Solar radiation forecasting using artificial neural network and random forest methods: Application to normal beam, horizontal diffuse and global components

Benali, L.; Notton, Gilles; Fouilloy, Alexis; Voyant, Cyril; Dizène, Rabah

doi:10.1016/j.renene.2018.08.044

Cited by 320 publications

(131 citation statements)

References 48 publications

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“…A short bibliographical study [60] on DNI forecasting concludes that the DNI forecasting is obtained with a lower accuracy than for GHI forecasting and that only a small number of articles are written on the DNI forecasting at short time horizons as confirmed by Law et al [20].…”

Section: Comparison Between Ghi and Dni Forecastsmentioning

confidence: 91%

Some Applications of ANN to Solar Radiation Estimation and Forecasting for Energy Applications

et al. 2019

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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.

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Section: Comparison Between Ghi and Dni Forecastsmentioning

confidence: 91%

Some Applications of ANN to Solar Radiation Estimation and Forecasting for Energy Applications

et al. 2019

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“…A reliability index was defined to estimate the validity of prediction. Benali et al compared ANN, random forest, and smart persistence methods on the basis of forecast accuracy of three solar components including diffuse horizontal, global horizontal, and beam normal. The comparison is carried out from 1‐ to 6‐hour ahead forecasting horizon.…”

Section: Solar Forecastingmentioning

confidence: 99%

A review on solar forecasting and power management approaches for energy‐harvesting wireless sensor networks

Kakkar

2020

Int J Communication

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Summary For rechargeable wireless sensor nodes, effective power management is of prime importance because of the stochastic behaviour of the environmental resources. A key issue in integrating solar resources with wireless sensor networks (WSNs) is the need of precise irradiance measurements and power to resource modelling. WSNs are employed in an adhoc manner comprises of numerous sensing nodes and organised as a network for the sake of checking and balancing the environmental factors. Each node has sensing, computation, communication, and locomotion capabilities but operates with limited battery life. Energy harvesting is a way of powering these WSNs by harvesting energy from the environment. By considering harvested energy as an energy source, certain considerations are different from that of battery‐operated networks. Nondeterministic energy availability with respect to time is the reason behind these differences, which put a limit on the maximum rate at which energy can be used. Thus, reliable knowledge of solar radiation is essential for informed design, deployment planning, and optimal management of energy in rechargeable WSNs. Further, power management is essential in self‐powerssed networks to efficiently utilize the available energy. In this paper, a detailed survey on different solar forecasting techniques has been presented for precise energy estimates. A detailed study on energy efficient power management techniques is also proposed to address the feasibility of energy‐harvesting approach in WSNs.

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“…For days-ahead predictions, numerical weather prediction (NWP) models have mostly been used. For minutes and hours (0-4 h) ahead forecasts, ground-based cloud images and satellite data, combined with an artificial neural network (ANN) or statistical methods, are most commonly used [8][9][10][11][12][13][14][15][16].…”

Section: Introductionmentioning

confidence: 99%

“…David Bernecker et al [8] utilized sky images to forecast global horizontal solar irradiance from 5 seconds to 10 min in advance in Germany. Benali et al [13] used three methods to predict three components of solar irradiation from the 1-6 h time horizons at the site of Odeillo, France: Smart persistence, artificial neural network, and random forest. Rosiek et al [9] used satellite remote sensing data and an artificial neural network to forecast the building integrated photovoltaic (BIPV) power output with the horizon, up to 3 h ahead in Almería, Spain.…”

Section: Introductionmentioning

confidence: 99%

“…As a result, many methods are only used for specific conditions. Therefore, a method to obtain the cloud index, developed for the FY-4A geostationary meteorological satellite, has not been found in the current literature [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21]. Zhai et al [22] developed a cloud/shadow detection method for remote sensing images that could detect the clouds with a relatively high accuracy and low computational complexity.…”

Section: Introductionmentioning

confidence: 99%

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Nowcasting of Surface Solar Irradiance Using FengYun-4 Satellite Observations over China

Yang

Gao

et al. 2019

Remote Sensing

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The accurate prediction of surface solar irradiance is of great significance for the generation of photovoltaic power. Surface solar irradiance is affected by many random mutation factors, which means that there are great challenges faced in short-term prediction. In Northwest China, there are abundant solar energy resources and large desert areas, which have broad prospects for the development of photovoltaic (PV) systems. For the desert areas in Northwest China, where meteorological stations are scarce, satellite remote sensing data are extremely precious exploration data. In this paper, we present a model using FY-4A satellite images to forecast (up to 15–180 min ahead) global horizontal solar irradiance (GHI), at a 15 min temporal resolution in desert areas under different sky conditions, and compare it with the persistence model (SP). The spatial resolution of the FY-4A satellite images we used was 1 km × 1 km. Particle image velocimetry (PIV) was used to derive the cloud motion vector (CMV) field from the satellite cloud images. The accuracy of the forecast model was evaluated by the ground observed GHI data. The results showed that the normalized root mean square error (nRMSE) ranged from 18.9% to 21.6% and the normalized mean bias error (nMBE) ranged from 3.2% to 4.9% for time horizons from 15 to 180 min under all sky conditions. Compared with the SP model, the nRMSE value was reduced by about 6%, 8%, and 14% with the time horizons of 60, 120, and 180 min, respectively.

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Solar radiation forecasting using artificial neural network and random forest methods: Application to normal beam, horizontal diffuse and global components

Cited by 320 publications

References 48 publications

Some Applications of ANN to Solar Radiation Estimation and Forecasting for Energy Applications

Some Applications of ANN to Solar Radiation Estimation and Forecasting for Energy Applications

A review on solar forecasting and power management approaches for energy‐harvesting wireless sensor networks

Nowcasting of Surface Solar Irradiance Using FengYun-4 Satellite Observations over China

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