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Hi @Eric Stein-Beldring, just want to confirm that this is an interesting feature for our company. Operations engineers want to be able to see a syntethic time serie summarizing data points from several time series in a chart. I think being able to specifiy this in the Cognite Chart url could work great. ExampleThis URL would give a chart showing a time serie with 10% of time serie 1 + 100 % of time serie 2, in the last year.https://charts.cogniteapp.com/prod?cluster=example&endTime=now&startTime=365d-ago&timeserieExternalIds=0.1,timeserie_1_id+1.0,timeserie2_id
Hi! I had a look at the new min/max functionality for calculated time series, which is really promising for our use cases. If we could get min max based on 10 years of data with the hourly granularity, we would be really happy. Our users typically looks at time series with 1h granularity the last ten years. The underlaying time series typically has datapoints every 10s. Based on the current configuration of Charts, the peak load the calculation I tested missed the 1h granularity peak load by about 20 percent, which is not satisfactory. The real maximim was 699 MW while the maximum in chart was 551 MW. I resampled to 1h granularity. It would be really interesting to look into whether we could close this gap.
Hi! Ok, a bit of a misunderstanding on our side here, so to make sure you understand it correctly, I am providing some more information. The issue is not meant to address gaps in the data, but rather timeseries of different 'lengths'. I see that these probably gets muddled together in practice, but the case for the feature described is different: If fx Trafo B was put into service in 2021, affecting the load on Trafo A that has been running since 2011. The analyst want to make a calculation of what the situation was like for Trafo A +B before and after. The current way to add these together in our app would only give data from 2021, where both timeseries have data. What we want with the checkbox is to 'extend' the timeseries for Trafo B, so that we get a result where the grid change can be understood (see our design suggestion below). How this relates to gaps in the data is another question, but I hope this is more clear..
Great, thanks. For sum of power flow, we have the average, which can be multiplied by time to get the integral. Max/Min aggregates are a relatively low-hanging fruit, which we have plans to implement.I think this is a solution that will work well for us in most cases. Over time periods where each time series have reaced min or max at different times, we will overestimate the min/max, but I don’t think this is an issue very often. The consequence of overestimating min/max is not of great importance either. If we could get one new feature, this is the one I would wish. Something that is more challenging though, is to do max_1h(TS_1+...+TS_N). That is, to add timeseries before we take the aggregate. In that case, we would need to create a new time series TS_sum, and fill it with datapoints that represent the sum, which we then could aggregate. We have done this through Cognite Functions on quite a few time series. We have discussed a feature where we create a new time serie on demand usin
Thanks for helpful and prompt replies to our questions! Representing long term synthetic time series with a representation of min/max of at least 1 hour granularity is an important issue for our users, the power system analysts. They typically want to sum the power flow over several components over several years, and see at least a good approximation of the maximum and minimum flow.The max and min values should represent the max/min at least one hour granularity to make it comparable to the capacities of the limiting component in the grid. The syntetic time series should take missing datapoints into account, because it is not uncommon for time series to have gaps. Most of these gaps are identifiable by events, such as disconnections but also integrity events that @RobersoSN has created.It quickly becomes complex to fix this on our side, so supporting features from Cognite are greatly appreciated.
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