Irradiance Forecasting for the Power Prediction of Grid-Connected Photovoltaic Systems

By E. Lorenz, Johannes Hurka, D. Heinemann, Hans-Georg Beyer. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2009

705 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Atmospheric Science, Global and Planetary Change

View Paper: https://www.semanticscholar.org/paper/00130488c6fa6a7c2e31208a8c7b415cabcc3666

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Atmospheric Science

In Global and Planetary Change

Citations in the corpus

Online short-term solar power forecasting (2009, background): “The need for efficient forecasting methods is evident and the research activities within the topic is increasing, see for example [1], [2], [3], and [4].”
Nearest-neighbor methodology for prediction of intra-hour global horizontal and direct normal irradiances (2015, neutral): “[6,12,16,20,24].”
Photovoltaic and solar power forecasting for smart grid energy management (2015, methodology): “The short term forecasts of solar irradiance up to 6-h ahead is conducted based on Meteosate satellite images [46].”
Proposed Metric for Evaluation of Solar Forecasting Models (2013, background): “[19], the “relative” RMSE (rRMSE) for one location are found to be on the order 36%, where as for an ensemble of sites the rRMSE is dramatically reduced to 13% for one-day ahead forecasts.”
An analog ensemble for short-term probabilistic solar power forecast (2015, neutral): “In [27] a normal distribution with zero mean and a standard deviation dependent on the solar zenith angle and the cloud situation is assumed to describe forecast errors of GHI.”
Short-Term Solar Irradiance Forecasting Model Based on Artificial Neural Network Using Statistical Feature Parameters (2012, background): “The errors of solar irradiance forecast based on NWP vary significantly and depend on the different climate and dynamic motion of the atmosphere at the study location [10–12].”
Uncertainty-Aware Household Appliance Scheduling Considering Dynamic Electricity Pricing in Smart Home (2013, background): “It is shown that the two-stage IncCond [41], [42] and the current sweep [43] MPPT methods are appropriate for residential areas, which can be further combined with the irradiance forecasting scheme presented in [44] to estimate the energy output.”
Forecasting of global and direct solar irradiance using stochastic learning methods, ground experiments and the NWS database (2011, methodology): “In Lorenz et al. (2009), s(x) was modeled using a fourth degree polynomial using the predicted clearness index and cloud cover.”
Solar Power Prediction Using Interval Type-2 TSK Modeling (2013, background): “[7] provided an approach for irradiance forecasting based on the assumption that a photovoltaic (PV) system is primarily influenced by solar irradiance.”
Generation and evaluation of space–time trajectories of photovoltaic power (2016, background): “There are handful practices on probabilistic forecasts of PV generation available in the literature [6, 7, 8, 9].”
Intra-hour DNI forecasting based on cloud tracking image analysis (2013, methodology): “For the forecasting horizon of same-day to a few days ahead, NWP models have been evaluated in various locations (Perez et al., 2010; Lorenz et al., 2009; Marquez and Coimbra, 2011; Mathiesen and Kleissl, 2011; Changsong et al., 2011).”
Comparison of numerical weather prediction solar irradiance forecasts in the US, Canada and Europe (2013, methodology): “During the last years the contribution of solar power to the electricity supply has been increasing fast leading to a strong need for accurate solar power predictions (in Germany, for instance, the PV production already exceeds 40% of electrical demand on sunny summer days).”
Review of photovoltaic power forecasting (2016, methodology): “…(2015), Alessandrini et al. (2015), Bessa et al. (2015), Monteiro et al. (2013b), Zamo et al. (2014b), Zhang et al. (2015a), Bracale et al. (2013), Lorenz et al. (2009), Fonseca et al. (2015), Huang and Perry (2015), Nagy et al. (2016), Jafarzadeh et al. (2013), AlHakeem et al. (2015), Rana et…”