AM263Px MCU+ SDK: HVAC Indoor Temperature Forecasting

| AM263Px MCU+ SDK 26.00.00 |

HVAC Indoor Temperature Forecasting

# Introduction

Traditional HVAC controllers are reactive - they respond to temperature deviations only after they occur, leading to delayed corrections, unnecessary compressor cycling, and wasted energy. A smarter approach is to anticipate what the indoor temperature will be in the near future and act preemptively.

This project demonstrates on-device machine learning for HVAC control. A compact LSTM model running directly on the AM26x MCU predicts upcoming temperature trends from recent sensor readings, enabling smoother, more energy-efficient control decisions without relying on cloud connectivity.

# Supported Combinations

Parameter | Value
--- | ---
CPU + OS | r5fss0-0 nortos
Toolchain | ti-arm-clang
Board | am263px-lp
Example folder | examples/ai/hvac_indoor_temp_forecast/

# Dataset and Model Details

## Dataset

This example uses a synthetic HVAC dataset designed to emulate realistic indoor thermal dynamics. The dataset captures the interplay between compressor operation, outdoor conditions, and the resulting indoor temperature.

Parameter | Value
--- | ---
Signals | compressorFrequency, outdoorTemperature, indoorTemperature
Input Variables | 3
Prediction Target | indoorTemperature (next timestep)

The model learns to predict the indoor temperature at the next timestep given the past 5 readings of all three signals.

## Model Architecture

The model is `FCST_LSTM10`, a single-layer LSTM network with a hidden size of 10 and approximately 611 trainable parameters. LSTM gating mechanisms capture the temporal dependencies between compressor activity, outdoor conditions, and the resulting indoor temperature trajectory.

## Input Features

The model takes 4D input (N,C,H,W):

- N (1): batch size, restricted to 1
- C (3): channels - compressorFrequency, outdoorTemperature, indoorTemperature
- H (5): number of historical timesteps (frame size) used for prediction
- W (1): width, restricted to 1 for timeseries applications

## Output

The model produces an output tensor of shape (1, 1, 1, 1), representing the predicted indoor temperature at the next timestep (forecast_horizon = 1). This is a continuous forecasting output that can be fed directly into a downstream control algorithm.

# Feature Extraction Configuration

A SimpleWindow transform segments the continuous stream into fixed-length windows of 5 consecutive timesteps across all 3 input channels. No FFT, binning, or log scaling is applied - the LSTM model learns directly from raw sensor values.

# Steps to Run the Example

- When using CCS projects to build, import the CCS project for the required combination and build it using the CCS project menu (see Using SDK with CCS Projects).
- When using makefiles to build, note the required combination and build using make command (see Using SDK with Makefiles)
- Launch a CCS debug session and run the executable, see CCS Launch, Load and Run
- The application will forecast indoor temperature and compare against expected values

# See Also

AI Examples

# Sample Output

HVAC Indoor Temperature Forecasting Example Started ...
All tests have passed!!
Golden vectors matched: 3 not matched: 0