• Energy Research

Abstract Solar global irradiation is barely recorded in isolated rural areas around the world. Traditionally, solar resource estimation has been performed using parametric-empirical models based on the relationship of solar irradiation with other atmospheric and commonly measured variables, such as temperatures, rainfall, and sunshine duration, achieving a relatively high level of certainty. Considerable improvement in soft-computing techniques, which have been applied extensively in many research fields, has lead to improvements in solar global irradiation modeling, although most of these techniques lack spatial generalization. This new methodology proposes support vector machines for regression with optimized variable selection via genetic algorithms to generate non-locally dependent and accurate models. A case of study in Spain has demonstrated the value of this methodology. It achieved a striking reduction in the mean absolute error (MAE) – 41.4% and 19.9% – as compared to classic parametric models; Bristow & Campbell and Antonanzas-Torres et al., respectively.

This study examines the progress made by two new reanalyses in the estimation of surface irradiance: ERA5, the new global reanalysis from the ECMWF, and COSMO-REA6, the regional reanalysis from the DWD for Europe. Daily global horizontal irradiance data were evaluated with 41 BSRN stations worldwide, 294 stations in Europe, and two satellite-derived products (NSRDB and SARAH). ERA5 achieves a moderate positive bias worldwide and in Europe of +4.05 W/m2 and +4.54 W/m2 respectively, which entails a reduction in the average bias ranging from 50% to 75% compared to ERA-Interim and MERRA-2. This makes ERA5 comparable with satellite-derived products in terms of the mean bias in most inland stations, but ERA5 results degrade in coastal areas and mountains. The bias of ERA5 varies with the cloudiness, overestimating under cloudy conditions and slightly underestimating under clear-skies, which suggests a poor prediction of cloud patterns and leads to larger absolute errors than that of satellite-based products. In Europe, the regional COSMO-REA6 underestimates in most stations (MBE = 5.29 W/m2) showing the largest deviations under clear-sky conditions, which is most likely caused by the aerosol climatology used. Above 45°N the magnitude of the bias and absolute error of COSMO-REA6 are similar to ERA5 while it outperforms ERA5 in the coastal areas due to its high-resolution grid (6.2 km). We conclude that ERA5 and COSMO-REA6 have reduced the gap between reanalysis and satellite-based data, but further development is required in the prediction of clouds while the spatial grid of ERA5 (31 km) remains inadequate for places with high variability of surface irradiance (coasts and mountains). Satellite-based data should be still used when available, but having in mind their limitations, ERA5 is a valid alternative for situations in which satellite-based data are missing (polar regions and gaps in times series) while COSMO-REA6 complements ERA5 in Central and Northern Europe mitigating the limitations of ERA5 in coastal areas. © 2018 The Author(s)

Abstract This work presents a kind of smart baseline models for solar irradiation forecasting, as models are only fed with meteorological records and solar-computed values, easy-to-obtain inputs that facilitate their implementation worldwide. Global horizontal irradiation (GHI) is predicted for horizons of 1 h in a site of Southeast Spain. Two types of approaches are undertaken: fixed models, trained just once with a global database, and moving models, where the training database is updated based on the features of the testing sample. The approaches are implemented with two machine learning algorithms, support vector regression (SVR) and random forest (RFs), along with the classic linear regression and kNN. Besides, genetic algorithms (GAs) are used to automate the training process of fixed models, a task traditionally performed based on the experience or the researcher. Significant improvements were obtained over the basic persistence methods with both approaches. In the case of moving models, results proved that the best approach to update the calibration set was by computing the Euclidean distance in the principal components space. Results of both approaches were comparable in terms of MAE and forecast skill ( s ), though slightly superior predictions were obtained with the moving SVR, with a forecast skill ranging from 8% to 23% and a testing MAE ranging from 49 to 64 W / m 2 for the different states of cloudiness. Anyway, both approaches are valid baselines to compare new forecasting models fed with more difficult-to-obtain features, supplementing the classic but naive persistence models.

Assessing the world’s collective progress towards the Paris Agreement’s long-term goals is a global priority. Local authorities (LAs), in particular, play an important role in a just transition. This paper evaluates the real achievements of local climate action plans developed in Europe from 2008 to 2020 under the Covenant of Mayors initiative. On average, 85.6% of the GHG reduction targets were achieved way before the year 2020; however, our assessment shows different reduction patterns, with several leading LAs exceeding by 2–4 times their targets and 12% of LAs increasing their baseline emissions. This paper weighs the factors which have a determinant impact on these patterns, investigating the key drivers and barriers towards a clean energy transition under a new population-driven approach. While, for large LAs, the climate experience and the engagement of stakeholders is an asset for increasing their achievements, small LAs are much more conditioned by the political mandate and support from regional governments or external actors. The key factor for climate action planning appears to be the joint partnership between several government levels from a national perspective.

