What is Forecasting

What is Forecasting?

Forecasting refers to the prediction of future values based on historical data. On the Eliona platform, the Forecast App enables the automatic analysis of time series data and the creation of reliable forecasts. Typical use cases include:

• Energy consumption in buildings

• Room temperatures and climate control

• Condition monitoring of systems

The system recognizes recurring patterns, seasonal fluctuations, and sudden deviations to predict future developments.

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How do LSTM Models work in the Forecast App?

1. Recurrent Neural Networks (RNN)

Unlike classic neural networks, RNNs process data sequentially and maintain an internal state ("memory"). This allows earlier time points to have a direct influence on later predictions.

2. Long Short-Term Memory (LSTM)

LSTM cells are a special RNN architecture that:

• Preserves long-term dependencies by "forgetting" unimportant information and passing on relevant information over long sequences.

• Recognizes and stores short-term fluctuations in the model.

• Uses gating mechanisms (input, forget, and output gates) to specifically control which information enters or is removed from the internal state and when.

In the Forecast App, the LSTM model looks at a context window (parameterized by Context Length) of past measured values and, based on this, predicts a defined number of future steps (set via Forecast Length).

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Role of TensorFlow

TensorFlow is the underlying framework that performs the following tasks in the Forecast App:

1. Model Structure

Definition and linking of the LSTM cells as well as additional layers (e.g., Dense Layers).

2. Efficient Training

Use of GPU acceleration, automatic differentiation, and optimized algorithms (e.g., Adam optimizer).

3. Continuous Updating

Retraining with new data to keep the model aligned with current trends.

Further information: → TensorFlow Documentation → TensorFlow Keras Documentation