A self-learning algorithm for estimating solar heat gain and temperature changes in a single-Family residence

The model was validated using experimental data from the National Institute of Standards and Technology (NIST) Net-Zero Energy Residential Test Facility. It was found that with a simple temperature decay test to determine a thermal time constant and a seven-day sliding window of training data to account for seasonal variations in other parameters, the algorithm can reliably predict indoor temperatures for a 24 h period using a solar irradiance forecast, an outdoor air temperature forecast, and heat pump output. The average root mean square temperature prediction error was found to be 2.2%.