Apps Market - Forecasting

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Forecasts your consumption and costs for the coming months, and compare it vs your different budgets and baselines.
 

Getting Started 

Note: you should be superAdmin or Admin in order to configure the Forecast. Otherwise, contact the administrator of your account.

The first step is to configure your forecast and allow the system to do some calculations based on temporal series algorithms. To do so, you should go to Settings > Forecasting > and click on "New Single Forecast". Here you can define the device you want to use to train and calculate its corresponding forecasting. Just select the associated location, the device and the corresponding parameter. Below you have an example:

In-bulk Forecast

In case you want to do the forecast for your whole portfolio in a massive way, you have the option to in-bulk create forecasts.
 
For this, you should click on "New in-bulk forecast" and select the Energy Source, forecasted parameter and Reference devices that you want to create the forecast to:
 
mceclip0.png
 

Artificial Intelligence Model

The model can't be edited, and it is based on artificial intelligence algorithms that use the consumption (or production) historical behavior and some time parameters: month, day and time.
 
The more historical data we have, the more reliable the model's prediction will be (go to Forecasting Reliability)


Add/Edit baselines

You may want to compare your forecast consumption against your goals or targets as a baseline. Here you can add up to 5 baselines to compare with your forecast consumption.


Analysis

Once you have configured your forecast information, you should wait a bit. Forecasted data is calculated every day (03:00h GMT) for one year in advance. The system takes between 1 week and 3 months to provide accurate models. 6 months of historical data is needed
  1. Energy source selector. Select the energy source you want to analyse
  2. Dates range picker. Select the period of time for the analysis. You can use the presets available to move quickly (today, yesterday, last 7 days, last month...)
  3. Location. Select the location for analysis.
  4. Device. Select the device you want to analyse
  5. Frequency. Select the frequency between daily, weekly and monthly
  6. Compare against these Goals and Budgets. Here you can select the baselines previously configured to compare them against the forecasted data.
  7. Cost. Convert the forecasting directly into Euros. We take the average price for the selected period to do the calculations
  8. Export data to Excel. Export the selected data to an Excel file.
  9. Bars chart. View your data in bars
  10. Accumulated view. Analyse your forecasting aggregating all the consumption for the selected period.
  11. Summary table. Comparison between the real consumption, the forecast, and all the baselines selected if any. Here you can see the total consumption and the difference with the real consumption.
 


Forecasting Reliability

This part of the article explains the Forecasting Reliability Metric available in the Forecasting App.


What is the Forecasting Reliability Metric?

The Forecast Reliability Metric allows you to assess how much you can rely on your forecast. It consists in a tag with 5 possibles values. When you hover your mouse over the tag, a tooltip pops up with information relative to the metric.

High
The difference between your forecast and your data is less than 10%. You can rely on this forecast.
High.png


Medium
The difference between your forecast and your data is between 10 and 20%. Is not a very good forecast but it is still acceptable. Please, check for any gaps or missing data.
Medium.png

Low
The difference between your forecast and your data is greater than 20%. We do not advise you to trust in this forecast.
Low.png
 
 
None
There is not enough data to calculate your forecast or your profile has too much variability.
None.png


Error
Something went wrong during the calculation of the forecast. Please, contact our support team.
Error.png
 


How does it work?

The Forecasting Reliability Metric is a mark based on statistical indicators that asses the quality of your data. Some of the analyzed variables are:

  • How many zero values have your data
  • How many N/A values have your data
  • Chaotic pattern changes
  • Outliers

The better the quality of your data is, the more grounded the forecasts will be. The score obtained determines the value of the Forecasting Reliability Metric.

ForecastReliabilityGraph.png

 

More information

If you want a deeper explanation of the app, here you will find a webinar dedicated to it. Enjoy!

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