•A new method for short-term solar radiation forecasting is proposed and evaluated.•The method is based on advection and diffusion of MSG cloud index using WRF model.•Cloud index values are treated ...as model tracers with heights given by a ceilometer.•The method is more suitable in areas where cloudiness is not ruled by topography.•Most relevant results are found for DNI forecasts and partially cloudy conditions.
A new method for short-term solar radiation forecasting (referred to as Cloud Index Advection and Diffusion, CIADCast) is proposed and validated. The method is based on the advection and diffusion of Meteosat Second Generation (MSG) cloud index estimates using the Weather Research and Forecasting (WRF) numerical weather prediction (NWP) model. The forecasted cloud index is transformed in global horizontal irradiance (GHI) and direct normal irradiance (DNI) forecasts by means of the Heliosat-2 method. The cloud index maps are inserted in the WRF vertical layer which corresponds to the cloud height provided by a ceilometer. GHI and DNI are forecasted up to 6h ahead with 15min of time resolution. The method was tested using 25days of radiometric data collected at three stations located in southern Spain. Benchmarking models such as smart persistence, a cloud motion vector (CMV) based approach and the WRF-Solar suite of the WRF model are also evaluated. Results were analyzed in the light of the different topographic characteristics of the evaluation stations areas. Results proved that CIADCast is able to provide enhanced forecasts in areas with low topographic complexity, where cloud advection by the atmospheric mesoscale dynamics is not perturbed by mountain features. In these areas, CIADCast generally outperforms the other models, especially for DNI and partially cloudy conditions. On the other hand, in regions with complex topography, where the mesoscale cloud pattern is influenced by the mountains, the performance of the CIADCast model is poor and the use of persistence or the WRF-Solar model proved to be more appropriate.
This work proposes and evaluates methods for extending the forecasting horizon of all-sky imager (ASI)-based solar radiation nowcasts and estimating the uncertainty of these predictions. In addition, ...we evaluated procedures for improving the temporal resolution and latency of satellite-imagery-derived solar nowcasts. Based on these contributions, we assessed the reliability of ASIs and satellite-derived solar radiation nowcasts, with 1-min time-resolution and up-to-90-min ahead. The study was conducted in a location in Southern Spain using a set of cloudy days, specifically selected as representative of the most challenging conditions regarding solar radiation nowcasting. The results reveal that the use of ASI-based models provide low benefits compared to the use of satellite-based models for point solar radiation nowcasting. Given the frequency of occurrence of the different sky types in the study area, the results suggest that the use of a simple smart persistence algorithm, in combination with a low-resolution satellite nowcasting model could be an adequate choice, avoiding the challenges associated with the use of ASIs.
•New methods for extending forecasting horizon of sky camera based solar nowcasting.•Sky camera solar nowcasting uncertainty assessed.•One minute time resolution satellite-imagery model solar nowcasting.•Sky camera provides low added value compared to the use of satellite imagery.•Smart-Persistence plus satellite-imagery models as the best option for solar nowcasting.
This study presents a comparative analysis of the ordinary and residual kriging methods for mapping, on a 1
km
×
1
km grid size, the monthly mean of global solar radiation at the surface in Andalusia ...(southern Spain). The region of study is characterized by a wide range of topographic and climatic characteristic, which allows properly evaluating the two methods. The experimental dataset includes 4 years (2003–2006) of data collected at 166 stations: 112 stations were used to train the models and 54 in an independent validation procedure. Overall, the ordinary kriging method provide fair estimates: RMSE ranges from 1.63
MJ
m
−2
day
−1 (6.2%) in June to around 1.44
MJ
m
−2
day
−1 (11.2%) in October. In the residual kriging procedure, we propose using an external explanatory variable (derived just based on a digital elevation model) that accounts for topographic shadows cast, and that is able to explain between 13% and 45% of the spatial variability. Based on the combined used of the elevation and the former external variable, residual kriging estimates shows a relative improvement in RMSE values ranging from 5% in the summer months to more than 20% in the autumn and winter months. Particularly, RMSE is 1.44
MJ
m
−2
day
−1 (5.5%) in June and 1.31
MJ
m
−2
day
−1 (10.2%) in October. Explained variance also shows a considerable improvement compared to the ordinary kriging method, with all the months showing
R
2 values above 0.92. Results show that most part of these improvements is associated with a better estimation of the minimum values, particularly during the winter part of the year. It is finally concluded that the proposed residual kriging method is particularly valuable when mapping complex topography areas.