Abstract Variability of solar resource poses difficulties in grid management as solar penetration rates rise continuously. Thus, the task of solar power forecasting becomes crucial to ensure grid stability and to enable an optimal unit commitment and economical dispatch. Several forecast horizons can be identified, spanning from a few seconds to days or weeks ahead, as well as spatial horizons, from single site to regional forecasts. New techniques and approaches arise worldwide each year to improve accuracy of models with the ultimate goal of reducing uncertainty in the predictions. This paper appears with the aim of compiling a large part of the knowledge about solar power forecasting, focusing on the latest advancements and future trends. Firstly, the motivation to achieve an accurate forecast is presented with the analysis of the economic implications it may have. It is followed by a summary of the main techniques used to issue the predictions. Then, the benefits of point/regional forecasts and deterministic/probabilistic forecasts are discussed. It has been observed that most recent papers highlight the importance of probabilistic predictions and they incorporate an economic assessment of the impact of the accuracy of the forecasts on the grid. Later on, a classification of authors according to forecast horizons and origin of inputs is presented, which represents the most up-to-date compilation of solar power forecasting studies. Finally, all the different metrics used by the researchers have been collected and some remarks for enabling a fair comparison among studies have been stated.

Different types of measuring errors can increase the uncertainty of solar radiation measurements, but most common quality control (QC) methods do not detect frequent defects such as shading or calibration errors due to their low magnitude. We recently presented a new procedure, the Bias-based Quality Control (BQC), that detects low-magnitude defects by analyzing the stability of the deviations between several independent radiation databases and measurements. In this study, we extend the validation of the BQC by analyzing the quality of all publicly available Spanish radiometric networks measuring global horizontal irradiance (9 networks, 732 stations). Similarly to our previous validation, the BQC found many defects such as shading, soiling, or calibration issues not detected by classical QC methods. The results questioned the quality of SIAR, Euskalmet, MeteoGalica, and SOS Rioja, as all of them presented defects in more than 40% of their stations. Those studies based on these networks should be interpreted cautiously. In contrast, the number of defects was below a 5% in BSRN, AEMET, MeteoNavarra, Meteocat, and SIAR Rioja, though the presence of defects in networks such as AEMET highlights the importance of QC even when using a priori reliable stations.

Abstract Exactly how to estimate solar resources in areas without pyranometers is of great concern for solar energy planners and developers. This study addresses the mapping of daily global irradiation by combining geostatistical interpolation techniques with support vector regression machines. The support vector regression machines training process incorporated commonly measured meteorological variables (temperatures, rainfall, humidity and wind speed) to estimate solar irradiation and was performed with data of 35 pyranometers over continental Spain. Genetic algorithms were used to simultaneously perform feature selection and model parameter optimization in the calibration process. The model was then used to estimate solar irradiation in a massive set of exogenous stations, 365 sites without irradiation sensors, so as to overcome the lack of pyranometers. Then, different spatial techniques for interpolation, fed with both measured and estimated irradiation values, were evaluated and compared, which led to the conclusion that ordinary kriging demonstrated the best performance. Training and interpolation mean absolute errors were as low as 1.81 MJ / m 2 day and 1.74 MJ / m 2 day , respectively. Errors improved significantly as compared to interpolation without exogenous stations and others referred in the bibliography for the same region. This study presents an innovative methodology for estimating solar irradiation, which is especially promising since it may be implemented broadly across other regions and countries under similar circumstances.

Abstract The major sources of uncertainty in short-term assessment of global horizontal radiation (G) are the pyranometer type and their operation conditions for measurements, whereas the modeling approach and the geographic location are critical for estimations. The influence of all these factors in the uncertainty of the data has rarely been compared. Conversely, solar radiation data users are increasingly demanding more accurate uncertainty estimations. Here we compare the annual bias and uncertainty of all the mentioned factors using 732 weather stations located in Spain, two satellite-based products and three reanalyses. The largest uncertainties were associated to operational errors such as shading (bias = −8.0%) or soiling (bias = −9.4%), which occurred frequently in low-quality monitoring networks but are rarely detected because they pass conventional QC tests. Uncertainty in estimations greatly changed from reanalysis to satellite-based products, ranging from the gross accuracy of ERA-Interim ( + 6.1 - 6.7 + 18.8 %) to the high quality and spatial homogeneity of SARAH-1 ( + 1.4 - 5.3 + 5.6 %). Finally, photodiodes from the Spanish agricultural network SIAR showed an uncertainty of - 5 . 4 + 6 . 9 %, which is far greater than that of secondary standards ( ± 1.5%) and similar to SARAH-1. This is probably caused by the presence of undetectable operational errors and the use of uncorrected photodiodes. Photodiode measurements from low-quality monitoring networks such as SIAR should be used with caution, because the chances of adding extra uncertainties due to poor maintenance or inadequate calibration considerably increase.