Data plays a crucial role in many models, especially large scale numerical scenario models. Getting the data needed to run your model is oftentimes the most frustrating part of the whole modeling process, simply for the fact that data is hard to get and seldom in the direct format you need for your model.

Data availability has increased in recent years, with statistical offices, agencies and associations providing diverse databases on multiple energy and environmental related topics. Even if you cannot find the desired data you may still be able to generate reasonable estimates. Always think about the underlying technical or natural structures that define the numbers you would need; i.e. there are often no plant specific generation costs available, but using fuel prices and plant efficiencies you can generate a close proxy.

Albeit, as a modeller you are more interested in transferring your input data into output, don’t neglect the interesting information contained in your input data: can you see shifts in the hourly demand of the last years? Is there some underlying price trend in your fuel costs? Does the spatial distribution of pollution give you an insight on non-observable pollution sources? Your model will use this data to hopefully generate interesting findings, but sometimes the data itself already provides some findings that may motivate you to add or change elements of your model.

When you finally get your model results there are two things to do: first, you need to make sure the numbers are correct; and second, you need to transfer the numbers into understandable results for your audience.

If you follow the steps provided during this course you hopefully have a working model that indeed produces correct results. But there is always the risk that you made some small errors while writing your model code or constructing your data upload. Always remember: your model software will do whatever you told it, not what you think you told it. So double-check that your coding is correct.

Presenting your results can sometimes be a challenge as you literally produce thousands of numbers. Boil it down to a reasonable reader-friendly summary: use descriptive statistics, aggregate results, show examples, limit the number of scenarios and highlight the insights. Tables and figures are your friends when it comes to presenting your model outcomes; but always remember: it’s what you read out of those numbers, not the numbers that make your interpretation.

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