scholarly journals Forecast Horizon and Solar Variability Influences on the Performances of Multiscale Hybrid Forecast Model

Energies ◽  
2019 ◽  
Vol 12 (12) ◽  
pp. 2264 ◽  
Author(s):  
Stéphanie Monjoly ◽  
Maina André ◽  
Rudy Calif ◽  
Ted Soubdhan
Keyword(s):  
Time Scales ◽  
Predictive Performance ◽  
Forecast Model ◽  
Decomposition Methods ◽  
Solar Forecasting ◽  
Forecast Horizon ◽  
Clear Sky ◽  
Modeling Process ◽  
Resource Variability ◽  
Solar Resource

The tropical insular region is characterized by a large diversity of microclimates and land/sea contrasts, creating a challenging solar forecasting. Therefore, it is necessary to develop and use performant and robustness forecasting techniques. This paper examines the predictive performance of a novel solar forecasting approach, the multiscale hybrid forecast model (MHFM), as a function of several parameters. The MHFM model is a technique recently used for irradiance forecasting based on a hybrid autoregressive (AR) and neural network (NN) model combined with multiscale decomposition methods. This technique presents a relevant performance for 1 h ahead global horizontal irradiance forecast. The goal of this work is to highlight the strength and limits of this model by assessing the influence of different parameters from a metric error analysis. This study illustrates modeling process performance as a function of daily insolation conditions and testifies the influence of learning data and test data time scales. Several forecast horizon strategies and their influence on the MHFM performance were investigated. With the best strategy, a rRMSE value from 4.43 % to 10.24 % was obtained for forecast horizons from 5 min to 6 h. The analysis of intra-day solar resource variability showed that the best performance of MHFM was obtained for clear sky days with a rRMSE of 2.91 % and worst for cloudy sky days with a rRMSE of 6.73 % . These works constitute an additional analysis in agreement with the literature about influence of daily insolation conditions and horizons time scales on modeling process.

scholarly journals Forecast Horizon and Solar Variability Influences on the Performances of Multiscale Hybrid Forecast Model (MHFM)

2018 ◽  
Author(s):  
Stéphanie Monjoly ◽  
Rudy Calif ◽  
Maina André ◽  
Ted Soubdhan
Keyword(s):  
Forecast Model ◽  
Decomposition Methods ◽  
Solar Variability ◽  
Forecast Horizon ◽  
Multiscale Decomposition ◽  
Clear Sky ◽  
Model Based

In this paper, the forecast horizon and solar variability influences on MHFM model based on multiscale decomposition, AR and NN models, are studied. This article follows the works published in [1] showing the performance of the MHFM using 3 multiscale decomposition methods and a forecast horizon equal to 1 hour. Several forecast horizon strategies and his influence on the MHFM performances are investigated. We show that the best strategy for a rRMSE variying from $4.43\%$ to $10.24\%$ is obtained for forecast horizons from $5$ minutes to $6$ hours. In a second part, the solar variability influence on the MHFM is studied. A classification based on a shows that the best performance of MHFM is obtained for clear sky days with a rRMSE of $2.91\%$ and worst for cloudy sky days with a rRMSE of $6.73\%$.

scholarly journals A New Approach for Satellite-Based Probabilistic Solar Forecasting with Cloud Motion Vectors

Energies ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 4951
Author(s):  
Thomas Carrière ◽  
Rodrigo Amaro e Silva ◽  
Fuqiang Zhuang ◽  
Yves-Marie Saint-Drenan ◽  
Philippe Blanc
Keyword(s):  
Prediction Interval ◽  
Solar Forecasting ◽  
Forecast Horizon ◽  
Motion Vectors ◽  
Forecast Uncertainty ◽  
Average Width ◽  
Clear Sky ◽  
Cloud Motion ◽  
Proposed Model ◽  
The One

Probabilistic solar forecasting is an issue of growing relevance for the integration of photovoltaic (PV) energy. However, for short-term applications, estimating the forecast uncertainty is challenging and usually delegated to statistical models. To address this limitation, the present work proposes an approach which combines physical and statistical foundations and leverages on satellite-derived clear-sky index (kc) and cloud motion vectors (CMV), both traditionally used for deterministic forecasting. The forecast uncertainty is estimated by using the CMV in a different way than the one generally used by standard CMV-based forecasting approach and by implementing an ensemble approach based on a Gaussian noise-adding step to both the kc and the CMV estimations. Using 15-min average ground-measured Global Horizontal Irradiance (GHI) data for two locations in France as reference, the proposed model shows to largely surpass the baseline probabilistic forecast Complete History Persistence Ensemble (CH-PeEn), reducing the Continuous Ranked Probability Score (CRPS) between 37% and 62%, depending on the forecast horizon. Results also show that this is mainly driven by improving the model’s sharpness, which was measured using the Prediction Interval Normalized Average Width (PINAW) metric.

