# Climate Data Values

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Hi,

I have a question regarding the climate data used to calculate power production etc.

Let's say we have climate data over a 10 years period of time, and we have 1 year of hourly values for the costumers consumption.

We are interested in the internal consumption of the produced energy, which are the values for climate data (for example irradiation) that goes in to the formula for this?

Is the percentage of internal consumption based on the "lowest" data (per time unit, hour) over this ten years period, or is it the average over these available 10 years (per time unit?.
Or the highest?

Lets say for one hour on one specific day, the data of course differs a lot over a 10 years period.

Best Regards

Daniel

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Hi Daniel,

a very good question you ask there. There are different approaches when dealing with climate data over a long period of time. And as always, there are pros and cons for every approach.

The approach most people use is to compile a representative collection of days out of the measurements of the past 10 or twenty years, a so-called typical meteorological year (TMY). The climate data we use is from MeteoNorm, and they use data over a period of twenty years, sometimes 10, sometimes 30. That means, in the final dataset the hourly values of one day are not averaged or max/min-ed over the years, but taken from one specific year. The data for the next day are then taken from another year, perhaps. You will find a lot of information on how TMY are compiled on the internet, here is a good start:

But you are totally right, the amount of self consumable solar energy at a given moment depends strongly on the specific value of solar irradiance at the given hour (or minute) and it would change perhaps significantly depending on which measurement year you use. It is important to understand that TMY data do not give any guarantee regarding the values at a specific point in time. What they guarantee is that the data over the year is typical for the location in question, that it is very probable. And while you cannot quantifiy the probability of a specific value for a given point in time, you can quantify the overall uncertainty of your climate data. Here is a good presentation to start:

The same problem you'll have with the load profile by the way. Perhaps you have measured the load profile of one year for a given household or company. But there is no guarantee that the 3kW that you measured on January 2nd at 9am will really occur in reality afterwards. But good news is that there are also standardized "typical" load profiles for households and different kinds of enterprises that minimize the uncertainty over a year.

But no, they do not solve the actual problem you mentioned. The only solution I could think of would be to use all twenty datasets for solar irradiance from the last twenty years and make twenty simulations, and then have a look at the minimum, maximum and average self-consumption. And then you would have to repeat that with several years of load measurements. I don't know if this is feasible But it would be a very interesting research topic!

Kind regards,

Martin

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