ANN based prognostication of the PV panel output power under various environmental conditions

Refaat, Shady S.; Abu-Rub, Omar H.; Nounou, Hazem

doi:10.1109/tpec.2018.8312051

Cited by 4 publications

(1 citation statement)

References 28 publications

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“…This method can be implemented in our model to estimate solar irradiance. Refaat et al 9 developed a reliable relationship between the PV system power output and efficiency and various environmental factors using a layer recurrent neural network. The study identified the influence of climate variability on the power output and efficiency of PV modules without any physical definitions.…”

Section: Introductionmentioning

confidence: 99%

Application of parallel Elman neural network to hourly area solar PV plant generation estimation

Cho

Chang

Huang

2020

Int Trans Electr Energ Syst

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Summary Based on existing power generation data, an hourly area solar power estimation model using the parallel Elman neural network with solar radiation and system conversion efficiency is proposed. The accuracy and reliability of the assessment were verified using the information/data of solar photovoltaic power stations in various regions and timescales. Using the established appraisal algorithm involving K‐means evaluation and inverse distance weighting, regional forecasting of solar power generation was achieved. The prediction accuracy was also investigated using the actual details of the photovoltaic power stations. The results of the proposed model can assist the electricity dispatcher to not only precisely monitor the trend of solar power generation in different areas, but also coordinate with traditional power plants to meet the load demand more accurately. The proposed method can benefit power dispatching involving a larger scale of intermittent and unstable solar power electricity in the future.

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Section: Introductionmentioning

confidence: 99%

Application of parallel Elman neural network to hourly area solar PV plant generation estimation

Cho

Chang

Huang

2020

Int Trans Electr Energ Syst

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Machine Learning Based Prediction of Solar Power Plant Performance Under the Impact of Natural Dust Accumulation

Elaprolu

Bhattacharjee

2023

Lecture Notes in Electrical Engineering

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An Approach for Multiparameter Meteorological Forecasts

2018

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Accurate meteorological forecasting has great importance in different fields. This works introduces a system to obtain precise predictions, which uses regression functions, and collected data using the meteorological stations from the Gran Canaria and South Tenerife airports. The dataset offers information about different phenomena as temperature, wind speed, solar radiation, pressure, moisture, cloudiness, rainfall and meteors. A preprocessing stage has been applied before prediction stage to adapt the collected data. A support vector machine, regression tree, and fit linear model are applied as regression functions. Results has been measured by the mean square error. These results reached an accuracy of 0.07 °C for temperature, 0.56 km/h for wind speed, 7.45 tenths of kJ/m2 for solar radiation and 0.11 mm for precipitation. It shows the robustness of the multiparameter meteorological forecast approach.

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ANN based prognostication of the PV panel output power under various environmental conditions

Cited by 4 publications

References 28 publications

Application of parallel Elman neural network to hourly area solar PV plant generation estimation

Application of parallel Elman neural network to hourly area solar PV plant generation estimation

Machine Learning Based Prediction of Solar Power Plant Performance Under the Impact of Natural Dust Accumulation

An Approach for Multiparameter Meteorological Forecasts

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