules From the Maximum Power Point Coordinates. IEEE Journal of Photovoltaics 2020, 10, 655 -663.
AMA StyleCaio Felippe Abe, Joao Batista Dias, Gilles Notton, Philippe Poggi. Computing Solar Irradiance and Average Temperature of Photovoltaic Modules From the Maximum Power Point Coordinates. IEEE Journal of Photovoltaics. 2020; 10 (2):655-663.
Chicago/Turabian StyleCaio Felippe Abe; Joao Batista Dias; Gilles Notton; Philippe Poggi. 2020. "Computing Solar Irradiance and Average Temperature of Photovoltaic Modules From the Maximum Power Point Coordinates." IEEE Journal of Photovoltaics 10, no. 2: 655-663.
In this article, heat loss reduction and overall performances improvement of a solar collector by using Phase Change Material (PCM) are examined. In authors' previous studies, a building-integrated solar collector has been presented with an experimental characterisation and a validated numerical model. In addition, thermal losses at high reduced temperatures were identified due to the specific collector shape. On the other hand, several authors introduced PCM thermal storage for domestic hot water systems (DHWS). In the frame of the present study, the goal is to use the high PCM volumetric thermal density for limiting both temperature and thermal losses and recovering a part of the stored heat during evening. Adding PCM might change the optimum operating conditions: the influence on monthly performances of existing PCM characteristics, flow rate variation, temperature regulation and PCM volume addition are investigated. Simulations for a complete DHWS have been performed with measured environmental data (solar radiation, wind, ambient temperature). The mathematical model of PCM thermal process is presented. The performances with PCM addition are evaluated and the improvements on the thermal behaviour are estimated. In addition, Life Cycle Assessment (LCA) is performed in order to examine the influence of PCM use on the environmental profile of the solar system.
F. Motte; G. Notton; Chr Lamnatou; C. Cristofari; Daniel Chemisana. Numerical study of PCM integration impact on overall performances of a highly building-integrated solar collector. Renewable Energy 2019, 137, 10 -19.
AMA StyleF. Motte, G. Notton, Chr Lamnatou, C. Cristofari, Daniel Chemisana. Numerical study of PCM integration impact on overall performances of a highly building-integrated solar collector. Renewable Energy. 2019; 137 ():10-19.
Chicago/Turabian StyleF. Motte; G. Notton; Chr Lamnatou; C. Cristofari; Daniel Chemisana. 2019. "Numerical study of PCM integration impact on overall performances of a highly building-integrated solar collector." Renewable Energy 137, no. : 10-19.
In solar energy, the knowledge of solar radiation is very important for the integration of energy systems in building or electrical networks. Global horizontal irradiation (GHI) data are rarely measured over the world, thus an artificial neural network (ANN) model was built to calculate this data from more available ones. For the estimation of 5-min GHI, the normalized root mean square error (nRMSE) of the 6-inputs model is 19.35%. As solar collectors are often tilted, a second ANN model was developed to transform GHI into global tilted irradiation (GTI), a difficult task due to the anisotropy of scattering phenomena in the atmosphere. The GTI calculation from GHI was realized with an nRMSE around 8% for the optimal configuration. These two models estimate solar data at time, t, from other data measured at the same time, t. For an optimal management of energy, the development of forecasting tools is crucial because it allows anticipation of the production/consumption balance; thus, ANN models were developed to forecast hourly direct normal (DNI) and GHI irradiations for a time horizon from one hour (h+1) to six hours (h+6). The forecasting of hourly solar irradiation from h+1 to h+6 using ANN was realized with an nRMSE from 22.57% for h+1 to 34.85% for h+6 for GHI and from 38.23% for h+1 to 61.88% for h+6 for DNI.
Gilles Notton; Cyril Voyant; Alexis Fouilloy; Jean Laurent Duchaud; Marie Laure Nivet. Some Applications of ANN to Solar Radiation Estimation and Forecasting for Energy Applications. Applied Sciences 2019, 9, 209 .
