Short and medium-term cloudiness forecasting using remote sensing techniques and sky camera imagery

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

doi:10.1016/j.energy.2014.06.101

Cited by 35 publications

(23 citation statements)

References 28 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…As a result, AI data are selected as an additional input variable in the proposed model, which are provided by NASA through remote sensing techniques. AI cannot be measured locally, but has been used in weather forecasting area with the assistance of remote sensing technology [37]. AI is an index that detects the presence of UV-absorbing aerosols such as dust and soot; there are different spectrally resolved and broadband aerosol optical depth definitions, e.g., some references use the ratio of spectral radiant flux of 340 and 380 nm channel [38], [39].…”

Section: B Definition Of Aerosol Index (Ai) In Meteorology Areamentioning

confidence: 99%

An Improved Photovoltaic Power Forecasting Model With the Assistance of Aerosol Index Data

Liu

Fang

Zhang

et al. 2015

IEEE Trans. Sustain. Energy

304

121

View full text Add to dashboard Cite

Due to the intermittency and randomness of solar photovoltaic (PV) power, it is difficult for system operators to dispatch PV power stations. In order to find a precise expectation for day-ahead PV power generation, conventional models have taken into consideration the temperature, humidity, and wind speed data for forecasting, but these predictions were always not accurate enough under extreme weather conditions. Aerosol index (AI), which indicates the particulate matter in the atmosphere, has been found to have strong linear correlation with solar radiation attenuation, and might have potential influence on the power generated by PV panels. A novel PV power forecasting model is proposed in this paper, considering AI data as an additional input parameter. Based on seasonal weather classification, the back propagation (BP) artificial neural network (ANN) approach is utilized to forecast the next 24-h PV power outputs. The estimated results of the proposed PV power forecasting model coincide well with measurement data, and the proposed model has shown the ability of improving prediction accuracy, compared with conventional methods using ANN.Index Terms-Aerosol index (AI), artificial neural network (ANN) method, back propagation (BP) network, maximum absolute prediction error, photovoltaic (PV) power forecasting.

show abstract

Section: B Definition Of Aerosol Index (Ai) In Meteorology Areamentioning

confidence: 99%

An Improved Photovoltaic Power Forecasting Model With the Assistance of Aerosol Index Data

Liu

Fang

Zhang

et al. 2015

IEEE Trans. Sustain. Energy

304

121

View full text Add to dashboard Cite

show abstract

“…in addition to this, a cloud identification system was developed where the sky camera images were processed according to a sky classification [12], thus solving the saturation problem in the solar area [13]. By combining both satellite and sky camera imagery, a cloudiness forecast was made for the short-and medium-term [14], where a user interface provided cloudiness forecasting in real-time [15].…”

Section: Introductionmentioning

confidence: 99%

Solar radiation forecasting in the short- and medium-term under all sky conditions

Alonso-Montesinos

Batlles

2015

Energy

Self Cite

View full text Add to dashboard Cite

“…Practically all the errors occurred when the SYNOP report presented an overcast sky from low or medium clouds, and the TAN classifier (using MSG satellite images) detected a cloud in a higher layer than the SYNOP report. This feature occurs in many situations, as shown in a recent published work [25], where the authors noted that the satellite did not see medium or low clouds in the presence of high clouds indeed, these situations occurred in more than 94% of cases in which high clouds were detected. If the human observer has 8 oktas of low/medium clouds, it is not possible to see above to higher cloud layers.…”

Section: Resultsmentioning

confidence: 61%

“…The importance of such cloud identification allows us to reproduce the motion of clouds observed using cloud motion vectors. This objective was presented in a study, where satellite and sky camera imagery where combined to make a cloudiness forecast for the short-and medium-term [25] with satisfactory results. The cloudiness forecast was represented using a real-time GUI designed especially for solar power plant operators, providing useful information about the cloud presence over a solar field in near time horizons [26].…”

Section: Introductionmentioning

confidence: 99%

The application of Bayesian network classifiers to cloud classification in satellite images

et al. 2016

Self Cite

View full text Add to dashboard Cite

a b s t r a c tThe need to reduce the impact of traditional electricity generation necessitates an increase in the optimization of alternative systems that produce less environmental contamination. Renewables play a key role, with solar energy considered one of the most important energy supply sources. Solar power plants have to be perfectly designed to optimize electricity generation, and their placement must be as suitable as possible for the meteorological conditions. Clouds are the most mitigating factor in solar energy production and their study is decisive in locating the plant. Apart from the importance of studying clouds before building the solar plants, cloud detection is equally decisive in adapting plant operation to cloud types during solar power plant operation.This adaptation benefits plant performance and allows electricity management to be integrated into the electricity grid. Nonetheless, the majority of cloud studies determine atmospheric parameters, which are sometimes not available. In this work, we have developed an automatic, fully-exportable cloud classification model, where Bayesian network classifiers were applied to satellite images so as to determine the presence of clouds, classifying the sky as cloudless or with high, medium and low cloud presence. There was an average success probability of 90% for all sky conditions.

show abstract

Short and medium-term cloudiness forecasting using remote sensing techniques and sky camera imagery

Cited by 35 publications

References 28 publications

An Improved Photovoltaic Power Forecasting Model With the Assistance of Aerosol Index Data

An Improved Photovoltaic Power Forecasting Model With the Assistance of Aerosol Index Data

Solar radiation forecasting in the short- and medium-term under all sky conditions

The application of Bayesian network classifiers to cloud classification in satellite images

Contact Info

Product

Resources

About