Solargis

Another year around the sun. What did it look like in terms of solar irradiation? We conducted our yearly analysis of global horizontal irradiation (GHI) and evaluated the difference for 2024 from the long-term average (LTA). The findings reveal notable decreases and increases in solar irradiation across the globe, while GHI in other regions oscillated around the long-term average. See the the regional patterns presented on the map below, showing the 2024 difference of GHI from the long-term average. ▶ Above-average GHI areas in orange-red colors: Western Canada, Northeast USA, Central-East Europe, Central China, South Australia, New Zealand, Northern parts of Japan, etc. ▶ Below-average GHI areas are in green-blue colors: France, Northern Italy, Uruguay, Oman, and Central-East India. ▶ Areas close to the long-term average are in bright yellowish colors: E.g. most of Spain, Brazil, North Africa, and parts of the Middle East remain consistent with LTA solar output throughout 2024. Click the link to look at some of the selected regions in more detail: https://lnkd.in/e_NCnNkR #Maps #SolarEnergy #Data #Solar #GHI

What factors influence satellite-model data uncertainty? Validation statistics provide useful metrics to evaluate the performance of a solar radiation model. Model performance analysis is not easy and requires expert-level knowledge of the model, its internal algorithm, and its inputs. On the other hand, a well-executed validation assumes that quality-controlled measurements from high-accuracy and rigorously maintained instruments are used. Simplified approaches to analyzing uncertainty convert validation statistics to expected uncertainty for all the covered territories. Or they simply calculate metrics by continent. Yes, satellite-based models perform better in more stable weather conditions. However, the climate is not the only factor affecting solar model performance. Deriving expected uncertainty by looking solely at climate does not yield a complete interpretation. In practice, you need to estimate the expected uncertainty on a case-by-case basis. You need to take into account all factors including: -- climate, -- geography, -- environment, and -- satellite technology. You can determine only an indicative range of uncertainty without looking at the specific site conditions. --- Learn more about solar model validation via ground measurements in our ebook: https://lnkd.in/eyw-vEbD #dataquality #solar #satellitedata #renewableenergy

As we wrap up another year, we are thankful for the incredible solar energy community that powers us forward! To our valued clients, dedicated partners, and amazing team - thank you for your trust and collaboration in 2024. And here's to an even more energizing 2025! #solarenergy #cleanenergy #happyholidays #newyear #2025

✅ Manage variability with accurate 14-day power output forecasts. ✅ Get insights for better battery management and trading. ✅ Reduce grid penalties and plan power plant maintenance. See how to predict the output of your solar power project with Solargis: https://lnkd.in/ewVtw-W7 #solarinvestment #solarenergy #forecast #solardata #powerplant

Horizontal vs inclined albedometer configuration. The albedometer arrangement where the separation plane of the instrument is parallel to the plane of the modules (Plane of array, POA) allows for simultaneous measurement of the GTI and the irradiance projected on the rear side of the modules. This is useful for monitoring during the operational phase of the bifacial PV power plants. However, the POA albedometer configuration does not allow to obtain the albedo of the surface. The main difference between the values derived from both configurations lies in the total incident irradiance measured, namely: – GHI in the case of the horizontal installation, and – GTI in the case of the POA installation*. While the horizontal configuration allows to properly derive the ground surface albedo according to the definition, the tilted (POA) configuration does not. In the picture: Example of a tilted installation of an albedometer (operation phase). Image credit & Copyright: GroundWork Renewables, Inc. #albedo #solarenergy #solarpowerplant #pvsolar #bifacial * Dittmann S. et al., 2019. Comparative analysis of albedo measurements (plane-of-array and horizontal) at multiple sites worldwide. 10.4229/EUPVSEC20192019-5DO.1.4.