AMA StyleGilles Notton, Cyril Voyant, Alexis Fouilloy, Jean Laurent Duchaud, Marie Laure Nivet. Some Applications of ANN to Solar Radiation Estimation and Forecasting for Energy Applications. Applied Sciences. 2019; 9 (1):209.
Chicago/Turabian StyleGilles Notton; Cyril Voyant; Alexis Fouilloy; Jean Laurent Duchaud; Marie Laure Nivet. 2019. "Some Applications of ANN to Solar Radiation Estimation and Forecasting for Energy Applications." Applied Sciences 9, no. 1: 209.
The present work aims to present the electrical energy situation of several French islands spread over the World. Various aspects are successively studied: repartition of energy means, renewable energy part in the production with a focus on the intermittent renewable sources, legal and financial aspect. The electrical situation of the islands is compared with the French mainland one. The electricity production cost in the islands are presented and the financial features for renewable energy in France are exposed. In a second part, a focus is realized on the Corsica Island situated in the Mediterranean Sea and partially connected to Italy. Successively, the energy mix, the objective of the new energy plan for 2023 and the renewable energy situation, present and future, are presented. Even if the integration of non-programmable renewable energy plants is more complex in small insular networks, the high cost of electricity generation in such territories encourages the introduction of wind and PV systems. The islands are good laboratories for the development of intermittent and stochastic renewable energy systems.
G. Notton; J.L. Duchaud; M.L. Nivet; C. Voyant; K. Chalvatzis; A. Fouilloy. The electrical energy situation of French islands and focus on the Corsican situation. Renewable Energy 2019, 135, 1157 -1165.
AMA StyleG. Notton, J.L. Duchaud, M.L. Nivet, C. Voyant, K. Chalvatzis, A. Fouilloy. The electrical energy situation of French islands and focus on the Corsican situation. Renewable Energy. 2019; 135 ():1157-1165.
Chicago/Turabian StyleG. Notton; J.L. Duchaud; M.L. Nivet; C. Voyant; K. Chalvatzis; A. Fouilloy. 2019. "The electrical energy situation of French islands and focus on the Corsican situation." Renewable Energy 135, no. : 1157-1165.
Eleven statistical and machine learning tools are analyzed and applied to hourly solar irradiation forecasting for time horizon from 1 to 6 h. A methodology is presented to select the best and most reliable forecasting model according to the meteorological variability of the site. To make the conclusions more universal, solar data collected in three sites with low, medium and high meteorological variabilities are used: Ajaccio, Tilos and Odeillo. The datasets variability is evaluated using the mean absolute log return value. The models were compared in term of normalized root mean square error, mean absolute error and skill score. The most efficient models are selected for each variability and temporal horizon: for the weak variability, auto-regressive moving average and multi-layer perceptron are the most efficient, for a medium variability, auto-regressive moving average and bagged regression tree are the best predictors and for a high one, only more complex methods can be used efficiently, bagged regression tree and the random forest approach.
Alexis Fouilloy; Cyril Voyant; Gilles Notton; Fabrice Motte; Christophe Paoli; Marie-Laure Nivet; Emmanuel Guillot; Jean-Laurent Duchaud. Solar irradiation prediction with machine learning: Forecasting models selection method depending on weather variability. Energy 2018, 165, 620 -629.
AMA StyleAlexis Fouilloy, Cyril Voyant, Gilles Notton, Fabrice Motte, Christophe Paoli, Marie-Laure Nivet, Emmanuel Guillot, Jean-Laurent Duchaud. Solar irradiation prediction with machine learning: Forecasting models selection method depending on weather variability. Energy. 2018; 165 ():620-629.
Chicago/Turabian StyleAlexis Fouilloy; Cyril Voyant; Gilles Notton; Fabrice Motte; Christophe Paoli; Marie-Laure Nivet; Emmanuel Guillot; Jean-Laurent Duchaud. 2018. "Solar irradiation prediction with machine learning: Forecasting models selection method depending on weather variability." Energy 165, no. : 620-629.
