Abstract
Radiation forecast accounting for daily and instantaneous variability was pursued by means of a new bi-parametric statistical model that builds on a model previously proposed by the same authors. The statistical model is developed with direct reference to the Liu-Jordan clear sky theoretical expression but is not bound by a specific clear sky model; it accounts separately for the mean daily variability and for the variation of solar irradiance during the day by means of two corrective parameters. This new proposal allows for a better understanding of the physical phenomena and improves the effectiveness of statistical characterization and subsequent simulation of the introduced parameters to generate a synthetic solar irradiance time series. Furthermore, the analysis of the experimental distributions of the two parameters’ data was developed, obtaining opportune fittings by means of parametric analytical distributions or mixtures of more than one distribution. Finally, the model was further improved toward the inclusion of weather prediction information in the solar irradiance forecasting stage, from the perspective of overcoming the limitations of purely statistical approaches and implementing a new tool in the frame of solar irradiance prediction accounting for weather predictions over different time horizons.
Highlights
Many photovoltaic (PV) applications, such as the sizing of stand-alone microgrids or sizing of energy storage systems for their inclusion in standalone systems or day-ahead market offering, require forecasting PV production variability along different time scenarios, often short and very short terms
The statistical model is developed with direct reference to the Liu-Jordan clear sky theoretical expression but is not bound by specific clear sky models; it accounts separately for the mean daily variability and for variation of solar irradiance during the day by means of two corrective parameters
The first parameter accounts for the mean daily variability, while the second accounts for the variation in solar irradiance during the day. This new proposal allows for a better understanding of the physical phenomena and improves the effectiveness of statistical characterization and subsequent simulation of the introduced parameters to generate synthetic solar irradiance time series, starting from parametric analytical distributions
Summary
Many photovoltaic (PV) applications, such as the sizing of stand-alone microgrids or sizing of energy storage systems for their inclusion in standalone (or grid connected) systems or day-ahead market offering, require forecasting PV production variability along different time scenarios, often short and very short terms. (1) numerical weather prediction (NWP) models that infer local cloud information through the dynamic modeling of the atmosphere up to several days ahead [1]; (2) models using satellite remote sensing or ground-based sky measurements to infer the motion of clouds and project their impact in the future; (3) statistical time series models based on measured irradiance data applied for very short term forecasting in the range of minutes to hours [2,3,4,5,6,7,8,9,10,11,12,13] In this context, daily variability of solar irradiance and its statistical characterization (and forecasting) is important.
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