Examination of Correction Method of Long-term Solar Radiation Forecasts of Numerical Weather Prediction

Ueshima, Miki; Babasaki, Tadatoshi; Yuasa, Kazufumi; Omura, Ichiro

doi:10.1109/icrera47325.2019.8997070

Cited by 6 publications

(3 citation statements)

References 5 publications

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“…PV power generation is highly dependent on weather conditions [92]. Then, prediction techniques are essential to reduce the unbalance between expected power and real power generation, and support power system operation [86,89]. Forecasting models can be classified in two categories: indirect and direct models.…”

Section: Power Forecastingmentioning

confidence: 99%

“…Thus, an additional term is included to improve the results. This function is implemented in [89,91]. In [91], the errors are reduced with a term result of a deviation analysis by using the cross-validation for historical data.…”

mentioning

confidence: 99%

“…In [91], the errors are reduced with a term result of a deviation analysis by using the cross-validation for historical data. In [89], a method for adjusting the solar radiation forecast from a numerical weather prediction model is presented, based on the tendency of past error occurrence.…”

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confidence: 99%

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A Review of Control Techniques in Photovoltaic Systems

et al. 2020

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Complex control structures are required for the operation of photovoltaic electrical energy systems. In this paper, a general review of the controllers used for photovoltaic systems is presented. This review is based on the most recent papers presented in the literature. The control architectures considered are complex hybrid systems that combine classical and modern techniques, such as artificial intelligence and statistical models. The main contribution of this paper is the synthesis of a generalized control structure and the identification of the latest trends. The main findings are summarized in the development of increasingly robust controllers for operation with improved efficiency, power quality, stability, safety, and economics.

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Section: Power Forecastingmentioning

confidence: 99%

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confidence: 99%