Simple, naïve, smart or clearness persistences are tools largely used as naïve predictors for the global solar irradiation forecasting. It is essential to compare the performances of sophisticated prediction approaches with that of a reference approach generally a naïve methods. In this paper, a new kind of naïve “nowcaster” is developed, a persistence model based on the stochastic aspect of measured solar energy signal denoted stochastic persistence and constructed without needing a large collection of historical data. Two versions are proposed: one based on an additive and one on a multiplicative scheme; a theoretical description and an experimental validation based on measurements realized in Ajaccio (France) and Tilos (Greece) are exposed. The results show that this approach is efficient, easy to implement and does not need historical data as the machine learning methods usually employed. This new solar irradiation predictor could become an interesting tool and become a new member of the solar forecasting family.
Cyril Voyant; Gilles Notton. Solar irradiation nowcasting by stochastic persistence: A new parsimonious, simple and efficient forecasting tool. Renewable and Sustainable Energy Reviews 2018, 92, 343 -352.
AMA StyleCyril Voyant, Gilles Notton. Solar irradiation nowcasting by stochastic persistence: A new parsimonious, simple and efficient forecasting tool. Renewable and Sustainable Energy Reviews. 2018; 92 ():343-352.
Chicago/Turabian StyleCyril Voyant; Gilles Notton. 2018. "Solar irradiation nowcasting by stochastic persistence: A new parsimonious, simple and efficient forecasting tool." Renewable and Sustainable Energy Reviews 92, no. : 343-352.
Three methods, smart persistence, artificial neural network and random forest, are compared to forecast the three components of solar irradiation (global horizontal, beam normal and diffuse horizontal) measured on the site of Odeillo, France, characterized by a high meteorological variability. The objective is to predict hourly solar irradiations for time horizons from h+1 to h+6. The random forest (RF) method is the most efficient and forecasts the three components with a nRMSE from 19.65% for h+1 to 27.78% for h+6 for the global horizontal irradiation (GHI), a nRMSE from 34.11% for h+1 to 49.08% for h+6 for the beam normal irradiation (BNI); a nRMSE from 35.08% for h+1 to 49.14% for h+6 for diffuse horizontal irradiation (DHI). The improvement brought by the use of RF compared to Artificial Neural Network (ANN) and smart persistence (SP) increases with the forecasting horizon. A seasonal study is realized and shows that the forecasting of solar irradiation during spring and autumn is less reliable than during winter and summer because during these periods the meteorological variability is more important.
L. Benali; G. Notton; A. Fouilloy; Cyril Voyant; R. Dizene. Solar radiation forecasting using artificial neural network and random forest methods: Application to normal beam, horizontal diffuse and global components. Renewable Energy 2018, 132, 871 -884.
AMA StyleL. Benali, G. Notton, A. Fouilloy, Cyril Voyant, R. Dizene. Solar radiation forecasting using artificial neural network and random forest methods: Application to normal beam, horizontal diffuse and global components. Renewable Energy. 2018; 132 ():871-884.
Chicago/Turabian StyleL. Benali; G. Notton; A. Fouilloy; Cyril Voyant; R. Dizene. 2018. "Solar radiation forecasting using artificial neural network and random forest methods: Application to normal beam, horizontal diffuse and global components." Renewable Energy 132, no. : 871-884.
The purpose of this work is to assess the energies produced by a hybrid system composed of photovoltaic generators and wind turbines. This study aims to develop a method, which could facilitate the sizing of photovoltaic and wind generators in a given hybrid system. The proposed method could also help with the sizing of storage devices in the hybrid system, which provide energy for the consumer in moments when the primary renewable energy source is lacking or for sizing the energy exchange with the grid.
