What are the main challenges of solar resource assessment and how can they be overcome?

To overcome spatial and temporal variability, it is crucial to adopt integrated approaches, combining data from multiple sources such as satellites, ground-based weather stations, and climate models. Rigorous calibration and validation of the data are essential to ensure its accuracy and reliability. Additionally, using advanced data processing techniques such as spatial and temporal interpolation, statistical analysis, and computational modeling can help fill data gaps and improve the accuracy of solar radiation estimates.

Depends on the level of accuracy you require. Weather by its own nature is variable, so you have be pragmatic. Use data which has enough history to give you a good reasonable forecast. This will be adequate enough to determine if the project will deliver what you want. I have used UK SAP calculations and over 5 years of monitoring the output all my installations, they’ve turned out to be pretty accurate.

PV watts has historically been the best provider here. Solar radiation data sources provide critical information on the amount of solar energy reaching the Earth's surface at a given location, crucial for various applications in solar energy utilization, climate modeling, and environmental monitoring.

The main challenges of solar resource assessment include data accuracy, variability in weather conditions, and site-specific factors. Overcome these challenges by using high-quality, long-term solar irradiance data from reliable sources and deploying advanced meteorological instruments. Employ satellite data and ground-based measurements for comprehensive analysis. Implement sophisticated modeling tools to account for variability and optimize site-specific assessments. Regularly update data and validate models with actual performance to enhance accuracy and reliability.

To ensure accuracy, it is crucial to compare and validate these different data sources, selecting the most appropriate one for the specific application. This careful selection process helps in achieving accurate and dependable solar resource assessments, essential for the planning and optimization of solar energy projects.

Consequently, it is essential to analyze historical and projected trends, patterns, and extremes of solar radiation, and to quantify the associated uncertainties and risks. This thorough analysis helps in accurately assessing the solar resource, ensuring more reliable and robust solar energy project planning and performance.

There's a variety of open source mapping technology software on the market currently; for example; Google maps, Microsoft suite (search), they're many companies- providers of SAS software such as, Maxar Technologies.

Ther's a bunch of resources in the market today. Lot's of these are tied into design tool like Aurora. Solar is a lot easier than it was in 2006!

Modeling enables accurate prediction of energy production under different environmental and operational conditions, providing valuable insights for proper system sizing and optimization of its performance over time. One of the challenges is the variability of solar irradiance due to factors such as time of day, seasons, weather conditions, and shading. Precise modeling takes into account these variables to accurately estimate energy production.

There are several software solutions which have been tried and tested. PVSOL and Solar Edge are industry standard, which will give you a comprehensive modelling result.

A lot of these organizations were created for agriculture in the early 1900's. Farmers were able to predict crop growth and forecast their yield based on seasons and past data. There's over a dozen of sources in the market today. All can easily be found through google search.

The main challenges of solar resource assessment in engineering project management include data accuracy, temporal and spatial variability, and technological limitations. Overcoming these involves using high-resolution satellite data and ground-based measurements to improve accuracy, employing advanced modeling techniques to account for variability, and leveraging machine learning to enhance predictive capabilities. Effective project management integrates these methods to optimize resource assessment and project planning.