Contents:
Introduction
In this article, you will learn how to use the Forecast feature in the energy management platform.
Forecast allows you to have energy consumption and solar production predictions for buildings up to 10 days ahead, based on historical data, weather and holidays.
The available forecast targets for this feature are:
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- Electricity consumption.
- Gas consumption
- Water consumption.
- Electricity generation.
Nomenclature
Firstly, it is necessary to explain the nomenclature used in this feature:
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Forecast Target: The specific outcome or variable that the forecast aims to predict, such as energy consumption, solar production, or gas usage.
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Forecast Input: The data used to generate a forecast, including historical consumption data, weather forecasts, holidays, and any other relevant parameters.
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Model: The actual machine learning model that is used, since it predicts best the forecast target (it has the lower error in prediction).
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Feature Importance: A measure of how much a particular input or variable contributes to the accuracy of a forecasting model, helping identify which factors most influence the predictions.
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Forecast Metrics: The performance indicators used to evaluate the accuracy and quality of the forecast, such as Mean Absolute Error (MAE) or Root Mean Square Error (RMSE).
Requirements
In order to use the Forecast feature, you need to meet the following requirements:
- Consumption data needs to be (at least) hourly.
- Reference devices need to be configured in the location.
Configuration
The configuration needed to create a Forecast is the following:
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Forecast target:
- First, you have to define the specific outcome to predict (e.g., electricity consumption, gas or solar production).
- Then, you need to specify the location, reference meter and parameter to forecast.
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Forecast inputs:
- The next step is to choose the inputs from available data, such as weather parameters and holidays.
- You can customise which variables are included in the forecasting model
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Acceptance of DP:
- Finally, you need to agree the acceptance of a new data point that will appear in the configuration.
Visualisation
There are two visualisations available for the Forecast:
- Forecast results: meaning the electricity, water or gas forecast, which can be represented as data points in any Analytics screen or dashboard, with a forecast horizon, displayed hourly.
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Model results: meaning additional information on the models that are being used to calculate the forecasts, included in the Forecast feature:
- Feature importance. Insights into which variables (e.g., weather, holidays) influence the most in the forecast
- Model metrics. Performance indicators, including Mean Absolute Error (MAE) and Root Mean Square Error (RMSE).
- Last training date. Information about the latest training session.
To keep in mind
The Forecast feature has the following limitations. Some of them will be deleted in future releases:
- If you change the configuration of the reference meter or the historical data for a certain meter, this can affect the results.
- If data quality for the historical data is not good enough, the forecast results will be as bad.
- You cannot choose which Machine Learning model will be used for the forecast, we will choose the best one among our offerings.
- You cannot specify the weight of specific parameters, we will choose the one that optimises results.
Pricing
Each forecast you create will generate one data point. All of them will be charged according to your pricing.
The Forecast feature is only included in Advanced and Ultimate Licences.
Feedback
If you have questions or need help setting up this tool, please feel free to contact our support team.
We are here to help you get the most out of Forecast's functionality!
Also, if you would like to give us your feedback on this feature, you can do so with this form: