Produkty
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Prospect
Odhadnite solárny potenciál vašej lokality
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Evaluate
Time Series a TMY dáta pre optimálny dizajn elektrárne
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Monitor
Hodnotenie fotovoltického výstupu v reálnom čase
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Forecast
Predpoveď výroby solárnej energie na až 14 dní
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Analyst
Spravujte všetky solárne dáta na jednom mieste
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Integrácie
Automatizujte dodávku Solargis dát
Príklady použitia
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Výber lokality
Nájdite správnu lokalitu pre váš solárny projekt
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Simulácia energetického výnosu
Analyzujte potenciálne zisky a riziká
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Optimalizácia návrhu elektrárne
Nájdite optimálny dizajn pre vašu solárnu elektráreň
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Skutočný výkon elektrárne
Poznajte skutočný výnos vašej elektrárne
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Predpoveď výkonu
Predpovedajte výnos z vášho solárneho projektu
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Overenie pozemných dát
Overte si kvalitu solárnych a meteo meraní
Hodnotenie solárneho zdroja a meteorologických dát
Detailné hodnotenie a validácia solárneho zdroja
Prispôsobenie modelov Solargis lokalite
Kombinácia satelitných dát s lokálnymi meraniami
Kontrola kvality solárnych a meteorologických meraní
Oprava chýb v dátach z pozemných meraní
GIS dáta na mieru
Solargis GIS dáta prispôsobené pre vaše aplikácie
Posúdenie energetického výnosu z fotovoltiky
Odhadnite neistotu vstupných dát
Hodnotenie výkonu fotovoltiky
Odhad energie pre refinancovanie alebo akvizíciu
Štúdia PV variability a optimalizácie skladovania energie
Pochopte variabilitu výstupu naprieč regiónmi
Regionálna štúdia potenciálu solárnej energie
Identifikácia lokalít pre solárne elektrárne
Naša expertíza
Ako naša technológia funguje?
Metodológia
Ako preklápame vedu do technológie?
API & integrácia
Ako integrovať Solargis dáta cez API
Príručky a dokumentácia
Release notes
Príbehy klientov
Blog
Ebooky
Webináre
Publikácie
Udalosti
Bezplatné mapy a GIS dáta
Mapy solárneho výkonu
O spoločnosti Solargis
Partneri
ISO certifikáty
Kariéra

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Pablo Caballero

Posts by Pablo Caballero

From Time Series to TMY: When to use each?
Best practices

From Time Series to TMY: When to use each?

All Solar industry players need to simulate their power plant designs and financial plans at some point. To do so against summarized conditions given by data products like Typical Meteorological Years has been common until recently. However, running energy simulations using more realistic conditions described by Multi-Year Time Series of data is recommended to reduce project risk and evaluate all scenarios.

How to spot solar Eclipse events in solar energy datasets
Best practices

How to spot solar Eclipse events in solar energy datasets

Solar irradiance modeling involves computing the amount of sunlight reaching the Earth's surface. During eclipses, the moon obstructs solar radiation, leading to a temporary reduction in irradiance.

Blending ERA5 reanalysis models for improving the accuracy of temperature data for PV simulations
Product updates

Blending ERA5 reanalysis models for improving the accuracy of temperature data for PV simulations

In this article, we are sharing how Solargis has improved the accuracy of air temperature datasets we provide along with other parameters affecting solar energy power simulations.

IEA’s Worldwide Benchmark new study shows the highest overall GHI and DNI accuracy for Solargis model
Solargis news

IEA’s Worldwide Benchmark new study shows the highest overall GHI and DNI accuracy for Solargis model

The study summarizes the results of comparing the data provided by various institutional or commercial providers of solar irradiance models against ground measurements collected at 129 sites distributed globally. It helps solar developers navigate through all the available databases.

Solargis’ approach to 1-minute data
Best practices

Solargis’ approach to 1-minute data

An increasing number of solar PV plant developers, operators and owners require high frequency data (1-min) to make qualitative improvements throughout the entire lifecycle of a solar project.

Surface Albedo – most frequent questions
Product updates

Surface Albedo – most frequent questions

Due to the impact that surface albedo has in PV yield calculations (mostly when we talk about bifacial modules), we have noticed an increasing interest in knowing more about this parameter. These are the most typical questions we are receiving (with their corresponding answers):

Solar resource data – time series data vs monthly averages
Best practices

Solar resource data – time series data vs monthly averages

Bad data in equals bad data out. This well-known phrase is very relevant in a context of technical design and energy simulation of photovoltaic (PV) power plants. Most solar companies understand this and are carefully evaluating uncertainty of solar resource data used for feasibility purposes ...

How to calculate P90 (or other Pxx) PV energy yield estimates
Best practices

How to calculate P90 (or other Pxx) PV energy yield estimates

To assess the solar resource or energy yield potential of a site, we model the solar resource/energy yield using best available information and methods. The resulting estimate is the P50 estimate, or in other words, the “best estimate”. P50 is essentially a statistical level of confidence suggesting that we expect to exceed the predicted solar resource/energy yield 50% of the time. However, ...

Being certain about solar radiation uncertainty
Best practices

Being certain about solar radiation uncertainty

The process of estimating solar radiation data uncertainty can be sometimes unclear. We've tried to summarise in 4 simple steps.

About the author

Pablo Caballero
Pablo CaballeroTechnical Writer
Pablo is an industrial engineer with extensive experience in the renewable energy and software development sectors. He specializes in technical writing and content marketing and is driven by a passion for bridging gaps between audiences, technology, and business. With a diverse background, he is dedicated to promoting sustainability ideas and education in STEM fields.