Ivan Bachev; Boris Demirkov; Ludmil Stoyanov; Vladimir Lazarov; Zahari Zarkov; Gilles Notton; Andrei Damian. GENERALIZED APPROACH FOR FEASIBILITY STUDY OF HYBRID SYSTEMS WITH RENEWABLE ENERGY SOURCES. Ecological Engineering and Environment Protection 2018, 64 -73.
AMA StyleIvan Bachev, Boris Demirkov, Ludmil Stoyanov, Vladimir Lazarov, Zahari Zarkov, Gilles Notton, Andrei Damian. GENERALIZED APPROACH FOR FEASIBILITY STUDY OF HYBRID SYSTEMS WITH RENEWABLE ENERGY SOURCES. Ecological Engineering and Environment Protection. 2018; ():64-73.
Chicago/Turabian StyleIvan Bachev; Boris Demirkov; Ludmil Stoyanov; Vladimir Lazarov; Zahari Zarkov; Gilles Notton; Andrei Damian. 2018. "GENERALIZED APPROACH FOR FEASIBILITY STUDY OF HYBRID SYSTEMS WITH RENEWABLE ENERGY SOURCES." Ecological Engineering and Environment Protection , no. : 64-73.
Hassan Z. Al Garni; Amr Amin; Arthur E.A. Amorim; Ahmad Atieh; Anjali Awasthi; Marco Balato; Marcelo A. Barone; Frede Blaabjerg; Alvaro Serna Cantero; Monica Carvalho; Maher Chaabene; Sana Charfi; Luigi Costanzo; Adel El Samhey; Ali M. Eltamaly; Alberto J.R. Freire; Antonio Lecuona-Neumann; Mathieu Legrand; Rafael López-Luque; José I. Nogueira; Gilles Notton; Flávio D.C. Oliveira; José C.E. Palacio; Juan Reca-Cardeña; Djamila Rekioua; Arnaldo M.M. Reyes; Marwa Salem; Ariya Sangwongwanich; José J.C.S. Santos; Ahmed Shaker; Adel Sharaf; Santiago Silvestre; Domingos S.L. Simonetti; Massimo Vitelli; Cyril Voyant; Imene Yahyaoui; Yongheng Yang; Erhab Youssef; Abdelhalim Zekry. List of Contributors. Advances in Renewable Energies and Power Technologies 2018, 1 .
AMA StyleHassan Z. Al Garni, Amr Amin, Arthur E.A. Amorim, Ahmad Atieh, Anjali Awasthi, Marco Balato, Marcelo A. Barone, Frede Blaabjerg, Alvaro Serna Cantero, Monica Carvalho, Maher Chaabene, Sana Charfi, Luigi Costanzo, Adel El Samhey, Ali M. Eltamaly, Alberto J.R. Freire, Antonio Lecuona-Neumann, Mathieu Legrand, Rafael López-Luque, José I. Nogueira, Gilles Notton, Flávio D.C. Oliveira, José C.E. Palacio, Juan Reca-Cardeña, Djamila Rekioua, Arnaldo M.M. Reyes, Marwa Salem, Ariya Sangwongwanich, José J.C.S. Santos, Ahmed Shaker, Adel Sharaf, Santiago Silvestre, Domingos S.L. Simonetti, Massimo Vitelli, Cyril Voyant, Imene Yahyaoui, Yongheng Yang, Erhab Youssef, Abdelhalim Zekry. List of Contributors. Advances in Renewable Energies and Power Technologies. 2018; ():1.
