Solar Power Forecasting: A Review

K., D.; Isha, I

doi:10.5120/ijca2016910728

Cited by 24 publications

(2 citation statements)

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“…Skikda is characterized by its Mediterranean climate marked by wet, mild winter and dry and clear summer. According to several works, the time series prediction of PV power on an hourly average basis is more accurate for medium-term forecasters [27,28]. However, averaging data may obscure some information like peaks occurring in the instantaneous irradiance curve as a result of partial or total shading.…”

Section: Weather Characteristics Of the Selected Location And Data Collectionmentioning

confidence: 99%

Effect of Environmental Conditions and Training Algorithms on the Efficiency of a NARX Based Approach to Predict PV Panel Power Output

2020

IJEM

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Photovoltaic energy is volatile in nature since it depends on weather conditions. It is important to have an idea about the reliability and the economic feasibility of any new project to decide whether it is right to proceed with the installation of such a project. Hence, it is becoming fundamental to know renewable energy state and production that can be combined with other less variable and more predictable sources to justify the choice of regions for the new photovoltaic projects installation. The current research investigates the forecasting abilities of a NARX based approach. The influence of the meteorological data, such as irradiance, ambient temperature, and wind speed, and the impact of training algorithms on the performance of the NARX-based forecaster is studied. For this purpose, four models are discussed, each model is trained based on three training algorithms. The NARX model using a Bayesian Regularization algorithm, trained by the three meteorological data as inputs and the converted power output as output, outperforms the other models. It consists of a simple architecture with one input layer, a hidden layer containing 1O neurons, and an output layer, with a mean square error of 0.0085 W2 for the training phase and 0.0043 W2 testing phase, and the overall regression of 95.48%. This simplified architecture and low values of the mean square error and the regression coefficient suggest that they are promising photovoltaic output prediction tools, particularly in locations where few meteorological parameters are monitored.

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Section: Weather Characteristics Of the Selected Location And Data Collectionmentioning

confidence: 99%

Effect of Environmental Conditions and Training Algorithms on the Efficiency of a NARX Based Approach to Predict PV Panel Power Output

2020

IJEM

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“…Ac-cuWeather uses the Global Forecast System (GFS) Numerical Weather Prediction (NWP) model to produce hourly-resolution forecasts of CC for the day ahead and is operated by the National Oceanic and Atmospheric Administration. The GFS model has a spatial domain of 28 km x 28 km, models 64 layers of the atmosphere, and is run every six hours to produce forecasts up to 180 hours ahead and every 12 hours for forecasts up to 384 hours ahead (Chaturvedi and Isha, 2016). The CC forecasts for Durban from AccuWeather are available at hourly-resolution for at least six hours ahead, producing a daily CC profile.…”

Section: Data 21 Cloud Covermentioning

confidence: 99%

Investigating diffuse irradiance variation under different cloud conditions in Durban, using k-means clustering

Govender

Sivakumar

2019

J. energy South. Afr.

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Diffuse irradiance is important for the operation of solar-powered devices such as photovoltaics, so it is important to analyse its behaviour under different sky conditions. The primary cause of short-term irradiance variability is clouds. One approach to analyse the diffuse irradiance variation is to use cluster analysis to group together days experiencing similar cloud patterns. A study was carried out to examine the application of k-means clustering to daily cloud data in Durban, South Africa (29.87 °S; 30.98 °E), which revealed four distinct day-time cloud cover (CC) patterns classified as Class I, II, III and IV, corresponding to cloudy, sunny, or a combination of the two. Diffuse irradiance was then correlated with each of the classes to establish corresponding diurnal irradiance patterns and the associated temporal variation. Class I had highest diffuse irradiance variation, followed by Classes III, IV and II. To further investigate the local cloud dynamics, cloud types were also analysed for Classes I−IV. It was found that stratocumulus (low cloud category); altocumulus translucidus, castellanus and altocumulus (middle cloud category); and cirrus fibrates and spissatus (high cloud category), were the most frequently occurring cloud types within the different classes. This study contributes to the understanding of the diurnal diffuse irradiance patterns under the four most frequently occurring CC conditions in Durban. Overall, knowledge of these CC and associated diffuse irradiance patterns is useful for solar plant operators to manage plant output where, depending on the CC condition, the use of back-up devices may be increased or reduced accordingly.

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