Methods and systems for predictive heated water provision

A method of training machine learning algorithm (MLA) executing on a control module of a water provision system to determine a correlation between cold water usage and a subsequent heated water demand for the water provision system installed in a building, the water provision system comprising a heat pump configured to transfer thermal energy from outside the building to a thermal energy storage medium inside the building and the control module configured to control operation of the heat pump, the water provision system being configured to provide water heated by the thermal energy storage medium to an occupant of the building at one or more water outlets, the method comprising:

• receiving by the MLA from a sensor at a first water outlet first sensor data indicating cold water usage at the first water outlet;
• receiving by the MLA from a second sensor at a second water outlet second sensor data indicating heated water usage at the second water outlet subsequent to the cold water usage at the first water outlet;
• establishing by the MLA a degree of correlation between the first sensor data and the second data based on one or more factors, wherein the one or more factors comprise one or more of an elapse time between receiving the first sensor data and receiving the second sensor data, a location of the second water outlet in relation to the first water outlet, a frequency of receiving the second sensor data subsequent to receiving the first sensor data, a time of the day, a day of the week, or a combination thereof;
• determining a threshold of correlation, wherein a degree of correlation above the threshold indicates a reception of the first sensor data is likely to be followed by a heated water demand, the threshold of correlation enabling the control module to prepare the water provision system for delivering heated water.