Chicago/Turabian StyleHassan Z. Al Garni; Amr Amin; Arthur E.A. Amorim; Ahmad Atieh; Anjali Awasthi; Marco Balato; Marcelo A. Barone; Frede Blaabjerg; Alvaro Serna Cantero; Monica Carvalho; Maher Chaabene; Sana Charfi; Luigi Costanzo; Adel El Samhey; Ali M. Eltamaly; Alberto J.R. Freire; Antonio Lecuona-Neumann; Mathieu Legrand; Rafael López-Luque; José I. Nogueira; Gilles Notton; Flávio D.C. Oliveira; José C.E. Palacio; Juan Reca-Cardeña; Djamila Rekioua; Arnaldo M.M. Reyes; Marwa Salem; Ariya Sangwongwanich; José J.C.S. Santos; Ahmed Shaker; Adel Sharaf; Santiago Silvestre; Domingos S.L. Simonetti; Massimo Vitelli; Cyril Voyant; Imene Yahyaoui; Yongheng Yang; Erhab Youssef; Abdelhalim Zekry. 2018. "List of Contributors." Advances in Renewable Energies and Power Technologies , no. : 1.
In this survey, several statistical and machine learning tools are analyzed and compared in view to forecast the solar irradiation in Ajaccio (Corsica, France, 41°55 N, 8°44 E, 4m asl). The forecasting horizon is from 1 to 6 hours with an hourly time granularity. Eleven forecasting models are compared: persistence, scaled persistence, ARMA, MLP, regression trees, boosted regression trees, bagged regression trees, pruned regression trees, random forest, Gaussian processes and support vector regression. The models are compared in term of error metrics: nRMSE (normalized root mean squared error), MAE (mean absolute error) and skill score related to the smart persistence.
Alexis Fouilloy; Cyril Voyant; Gilles Notton; Marie Laure Nivet; Jean Laurent Duchaud. Machine Learning Methods for Solar Irradiation Forecasting: A Comparison in a Mediterranean Site. 2017, 1 .
AMA StyleAlexis Fouilloy, Cyril Voyant, Gilles Notton, Marie Laure Nivet, Jean Laurent Duchaud. Machine Learning Methods for Solar Irradiation Forecasting: A Comparison in a Mediterranean Site. . 2017; ():1.
Chicago/Turabian StyleAlexis Fouilloy; Cyril Voyant; Gilles Notton; Marie Laure Nivet; Jean Laurent Duchaud. 2017. "Machine Learning Methods for Solar Irradiation Forecasting: A Comparison in a Mediterranean Site." , no. : 1.
A simulation tool for the operation of a hybrid PV/Wind plant coupled with a hydro-pumping storage (HPS) was built; it was used for simulating the behavior of such a system among an energy mix constituted by fuel oil generators and electrical cables in an insular electrical network. Each subsystem is modeled with a variable efficiency depending on the operating regime and on solar and wind sources variability. An optimization of the hydro-pumping system operation was developed using four reversible pumps in parallel. The objective is to shave the electrical peak demand in replacement of costly and polluting combustible turbines. An energy situation like the one in Corsica Island is considered and all the electrical productions are taken into account, not only renewables but also fuel and imported electrical energy. The covered part of the peak demand can reach 80% in an annual basis and the influence of the hybrid system characteristics on the performances were studied. Some hybrid systems configurations were highlighted.
Gilles Notton; Driada Mistrushi; Ludmil Stoyanov; Pellumb Berberi. Operation of a photovoltaic-wind plant with a hydro pumping-storage for electricity peak-shaving in an island context. Solar Energy 2017, 157, 20 -34.
AMA StyleGilles Notton, Driada Mistrushi, Ludmil Stoyanov, Pellumb Berberi. Operation of a photovoltaic-wind plant with a hydro pumping-storage for electricity peak-shaving in an island context. Solar Energy. 2017; 157 ():20-34.
Chicago/Turabian StyleGilles Notton; Driada Mistrushi; Ludmil Stoyanov; Pellumb Berberi. 2017. "Operation of a photovoltaic-wind plant with a hydro pumping-storage for electricity peak-shaving in an island context." Solar Energy 157, no. : 20-34.
