Validation of short and medium term operational solar radiation forecasts in the US

Perez, Richard; Kivalov, Sergey; Schlemmer, James; Hemker, Karl; Renné, D.; Hoff, Thomas

doi:10.1016/j.solener.2010.08.014

Cited by 392 publications

(194 citation statements)

References 6 publications

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“…The correlation coefficient is used to test the relationship between forecasted and true values [3,40]; this index can be obtained from the following equation:…”

Section: Correlation Coefficientmentioning

confidence: 99%

“…Accurate solar forecasting facilitates the integration of solar generation into the grid by reducing the integration and operational costs associated with solar intermittencies [2]. Nowadays, physicallybased forecasting is mainly conducted using numerical weather prediction methods such as the well-known numerical weather prediction (NWP), which provides information up to several days ahead; however, it has obvious biases and random errors [3]. It is therefore very important to develop effective solar radiation forecasting methods, especially ones that can use less measurements.…”

Section: Introductionmentioning

confidence: 99%

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Hourly Solar Radiation Forecasting Using a Volterra-Least Squares Support Vector Machine Model Combined with Signal Decomposition

et al. 2018

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Accurate solar forecasting facilitates the integration of solar generation into the grid by reducing the integration and operational costs associated with solar intermittencies. A novel solar radiation forecasting method was proposed in this paper, which uses two kinds of adaptive single decomposition algorithm, namely, empirical mode decomposition (EMD) and local mean decomposition (LMD), to decompose the strong non-stationary solar radiation sequence into a set of simpler components. The least squares support vector machine (LSSVM) and the Volterra model were employed to build forecasting sub-models for high-frequency components and low-frequency components, respectively, and the sub-forecasting results of each component were superimposed to obtain the final forecast results. The historical solar radiation data collected on Golden (CO, USA), in 2014 were used to evaluate the accuracy of the proposed model and its comparison with that of the ARIMA, the persistent model. The comparison demonstrated that the superior performance of the proposed hybrid method.

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“…The correlation coefficient is used to test the relationship between forecasted and true values [3,40]; this index can be obtained from the following equation:…”

Section: Correlation Coefficientmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hourly Solar Radiation Forecasting Using a Volterra-Least Squares Support Vector Machine Model Combined with Signal Decomposition

et al. 2018

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“…As mentioned above, we used in this work data retrieved from the IFS model maintained and ran by ECMWF. Although NWP models outperform forecasts based on satellite data or ground measurements (only) for horizons longer than 5 h [20], they provide useful information as exogenous inputs. IFS provides hourly GHI data with a spatial resolution of 0.125 • × 0.125 • (approximately 14 km × 14 km; see Figure 2).…”

Section: Ecmwf Day-ahead Forecastsmentioning

confidence: 99%

Probabilistic Solar Forecasting Using Quantile Regression Models

Lauret¹,

David²,

Pedro

2017

Energies

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Abstract:In this work, we assess the performance of three probabilistic models for intra-day solar forecasting. More precisely, a linear quantile regression method is used to build three models for generating 1 h-6 h-ahead probabilistic forecasts. Our approach is applied to forecasting solar irradiance at a site experiencing highly variable sky conditions using the historical ground observations of solar irradiance as endogenous inputs and day-ahead forecasts as exogenous inputs. Day-ahead irradiance forecasts are obtained from the Integrated Forecast System (IFS), a Numerical Weather Prediction (NWP) model maintained by the European Center for Medium-Range Weather Forecast (ECMWF). Several metrics, mainly originated from the weather forecasting community, are used to evaluate the performance of the probabilistic forecasts. The results demonstrated that the NWP exogenous inputs improve the quality of the intra-day probabilistic forecasts. The analysis considered two locations with very dissimilar solar variability. Comparison between the two locations highlighted that the statistical performance of the probabilistic models depends on the local sky conditions.

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“…In Perez et al [42] the author presents a temporal partitioning of solar irradiation prediction where 1-6 h ahead is considered to be short term and up to 6 day ahead is a medium term prediction. This paper also presents Numerical Weather Prediction (NWP) as used for the National Digital Forecast Database [43] being more accurate than other short term methods once the forecast horizon is greater than 3-4 h.…”

Section: Models For Solar Irradiationmentioning

confidence: 99%

Optimisation of Storage for Concentrated Solar Power Plants

et al. 2014

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The proliferation of non-scheduled generation from renewable electrical energy sources such concentrated solar power (CSP) presents a need for enabling scheduled generation by incorporating energy storage; either via directly coupled Thermal Energy Storage (TES) or Electrical Storage Systems (ESS) distributed within the electrical network or grid. The challenges for 100% renewable energy generation are: to minimise capitalisation cost and to maximise energy dispatch capacity. The aims of this review article are twofold: to review storage technologies and to survey the most appropriate optimisation techniques to determine optimal operation and size of storage of a system to operate in the Australian National Energy Market (NEM). Storage technologies are reviewed to establish indicative characterisations of energy density, conversion efficiency, charge/discharge rates and costings. A partitioning of optimisation techniques based on methods most appropriate for various time scales is performed: from "whole of year", seasonal, monthly, weekly and daily averaging to those best suited matching the NEM bid timing of five minute dispatch bidding, averaged on the half hour as the trading settlement spot price. Finally, a selection of the most promising research directions and methods to determine the optimal operation and sizing of storage for renewables in the grid is presented.

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Validation of short and medium term operational solar radiation forecasts in the US

Cited by 392 publications

References 6 publications

Hourly Solar Radiation Forecasting Using a Volterra-Least Squares Support Vector Machine Model Combined with Signal Decomposition

Hourly Solar Radiation Forecasting Using a Volterra-Least Squares Support Vector Machine Model Combined with Signal Decomposition

Probabilistic Solar Forecasting Using Quantile Regression Models

Optimisation of Storage for Concentrated Solar Power Plants

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