•Short-term (6 h ahead, 15 min) DNI and GHI forecasting.•Blending of four models based on Support Vector Regression.•General and horizon blending approaches.•Regional GHI and DNI short term ...forecasting.•Blending approaches improve the individual models for all horizons.
In this article we explore the blending of the four models (Satellite, WRF-Solar, Smart Persistence and CIADCast) studied in Part 1 by means of Support Vector Machines with the aim of improving GHI and DNI forecasts. Two blending approaches that use the four models as predictors have been studied: the horizon approach constructs a different blending model for each forecast horizon, while the general approach trains a single model valid for all horizons. The influence on the blending models of adding information about weather types is also studied. The approaches have been evaluated in the same four Iberian Peninsula stations of Part 1. Blending approaches have been extended to a regional context with the goal of obtaining improved regional forecasts. In general, results show that blending greatly outperforms the individual predictors, with no large differences between the blending approaches themselves. Horizon approaches were more suitable to minimize rRMSE and general approaches work better for rMAE. The relative improvement in rRMSE obtained by model blending was up to 17% for GHI (16% for DNI), and up to 15% for rMAE. Similar improvements were observed for the regional forecast. An analysis of performance depending on the horizon shows that while the advantage of blending for GHI remains more or less constant along horizons, it tends to increase with horizon for DNI, with the largest improvements occurring at 6 h. The knowledge of weather conditions helped to slightly improve further the forecasts (up to 3%), but only at some locations and for rRMSE.
Accurate solar radiation nowcasting models are critical for the integration of the increasing solar energy in power systems. This work explored the benefits obtained by the blending of four ...all-sky-imagers (ASI)-based models, two satellite-images-based models and a data-driven model. Two blending approaches (general and horizon) and two blending models (linear and random forest (RF)) were evaluated. The relative contribution of the different forecasting models in the blended-models-derived benefits was also explored. The study was conducted in Southern Spain; blending models provide one-minute resolution 90 min-ahead GHI and DNI forecasts. The results show that the general approach and the RF blending model present higher performance and provide enhanced forecasts. The improvement in rRMSE values obtained by model blending was up to 30% for GHI (40% for DNI), depending on the forecasting horizon. The greatest improvement was found at lead times between 15 and 30 min, and was negligible beyond 50 min. The results also show that blending models using only the data-driven model and the two satellite-images-based models (one using high resolution images and the other using low resolution images) perform similarly to blending models that used the ASI-based forecasts. Therefore, it was concluded that suitable model blending might prevent the use of expensive (and highly demanding, in terms of maintenance) ASI-based systems for point nowcasting.
The automatic and non-supervised detection of the planetary boundary layer height (zPBL) by means of lidar measurements was widely investigated during the last several years. Despite considerable ...advances, the experimental detection still presents difficulties such as advected aerosol layers coupled to the planetary boundary layer (PBL) which usually produces an overestimation of the zPBL. To improve the detection of the zPBL in these complex atmospheric situations, we present a new algorithm, called POLARIS (PBL height estimation based on lidar depolarisation). POLARIS applies the wavelet covariance transform (WCT) to the range-corrected signal (RCS) and to the perpendicular-to-parallel signal ratio (δ) profiles. Different candidates for zPBL are chosen and the selection is done based on the WCT applied to the RCS and δ. We use two ChArMEx (Chemistry-Aerosol Mediterranean Experiment) campaigns with lidar and microwave radiometer (MWR) measurements, conducted in 2012 and 2013, for the POLARIS' adjustment and validation. POLARIS improves the zPBL detection compared to previous methods based on lidar measurements, especially when an aerosol layer is coupled to the PBL. We also compare the zPBL provided by the Weather Research and Forecasting (WRF) numerical weather prediction (NWP) model with respect to the zPBL determined with POLARIS and the MWR under Saharan dust events. WRF underestimates the zPBL during daytime but agrees with the MWR during night-time. The zPBL provided by WRF shows a better temporal evolution compared to the MWR during daytime than during night-time.