International audienceForecasting the output power of solar systems is required for the good operation of the power grid or for the optimal management of the energy fluxes occurring into the solar system. Before forecasting the solar systems output, it is essential to focus the prediction on the solar irradiance. The global solar radiation forecasting can be performed by several methods; the two big categories are the cloud imagery combined with physical models, and the machine learning models. In this context, the objective of this paper is to give an overview of forecasting methods of solar irradiation using machine learning approaches. Although, a lot of papers describes methodologies like neural networks or support vector regression, it will be shown that other methods (regression tree, random forest, gradient boosting and many others) begin to be used in this context of prediction. The performance ranking of such methods is complicated due to the diversity of the data set, time step, forecasting horizon, set up and performance indicators. Overall, the error of prediction is quite equivalent. To improve the prediction performance some authors proposed the use of hybrid models or to use an ensemble forecast approach
Cyril Voyant; Gilles Notton; Soteris Kalogirou; Marie-Laure Nivet; Christophe Paoli; Fabrice Motte; Alexis Fouilloy. Machine learning methods for solar radiation forecasting: A review. Renewable Energy 2017, 105, 569 -582.
AMA StyleCyril Voyant, Gilles Notton, Soteris Kalogirou, Marie-Laure Nivet, Christophe Paoli, Fabrice Motte, Alexis Fouilloy. Machine learning methods for solar radiation forecasting: A review. Renewable Energy. 2017; 105 ():569-582.
Chicago/Turabian StyleCyril Voyant; Gilles Notton; Soteris Kalogirou; Marie-Laure Nivet; Christophe Paoli; Fabrice Motte; Alexis Fouilloy. 2017. "Machine learning methods for solar radiation forecasting: A review." Renewable Energy 105, no. : 569-582.
International audienceAs global solar radiation forecasting is a very important challenge, several methods are devoted to this goal with different levels of accuracy and confidence. In this study we propose to better understand how the uncertainty is propagated in the context of global radiation time series forecasting using machine learning. Indeed we propose to decompose the error considering four kinds of uncertainties: the error due to the measurement, the variability of time series, the machine learning uncertainty and the error related to the horizon. All these components of the error allow to determinate a global uncertainty generating prediction bands related to the prediction efficiency. We also have defined a reliability index which could be very interesting for the grid manager in order to estimate the validity of predictions. We have experimented this method on a multilayer perceptron which is a popular machine learning technique.We have shown that the global error and its components are essential to quantify in order to estimate the reliability of the modeloutputs.The described method has been successfully applied to four meteorological stations in Mediterranean area
Cyril Voyant; Gilles Notton; Christophe Darras; Alexis Fouilloy; Fabrice Motte. Uncertainties in global radiation time series forecasting using machine learning: The multilayer perceptron case. Energy 2017, 125, 248 -257.
AMA StyleCyril Voyant, Gilles Notton, Christophe Darras, Alexis Fouilloy, Fabrice Motte. Uncertainties in global radiation time series forecasting using machine learning: The multilayer perceptron case. Energy. 2017; 125 ():248-257.
Chicago/Turabian StyleCyril Voyant; Gilles Notton; Christophe Darras; Alexis Fouilloy; Fabrice Motte. 2017. "Uncertainties in global radiation time series forecasting using machine learning: The multilayer perceptron case." Energy 125, no. : 248-257.
Cyril Voyant; Fabrice Motte; Alexis Fouilloy; Gilles Notton; Christophe Paoli; Marie-Laure Nivet. Forecasting method for global radiation time series without training phase: Comparison with other well-known prediction methodologies. Energy 2017, 120, 199 -208.
AMA StyleCyril Voyant, Fabrice Motte, Alexis Fouilloy, Gilles Notton, Christophe Paoli, Marie-Laure Nivet. Forecasting method for global radiation time series without training phase: Comparison with other well-known prediction methodologies. Energy. 2017; 120 ():199-208.
Chicago/Turabian StyleCyril Voyant; Fabrice Motte; Alexis Fouilloy; Gilles Notton; Christophe Paoli; Marie-Laure Nivet. 2017. "Forecasting method for global radiation time series without training phase: Comparison with other well-known prediction methodologies." Energy 120, no. : 199-208.