GHI difference maps for November 2024 & some interesting findings👇 Contrasting Central Europe ▶ Heavy, persistent fogs in the first half of November 2024 in northern France, England, Benelux, northern parts of Germany, and Poland; partially also river Po basin in Italy ▶ On the contrary, mostly sunny weather in higher elevations: Alps, Carpathians, Ceska vysocina (Bohemian Massif) Contrasting Scandinavia ▶ Low GHI on the Atlantic coast, high GHI on the Baltic Sea & Gulf of Finland South China ▶ High GHI due to many clear-sky days Kamchatka extreme ▶ High GHI – many clear-sky days and snowy landscapes with high albedo Others ▶ Extreme GHI lows along the NW coast of the Hudson Bay in Canada ▶ Notable GHI highs +25% in Veracruz state in Mexico --- Monitor your PV plant performance with Solargis monthly maps & reports. Check free report samples here: https://lnkd.in/e-2RybTY Care to receive these maps monthly in your inbox? Sign up here: https://lnkd.in/eZPzevKd #Maps #SolarEnergy #Data #Solar #GHI

Tengeh is the first large-scale solar floating farm in Singapore which has a capacity of 60MWp 👇 #SolarPower #SolarData #FloatingSolar #FloatingPV

Expanding the horizons of solar energy in North America ☀️ Did you know that regions as far north as 65˚N in North America are now part of our solar data and PV output? Traditionally, the subpolar regions are thought to have limited solar potential.  But the reality is changing thanks to advancements in photovoltaic (PV) technology: ✅ Bifacial panels capitalize on snowy terrain to boost energy output. ✅ Lower operating temperatures enhance efficiency. ✅ Endless summer daylight hours maximize production during key seasons. While winter sunlight may be scarce, PV systems are still a game-changer in reducing reliance on fossil fuels often used in remote communities. Meteorological satellites used in solar irradiance modeling do not fully cover polar latitudes. Yet, we found the current generation of GOES-R satellites to have sufficient capabilities to support extending our historical data coverage up to the 65th parallel in North America. The Solargis Prospect now covers swaths of Alaska and Canada's provinces Yukon, Northwest Territories, and Nunavut, as well as the southern part of Greenland. See for yourself: https://lnkd.in/e5xe_uiW #data #solar #pvdata #renewables #polar #northamerica

Do not use just one variable in data comparisons. Whenever we compare a model with a ground-based data reference, we assess the similarity between the two sets of records for the same site and period. A specific set of statistical indicators should be used in the model-measurement comparisons. When running model-measurements solar irradiance data comparisons, bias calculations give results close to zero. However, it might be the case of positive and negative errors canceling each other out when the sum is calculated. That is why we calculate an average over the absolute error values (Mean Absolute Deviation or MAD). Calculating the mean over the absolute values can still hide something: the big errors remain undetected when they balance out with the smaller ones. To give a higher weight to bigger errors, we calculate an average over the squared deviations – Root Mean Square Deviation or RMSD. This indicator can be calculated for different aggregation levels (dispersion of sub-hourly, hourly, daily values, etc). To be able to compare RMSD, we use normalization of values (calculating the percentage using a reference value). We can also benefit from other unit-invariant metrics like the Coefficient of determination and correlation (R2 and R). --- Learn more about solar model validation via ground measurements in our ebook: https://lnkd.in/eyw-vEbD? #dataquality #solar #satellitedata #renewableenergy

Don't struggle with spreadsheets. Solar energy specialists spend hours trying to analyze solar data using tools or spreadsheets that don't fit their needs. Even if you use custom Python scripts, it's hard to share results with others in an easy-to-understand way. We designed Solargis Analyst to empower solar analysts to work with solar data more efficiently.   👉 Visualize complex and large solar datasets 👉 Compare measured data to model outputs 👉 Identify and clean errors from measurements  👉 Harmonize multi-source input streams 👉 Streamline solar data management You can analyze solar resource data without writing a single line of code. Use pre-designed plots and visualizations. Create customized calculations, aggregations, and comparisons. All from a single user-friendly platform. Explore the solution in detail 👉 https://lnkd.in/evWZfsi7 #solarenergy #solardata #dataanalysis #solar #solaranalyst