Concentrating solar technologies, which are fuelled by the direct normal component of solar irradiance (DNI), are among the most promising solar technologies. Currently, the state-of the-art methods ...for DNI evaluation use datasets of aerosol optical depth (AOD) with only coarse (typically monthly) temporal resolution. Using daily AOD data from both site-specific observations at ground stations as well as gridded model estimates, a methodology is developed to evaluate how the calculated long-term DNI resource is affected by using AOD data averaged over periods from 1 to 30 days. It is demonstrated here that the use of monthly representations of AOD leads to systematic underestimations of the predicted long-term DNI up to 10% in some areas with high solar resource, which may result in detrimental consequences for the bankability of concentrating solar power projects. Recommendations for the use of either daily or monthly AOD data are provided on a geographical basis.
In the last years, there is an increasing interest for enhanced method for assessing and monitoring the level of the global horizontal irradiance (GHI) in photovoltaic (PV) systems, fostered by the ...massive deployment of this energy. Thermopile or photodiode pyranometers provide point measurements, which may not be adequate in cases when areal information is important (as for PV network or large PV plants monitoring). The use of All Sky Imagers paired convolutional neural networks, a powerful technique for estimation, has been proposed as a plausible alternative. In this work, a convolutional neural network architecture is presented to estimate solar irradiance from sets of ground-level Total Sky Images. This neural network is capable of combining images from three cameras. Results show that this approach is more accurate than using only images from a single camera. It has also been shown to improve the performance of two other approaches: a cloud fraction model and a feature extraction model.
The concept of a European super-grid for electricity presents clear advantages for a reliable and affordable renewable power production (photovoltaics and wind). Based on the mean-variance portfolio ...optimization analysis, we explore optimal scenarios for the allocation of new renewable capacity at national level in order to provide to energy decision-makers guidance about which regions should be mostly targeted to either maximize total production or reduce its day-to-day variability. The results show that the existing distribution of renewable generation capacity across Europe is far from optimal: i.e. a 'better' spatial distribution of resources could have been achieved with either a ~31% increase in mean power supply (for the same level of day-to-day variability) or a ~37.5% reduction in day-to-day variability (for the same level of mean productivity). Careful planning of additional increments in renewable capacity at the European level could, however, act to significantly ameliorate this deficiency. The choice of where to deploy resources depends, however, on the objective being pursued-if the goal is to maximize average output, then new capacity is best allocated in the countries with highest resources, whereas investment in additional capacity in a north/south dipole pattern across Europe would act to most reduce daily variations and thus decrease the day-to-day volatility of renewable power supply.
•Solar resource intra-day variability is explained based on few (3 or 4) modes.•Solar resource variability is mostly explained by regional weather patterns.•PV yield variability explained based on ...the proposed modes.
The intra-day modes of variability of the solar resources in the Iberian Peninsula, their associated weather patterns and their impact on the solar power output are assessed in this work. The analysis is performed for yearly and seasonal variability. Firstly, the modes of variability are identified by means of hierarchical cluster analysis. It is computed with two years of measured global horizontal irradiance (GHI) and direct normal irradiance (DNI) data gathered at four stations. Notably, three-hour statistics describing mean and variability of solar radiation are used as input to the cluster analysis. Secondly, synoptic weather patterns associated with each group resulting from the cluster analysis are assessed using sea level pressure and cloudiness data. Finally, the solar PV power yield associated with each mode is evaluated. The yearly analysis reveals the existence of four modes of variability of the solar resource in the study area. The four modes are shown to have a distinctive weather pattern and also specific impacts on solar power generation in the study area. Seasonal analyses show results similar to the annual analysis, but with marked seasonal differences.
PDF
