Online 3-h forecasting of the power output from a BIPV system using satellite observations and ANN

Rosiek, Sabina; Alonso-Montesinos, Joaquín; Batlles, F.J.

doi:10.1016/j.ijepes.2018.01.025

Cited by 43 publications

(20 citation statements)

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“…All the weighted inputs are summed, and a neuron activation function is activated, such that it generates neuronal learning; when passing to the last neuronal layer, the output data are obtained -in our case, the (y est ) results. A representation of the artificial neural networks model is described, as in Equation (4), where N is the total number of neurons [37,38].…”

Section: Estimation Of the Dni Attenuation Factor Using Neural Networkmentioning

confidence: 99%

“…The Levenberg-Marquardt algorithm was used to train the neurons since it has a faster MSE convergence speed, facilitating the computation time [37,38]. To maintain the same proportion as in the previous models, we used 300 registers to train the neural network; 50 registers were occupied for validation and 50 for testing (400 registers in total).…”

Section: Estimation Of the Dni Attenuation Factor Using Neural Networkmentioning

confidence: 99%

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Attenuation Factor Estimation of Direct Normal Irradiance Combining Sky Camera Images and Mathematical Models in an Inter-Tropical Area

et al. 2020

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Nowadays, it is of great interest to know and forecast the solar energy resource that will be constantly available in order to optimize its use. The generation of electrical energy using CSP (concentrated solar power) plants is mostly affected by atmospheric changes. Therefore, forecasting solar irradiance is essential for planning a plant's operation. Solar irradiance/atmospheric (clouds) interaction studies using satellite and sky images can help to prepare plant operators for solar surface irradiance fluctuations. In this work, we present three methodologies that allow us to estimate direct normal irradiance (DNI). The study was carried out at the Solar Irradiance Observatory (SIO) at the Geophysics Institute (UNAM) in Mexico City using corresponding images obtained with a sky camera and starting from a clear sky model. The multiple linear regression and polynomial regression models as well as the neural networks model designed in the present study, were structured to work under all sky conditions (cloudy, partly cloudy and cloudless), obtaining estimation results with 82% certainty for all sky types. and overcast skies over the short-term (from 1 to 180 min) using a sky camera, where the average nRMSE values obtained were 24. 36%, 20.9% and 19.17%, respectively [5]. G. Reikard calculated the solar irradiance over time horizons of 60, 30, 15, and 5 min, implementing Autoregressive Integrated Moving Average (ARIMA) with errors between 20% and 90% [6]. Solar irradiance forecasting applied to photovoltaic energy production was implemented using the Smart Persistence algorithm in Machine Learning techniques, achieving an nRMSE of 25% on the best panels over short horizons, and 33% over a 6 h horizon [7].An analysis of energy forecasting in solar-tower plants combining a short-term solar irradiation forecasting scheme with a solar-tower plant model, the System Advisor Model (SAM), was used to simulate the behavior of the Gemasolar and Crescent Dunes plants. The findings showed that the best results appeared for the 90-min horizon, where the annual forecasting energy yield for Gemasolar was 97.34 GWh year while for Crescent Dunes it was 392.57 GWh year [8]. Similarly, cloud abundance forecasting has been studied for timescales of between (1-180 min), resulting in short-term forecasting (of less than one hour) and medium-term forecasting (up to 3 h), which was proven to have an 80% success rate-indeed, it was so successful that an application (portal) tool was developed that helps to increase power plant production [9,10].Recent studies have presented a method for the probabilistic forecasting of solar irradiance based on the joint Probability Distribution Function (PDF) of irradiance predicted using the Numerical Weather Prediction (NWP) and the irradiance observed; these are based on models of meteorological processes such as atmospheric dynamics, cloud formation and radiative transfer processes [11]. H. Yang and B. Kurtz estimated direct solar irradiance over the short and medium term for different sky conditions ...

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Section: Estimation Of the Dni Attenuation Factor Using Neural Networkmentioning

confidence: 99%

Section: Estimation Of the Dni Attenuation Factor Using Neural Networkmentioning

confidence: 99%

Attenuation Factor Estimation of Direct Normal Irradiance Combining Sky Camera Images and Mathematical Models in an Inter-Tropical Area

et al. 2020

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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%

“…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. Miller et al [14] used a satellite-based model and fusion technique to forecast global horizontal solar irradiance with the horizons of 0-3 h in the USA.…”

Section: Introductionmentioning

confidence: 99%

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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“…The proposed method outperformed other methods in terms of photovoltaic output power. Rosiek et al [11] presented a hybrid system for forecasting building-integrated photovoltaics. The system combined satellite images with a neural network.…”

Section: Introductionmentioning

confidence: 99%

Renewable Power Output Forecasting Using Least-Squares Support Vector Regression and Google Data

et al. 2019

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Sustainable and green technologies include renewable energy sources such as solar power, wind power, and hydroelectric power. Renewable power output forecasting is an essential contributor to energy technology and strategy analysis. This study attempts to develop a novel least-squares support vector regression with a Google (LSSVR-G) model to accurately forecast power output with renewable power, thermal power, and nuclear power outputs in Taiwan. This study integrates a Google application programming interface (API), least-squares support vector regression (LSSVR), and a genetic algorithm (GA) to develop a novel LSSVR-G model for accurately forecasting power output from various power outputs in Taiwan. Material price and the search volume via Google's search engine for keywords, which is used for various power outputs and is collected by Google APIs, are used as input data. The forecasting model uses LSSVR. Furthermore, the LSSVR employs a GA to find the optimal parameters for the LSSVR. Real-world annual power output datasets collected from Taiwan were used to demonstrate the forecasting performance of the model. The empirical results reveal that the proposed LSSVR-G model is superior to all other considered models both in terms of accuracy and stability, and, thus, can be a useful tool for renewable power forecasting. Moreover, the accuracy forecasting thermal power and nuclear power could effectively assist in understanding the future trend of renewable power output in Taiwan. The accurately forecasting result could effectively provide basic information for renewable power, thermal power, and nuclear power planning and policy making in Taiwan.

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Online 3-h forecasting of the power output from a BIPV system using satellite observations and ANN

Cited by 43 publications

References 42 publications

Attenuation Factor Estimation of Direct Normal Irradiance Combining Sky Camera Images and Mathematical Models in an Inter-Tropical Area

Attenuation Factor Estimation of Direct Normal Irradiance Combining Sky Camera Images and Mathematical Models in an Inter-Tropical Area

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

Renewable Power Output Forecasting Using Least-Squares Support Vector Regression and Google Data

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