> For the complete documentation index, see [llms.txt](https://docs.buildings.ability.abb/collection/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.buildings.ability.abb/collection/english-v14/getting-started/kunstliche-intelligenz-in-abb-buildingpro/forecasting.md).

# Forecasting

## **What is forecasting?**

Forecasting refers to predicting future values based on historical time series. Using statistical and machine learning methods, patterns are identified in past data in order to generate forecasts—for example for energy consumption, temperatures, or occupancy levels.

<figure><img src="https://content.gitbook.com/content/Nyvwhz1kEMXcHf4HLuZ8/blobs/cxdeR6kdLLf2cNgguRrO/forecast%20app%20(6).png" alt=""><figcaption></figcaption></figure>

## **What is forecasting used for in smart buildings?**

In building management, forecasting provides the basis for proactive operating strategies:

* **Peak load smoothing:** Avoiding high grid draw during peak times through early switching actions.
* **Predictive maintenance:** Predicting system failures (e.g., vibration or pressure spikes) and automatically creating tickets before actual damage occurs.
* **Comfort optimization:** Gentle preheating or cooling before or after to maintain desired temperature ranges without hectic readjustment.
* **Occupancy forecasts:** Adjusting ventilation and lighting schedules to expected user numbers.
* **Tariff management:** Shifting consumption to cheaper time windows with variable electricity tariffs.

***

## **How do we implement forecasting at Eliona?**

In our [➔ **Forecast app**](/collection/english-v14/apps/apps/forecast.md) it is enough to

1. **target attribute** (e.g. “electricity consumption meter X”) and, if applicable, **additional variables** (outside temperature, day of the week, occupancy)
2. the desired **forecast duration** to be defined.

The app then automatically generates forecast attributes that behave exactly like sensor data—usable in dashboards, rule chains, or analytics.

***

## **Which technologies do we use—and why?**

* **LSTM (Long Short-Term Memory)**
  * **Why:** LSTM networks are able to **nonlinear relationships** and model long-term temporal dependencies without failing due to vanishing gradients.
  * **Advantages for smart buildings:**
    * Capture complex interactions (e.g., opening hours × temperature × occupancy).
    * Robust with many additional features and extended forecast horizons.
  * **Practical evidence:** A multi-layer LSTM with denoising achieved 15–30% higher accuracy for building energy forecasts in recent studies [➔arXiv\[External\]](https://arxiv.org/abs/2309.02908).
* **SNARIMAX (Seasonal Non-linear ARIMAX)**
  * **Why:** SNARIMAX integrates **seasonal patterns**, auto-regressive and moving-average components as well as **exogenous influencing factors** (e.g. weather, day of the week) in a single, online-capable model [➔MDPI\[External\]](https://riverml.xyz/dev/api/time-series/SNARIMAX/).
  * **Advantages for smart buildings:**
    * Low data requirements and a robust baseline for short histories.
    * Rapid adaptation to live data (incremental updates).
* **Auto mode:**\
  Our app compares both methods in backtesting and automatically uses the more powerful engine.

***

**Conclusion:**\
By combining **LSTM** for nonlinear, long-term patterns and **SNARIMAX** for seasonal, explainable baselines, Eliona offers a **highly flexible**, **low-threshold** forecast solution. This enables us to implement proactive control and real predictive use cases—with no ML expertise and in just a few clicks.


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