Effects of solar resource variability on the future Florida transmission and distribution system

PES T&D 2012 ◽  
2012 ◽  
Author(s):  
David K. Click ◽  
Houtan Moaveni ◽  
Kristopher O. Davis ◽  
Richard H. Meeker ◽  
Robert M. Reedy ◽  
...  
Keyword(s):  
Distribution System ◽  
The Future ◽  
Resource Variability ◽  
Solar Resource ◽  
Transmission And Distribution

scholarly journals Assessment and Day-Ahead Forecasting of Hourly Solar Radiation in Medellín, Colombia

Energies ◽  
2019 ◽  
Vol 12 (22) ◽  
pp. 4402
Author(s):  
Julián Urrego-Ortiz ◽  
J. Alejandro Martínez ◽  
Paola A. Arias ◽  
Álvaro Jaramillo-Duque
Keyword(s):  
Solar Radiation ◽  
Markov Model ◽  
Power Plants ◽  
Wrf Model ◽  
Tropical Andes ◽  
Clear Sky ◽  
Proposed Model ◽  
Solar Power Plants ◽  
Solar Resource ◽  
Sky Conditions

The description and forecasting of hourly solar resource is fundamental for the operation of solar energy systems in the electric grid. In this work, we provide insights regarding the hourly variation of the global horizontal irradiance in Medellín, Colombia, a large urban area within the tropical Andes. We propose a model based on Markov chains for forecasting the hourly solar irradiance for one day ahead. The Markov model was compared against estimates produced by different configurations of the weather research forecasting model (WRF). Our assessment showed that for the period considered, the average availability of the solar resource was of 5 PSH (peak sun hours), corresponding to an average daily radiation of ~5 kWh/m2. This shows that Medellín, Colombia, has a substantial availability of the solar resource that can be a complementary source of energy during the dry season periods. In the case of the Markov model, the estimates exhibited typical root mean squared errors between ~80 W/m2 and ~170 W/m2 (~50%–~110%) under overcast conditions, and ~57 W/m2 to ~171 W/m2 (~16%–~38%) for clear sky conditions. In general, the proposed model had a performance comparable with the WRF model, while presenting a computationally inexpensive alternative to forecast hourly solar radiation one day in advance. The Markov model is presented as an alternative to estimate time series that can be used in energy markets by agents and power-system operators to deal with the uncertainty of solar power plants.

scholarly journals Analysis of Long-Term Solar Resource Variability Using NSRDB Version 3

2021 ◽  
Author(s):  
Manajit Sengupta ◽  
Aron Habte
Keyword(s):  
Solar Radiation ◽  
Spatial Variability ◽  
Temporal Variability ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Spatial And Temporal Variability ◽  
Cloud Optical Depth ◽  
Resource Variability ◽  
Solar Resource

<p>Understanding long-term solar resource variability is essential for planning and deployment of solar energy systems. These variabilities occur due to deterministic effects such as sun cycle and nondeterministic such as complex weather patterns. The NREL’s National Solar Radiation Database (NSRDB) provides long term solar resource data covering 1998- 2019 containing more than 2 million pixels over the Americas and gets updated on an annual basis. This dataset is satellite-based and uses a two-step physical model for it’s development. In the first step we retrieve cloud properties such as cloud mask, cloud type, cloud optical depth and effective radius. The second step uses a fast radiative transfer model to compute solar radiation.  This dataset is ideal for studying solar resource variability. For this study, NSRDB version 3 which contains data from 1998-2017 on a half hourly and 4x4 km temporal and spatial resolution was used. The study analyzed the spatial and temporal trend of solar resource of global horizontal irradiance (GHI) and direct normal irradiance (DNI) using long-term 20-years NSRDB data. The coefficient of variation (COV) was used to analyze the spatio-temporal interannual and seasonal variabilities. The spatial variability was analyzed by comparing the center pixel to neighboring pixels. The spatial variability result showed higher COV as the number of neighboring pixels increased. Similarly, the temporal variability for the NSRDB domain ranges on average from ±10% for GHI and ±20% for DNI. Furthermore, the long-term variabilities were also analyzed using the Köppen-Geiger climate classification. This assisted in the interpretation of the result by reducing the originally large number of pixels into a smaller number of groups. This presentation will provided a unique look at long-term spatial and temporal variability of solar radiation using high-resolution satellite-based datasets.</p>

Uncertainty in Predicting the Start-Up Time and Losses for a High Temperature Particle Receiver due to Solar Resource Variability

2020 ◽  
Author(s):  
Muhammad M. Rafique ◽  
Graham Nathan ◽  
Woei Saw
Keyword(s):  
Steady State ◽  
High Temperature ◽  
Climatic Conditions ◽  
Heat Losses ◽  
Practical Implementation ◽  
Constant Input ◽  
Transient Model ◽  
Start Up ◽  
Resource Variability ◽  
Solar Resource

Abstract In this paper, the effect of solar resource variability has been assessed on the start-up time and different heat transfer phenomena associated with a high temperature particle receiver. The receiver analyzed in this study has a cylindrical cavity made of three different layers in order to have good absorption, higher durability and lower thermal heat losses. A detailed transient mathematical model is developed, considering the input solar energy to the receiver aperture and all heat losses from the receiver cavity. The developed transient model is employed to study the time required to achieve a receiver start-up temperature from room temperature to 1000°C, under steady-state and transient operation, for the climatic conditions of Pinjarra, Australia. Furthermore, the total energy gain by the receiver and associated heat losses including re-radiation, convection, and conduction have been accounted for, with and without considering the solar resource variability. The results revealed that an uncertainty of about 40% exists in the prediction of the receiver start-up time and associated heat losses during the start-up period under steady state operation, with a constant input heat flux. This uncertainty in the prediction of the receiver start-up time and losses will directly affect the overall performance and design of the receiver, which will result in unscheduled disruption of the industrial process. This indicates a need to analyse the performance of high temperature particle receivers under transient conditions, considering the solar resource variability for practical implementation of this technology to different processes. This will help to investigate better control strategies for the inflow of particles, based on the real-time climatic conditions, to achieve better thermal performance.

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