Artificial neural network based daily local forecasting for global solar radiation

Amrouche, Badia; Pivert, Xavier Le

doi:10.1016/j.apenergy.2014.05.055

Cited by 210 publications

(81 citation statements)

References 48 publications

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“…Performance test data set is used to evaluate GSI forecast accuracy of the ANN algorithm. The ANN models inputs acts directly on the training period duration for GSI forecasting [9]. To choose the best design modeling for forecasting, we investigated the role of meteorology data in the ANN model effectivity and then we researched the impact of the data mode on the training good performance and on the forecasting accuracy.…”

Section: Artificial Neural Network Modellingmentioning

confidence: 99%

“…Spatiotemporal pattern recognition and nonlinear principal component analysis (PCA) for global horizontal irradiance forecasting has been proposed as well by Licciardi et al [8]. Amrouche and Le Pivert [9] have presented an ANN based on daily local forecasting for global solar radiation, describing a novel methodology for local forecasting of daily global horizontal irradiance (GHI). The methodology is a combination of spatial modelling and ANNs algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…Yang et al [6,7] studied the forecasting of global horizontal irradiance by exponential smoothing using decompositions, while on another study, they developed the least absolute shrinkage and selection operator model using irradiance very short-term forecasting. Combining a forecasting model with GSI is important to get a better result, and forecasting GSI by the spatiotemporal pattern recognition method, ANN method, parametric models and decomposition models, has been described in [8][9][10]. Spatiotemporal pattern recognition and nonlinear principal component analysis (PCA) for global horizontal irradiance forecasting has been proposed as well by Licciardi et al [8].…”

Section: Introductionmentioning

confidence: 99%

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k-Nearest Neighbor Neural Network Models for Very Short-Term Global Solar Irradiance Forecasting Based on Meteorological Data

Chen

Kartini

2017

Energies

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This paper proposes a novel methodology for very short term forecasting of hourly global solar irradiance (GSI). The proposed methodology is based on meteorology data, especially for optimizing the operation of power generating electricity from photovoltaic (PV) energy. This methodology is a combination of k-nearest neighbor (k-NN) algorithm modelling and artificial neural network (ANN) model. The k-NN-ANN method is designed to forecast GSI for 60 min ahead based on meteorology data for the target PV station which position is surrounded by eight other adjacent PV stations. The novelty of this method is taking into account the meteorology data. A set of GSI measurement samples was available from the PV station in Taiwan which is used as test data. The first method implements k-NN as a preprocessing technique prior to ANN method. The error statistical indicators of k-NN-ANN model the mean absolute bias error (MABE) is 42 W/m 2 and the root-mean-square error (RMSE) is 242 W/m 2 . The models forecasts are then compared to measured data and simulation results indicate that the k-NN-ANN-based model presented in this research can calculate hourly GSI with satisfactory accuracy.

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Section: Artificial Neural Network Modellingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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k-Nearest Neighbor Neural Network Models for Very Short-Term Global Solar Irradiance Forecasting Based on Meteorological Data

Chen

Kartini

2017

Energies

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“…Pandey et al [4] studied the solar irradiance calculation for inclined surface in India and analyzed and compared various calculation models concluding that Klucher model gives the best calculation result. Amrouche et al [5] proposed to establish a neural network model for predicting total solar irradiance on horizontal plane.…”

Section: Introductionmentioning

confidence: 99%

Comparison and Analysis of Calculation Models of Solar Irradiance on Inclined Surface Based on Numerical Simulation Method

Luo¹,

Sun²,

Xu³

et al. 2017

Proceedings of the 2017 2nd International Conference on Electrical, Automation and Mechanical Engineering (EAME 2017)

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Abstract-The present paper summarized the results of the most commonly adopted calculation models for total solar irradiance on inclined surface. The solar irradiance on inclined surface in Beijing was taken as an example and was simulated using Liu and Jordan, Klucher, Hay, and Perez sky models and the results were analyzed in a variety of circumstances. The modified Klucher model was improved with the introduction of the correction for tilt angle β=0° and the suitability of all the five calculation models was analyzed.

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“…Regarding solar energy forecasting, the first attempts made for predicting solar irradiance can be traced back to [10]. Comprehensive reviews of the status of forecasting solar irradiance on different time scales for energy generation are reported in [11,12], while different forecasting techniques to predict solar power output are evaluated and compared in [13,14]. In addition to these references, an interesting review of a wide range of forecasting tools as statistical and computational intelligence models, focused on electricity price forecasting, can be found in [15].…”

Section: Introductionmentioning

confidence: 99%

The “Weather Intelligence for Renewable Energies” Benchmarking Exercise on Short-Term Forecasting of Wind and Solar Power Generation

et al. 2015

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A benchmarking exercise was organized within the framework of the European Action Weather Intelligence for Renewable Energies ("WIRE") with the purpose of evaluating the performance of state of the art models for short-term renewable energy forecasting. The exercise consisted in forecasting the power output of two wind farms and two photovoltaic power plants, in order to compare the merits of forecasts based on different modeling approaches and input data. It was thus possible to obtain a better knowledge of the state of the art in both wind and solar power forecasting, with an overview and comparison of the principal and the novel approaches that are used today in the field, and to assess the evolution of forecast performance with respect to previous benchmarking exercises. The outcome of this exercise consisted then in proposing new challenges in the renewable power forecasting field and identifying the main areas for improving accuracy in the future.

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Artificial neural network based daily local forecasting for global solar radiation

Cited by 210 publications

References 48 publications

k-Nearest Neighbor Neural Network Models for Very Short-Term Global Solar Irradiance Forecasting Based on Meteorological Data

k-Nearest Neighbor Neural Network Models for Very Short-Term Global Solar Irradiance Forecasting Based on Meteorological Data

Comparison and Analysis of Calculation Models of Solar Irradiance on Inclined Surface Based on Numerical Simulation Method

The “Weather Intelligence for Renewable Energies” Benchmarking Exercise on Short-Term Forecasting of Wind and Solar Power Generation

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