A Characterization of Metrics for Comparing Satellite-Based and Ground-Measured Global Horizontal Irradiance Data: A Principal Component Analysis Application

Abstract

The increasing integration of photovoltaic (PV) power plants into power systems demands a high accuracy of yield prediction and measurement. With this aim, different global horizontal irradiance (GHI) estimations based on new-generation geostationary satellites have been recently proposed, providing a growing number of solutions and databases, mostly available online, in addition to the many ground-based irradiance data installations currently available. According to the specific literature, there is a lack of agreement in validation strategies for a bankable, satellite-derived irradiance dataset. Moreover, different irradiance data sources are compared in recent contributions based on a diversity of arbitrary metrics. Under this framework, this paper describes a characterization of metrics based on a principal component analysis (PCA) application to classify such metrics, aiming to provide non-redundant and complementary information. Therefore, different groups of metrics are identified by applying the PCA process, allowing us to compare, in a more extensive way, different irradiance data sources and exploring and identifying their differences. The methodology has been evaluated using satellite-based and ground-measured GHI data collected for one year in seven different Spanish locations, with a one-hour sample time. Data characterization, results, and a discussion about the suitability of the proposed methodology are also included in the paper.

Sensitive Parameter Analysis for Solar Irradiance Short-Term Forecasting: Application to LoRa-Based Monitoring Technology

“…As can be seen, the truncated histograms retain more than 90% of such discrepancies and are considered suitable enough for this sensitive analysis. Secondly, and according to the variety of errors and differences available in the specific literature, as well as the comparison conducted by the authors in [83], the mean absolute percentage error (MAPE) and dynamic time warping (DTW) were also selected as metric estimations-see Section 3.3. Indeed, and as can be found in [92], DTW is considered as an appropriate technique to estimate and find an optimal alignment between two time-dependent sequences under a set of restrictions.…”

“…Discrepancies and similarities are calculated, discussing the influence of the different realistic LoRa parameters on the solar forecasting process. As previously analyzed by the authors in [83], different metrics can be found in the specific literature to determine discrepancies. From this classification, normalized root mean square error (nRMSE), mean absolute percentage error (MAPE), and dynamic time warping (DTW) are determined to provide complementary information and characterize convenient discrepancies among the PV short-term forecasting data by considering SF09 and 0% loss of data and the rest of scenarios.…”

Spatio-Temporal Dynamic Clustering Modeling for Solar Irradiance Resource Assessment

“…Meteorological data can be collected from on-the-ground weather stations or online satellite data, which can be downloaded in a variety of sources and formats. A previous satellitebased and ground-measured GHI comparison was carried out by the authors and it can be found in [29]. Nevertheless, and in order to organize and manipulate temporal data, it is necessary to consider a set of tidy data principles which are able to be extended to temporal data as was previously described by other researchers [30].…”

Intercomparison of Satellite-Derived Solar Irradiance from the GEO-KOMSAT-2A and HIMAWARI-8/9 Satellites by the Evaluation with Ground Observations

“…Relative error metrics as a percentage of maximum total irradiance are already introduced in Mathiesen et al [48], and more recently in Bueso et al [49], to validate the variability from the clear sky irradiance. These error metrics are compared quantitatively, regardless of changes in maximum irradiance due to the solar diurnal variation, geographic position, and time of year.…”

“…The hourly mean or total GHI is useful for the estimation of solar power generation from photovoltaic systems because variability in solar irradiance is a prominent barrier to the extending of renewable energy. The nominal RMSE, which is defined as the ratio of RMSE to maximum solar irradiance, is widely used in energy research [49]. Assuming that the maximum solar irradiance is 800 W•m −2 during the investigation period, the nominal RMSE values are 4.8%, 7.4%, 12.1%, and 9.8%, for the UASIBS-KIER model V1, V2, NMSC-INS, and JAXA-INS models, respectively.…”