52nd IEEE Conference on Decision and Control 2013
DOI: 10.1109/cdc.2013.6760012
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Estimation of a simple model of solar power generation using partial information

Abstract: This paper presents a heuristic method for the estimation of a model of a photovoltaic plant to be used for power generation forecasting purposes. The problem is addressed in a scenario where measurements of meteorological variables (i.e., irradiance and temperature) at the plant site are not available. The proposed approach efficiently exploits only power generation measurements and theoretical clear-sky irradiance, and is characterized by very low computational effort. A real-world application is presented t… Show more

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Cited by 4 publications
(5 citation statements)
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“…The information contained in such portions is then exploited in a recursive parameter estimation algorithm in combination with theoretical clear-sky irradiance provided by a suitable model. The method proposed in this paper improves over [26,27,29], since it is able to adapt to parameter variations and requires the tuning of a single threshold coefficient whose physical role can be interpreted in terms of the cloud cover factor (CCF) [31].…”
Section: Paper Contributionmentioning
confidence: 99%
See 3 more Smart Citations
“…The information contained in such portions is then exploited in a recursive parameter estimation algorithm in combination with theoretical clear-sky irradiance provided by a suitable model. The method proposed in this paper improves over [26,27,29], since it is able to adapt to parameter variations and requires the tuning of a single threshold coefficient whose physical role can be interpreted in terms of the cloud cover factor (CCF) [31].…”
Section: Paper Contributionmentioning
confidence: 99%
“…• As previously stated, a good guess for the main power/irradiance gain µ 1 is represented bŷ µ 1 (0) = P nom /1000, where P nom denotes the nominal plant power [24,27]. As pointed out in Remark 1, it is appropriate to start with an underestimate of this value, e.g., 75%, to ensure faster parameter adaptation.…”
Section: Model Estimationmentioning
confidence: 99%
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“…In [19,20], two methods are proposed to estimate the parameters of the PVUSA model [21] of a PV plant via a recursive framework based only on measures of generated power and temperature forecasts. This solution is not always data-efficient, since generation data collected during cloudy days are not exploited.…”
Section: Introductionmentioning
confidence: 99%