A method for determining a preferential minimum power set point by a consumer i, said consumer including an electric water heater, the method including determining the state of the consumer i at an instant k; determining a minimum power set point P.sub.c.sup.min(i,k) at the instant k as a function of the state of the consumer i determined during the determining of the state of the consumer i at an instant k; determining a minimum power set point P.sub.c.sup.min(i,k+1:K) at the instants k+1 to K as a function of the predicted state of the consumer i estimated from the state of the consumer i determined during the determining of the state of the consumer i at an instant k; determining a preferential minimum power set point P.sub.c.sup.min_pref(i,k) as a function of the minimum power set point P.sub.c.sup.min(i,k) at the instant k and of the minimum power set point P.sub.c.sup.min(i,k+1:K) at the instants k+1 to K.

Disclosed herein are WIFI and could enabled temperature control systems. The temperature control systems are configured to receive user temperature settings and preferences, and control a water heater based on the user temperature settings and preferences. The temperature control systems include two or more sampling rates for enabling a higher efficiency of operation compared to conventional water heaters.

There is provided a hot water supply system including: a controllable hot water supply outlet having when fully opened a given flowrate; a thermal energy store, containing an energy storage medium comprising a phase change material to store energy as latent heat, that is configured to receive energy from a source of renewable energy; a renewable energy source; the hot water supply system being operable, under the control of the processor, to heat water that is to be supplied to the hot water outlet to a target system supply temperature using a selection of one or more of the renewable energy source, energy from the thermal energy store, and optionally an auxiliary water heater intermediate the thermal energy store and the hot water supply outlet; wherein the thermal energy store has an energy storage capacity, when fully charged, that is sufficient to provide hot water to the hot water outlet, at the given flowrate, and at the target system supply temperature for a period of at least 8 minutes, and preferably at least 10 minutes; wherein the renewable energy source is also configured to provide building heating under control of the processor; the processor being configured to: monitor actual demand for hot water from the hot water supply system; predict future demand for hot water from the hot water supply system based on the monitored actual demand; pre-charge the thermal energy store so that sufficient energy will be stored in the thermal energy store to satisfy the predicted demand; and to temporarily divert heat from the renewable energy source to charge the phase change material rather than to provide building heating. A corresponding method is also provided.

There is provided a hot water supply system including: a controllable hot water supply outlet having when fully opened a given flowrate; a thermal energy store, containing an energy storage medium comprising a phase change material to store energy as latent heat, that is configured to receive energy from a source of renewable energy; a renewable energy source; the hot water supply system being operable, under the control of the processor, to heat water that is to be supplied to the hot water outlet to a target system supply temperature using a selection of one or more of the renewable energy source, energy from the thermal energy store, and optionally an auxiliary water heater intermediate the thermal energy store and the hot water supply outlet; wherein the thermal energy store has an energy storage capacity, when fully charged, that is sufficient to provide hot water to the hot water outlet, at the given flowrate, and at the target system supply temperature for a period of at least 8 minutes, and preferably at least 10 minutes; wherein the renewable energy source is also configured to provide building heating under control of the processor; the processor being configured to: monitor actual demand for hot water from the hot water supply system; predict future demand for hot water from the hot water supply system based on the monitored actual demand; pre-charge the thermal energy store so that sufficient energy will be stored in the thermal energy store to satisfy the predicted demand; and to temporarily divert heat from the renewable energy source to charge the phase change material rather than to provide building heating. A corresponding method is also provided.

The present disclosure provides a computer-implemented method of defrosting a heat pump of a water provision system installed in a building, the water provision system comprising the heat pump configured to transfer thermal energy from outside the building to a thermal energy storage medium inside the building and a 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 being performed by the control module and comprising: determining, based on performance of the heat pump, an expected start time of a next defrost cycle; and preparing the water provision system before the expected start time of the next defrost cycle.

The present disclosure provides a computer-implemented method of defrosting a heat pump of a water provision system installed in a building, the water provision system comprising the heat pump configured to transfer thermal energy from outside the building to a thermal energy storage medium inside the building and a 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 being performed by the control module and comprising: determining, based on performance of the heat pump, an expected start time of a next defrost cycle; and preparing the water provision system before the expected start time of the next defrost cycle.

The present disclosure provides a computer-implemented method of predictively preparing a 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 a control module configured to control operation of the heat pump, the control module having executing thereon a first machine learning algorithm, MLA, having previously been trained to determine a correlation between cold water usage and a subsequent heated water demand, 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 being performed by the control module and comprising: receiving first sensor data indicating cold water usage at a first water outlet; determining whether the cold water usage at the first water outlet is correlated to a subsequent heated water demand at a second water outlet by inputting the first sensor data to the first MLA; and upon determining that the cold water usage at the first water outlet is correlated to a subsequent heated water demand at a second water outlet, preparing the water provision system for delivering heated water.

The present disclosure provides a computer-implemented method of predictively preparing a 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 a control module configured to control operation of the heat pump, the control module having executing thereon a first machine learning algorithm, MLA, having previously been trained to determine a correlation between cold water usage and a subsequent heated water demand, 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 being performed by the control module and comprising: receiving first sensor data indicating cold water usage at a first water outlet; determining whether the cold water usage at the first water outlet is correlated to a subsequent heated water demand at a second water outlet by inputting the first sensor data to the first MLA; and upon determining that the cold water usage at the first water outlet is correlated to a subsequent heated water demand at a second water outlet, preparing the water provision system for delivering heated water.

An electrical power distribution control system configured to issue a demand response signal to cut power to a plurality of electrical power consuming loads within an electrical power distribution network to reduce a peak power demand within an electrical power grid during a peak power demand. Unlike conventional demand response systems, the controller in each consumer residence includes both a distributed control based on the ability to track individual 24 hour usage patterns and selectively delay the demand response signal on individual resistive heating loads based on usage patterns for the purpose of reducing a likelihood of consumers experiencing effects of the reduced peak power demand.

An electrical power distribution control system configured to issue a demand response signal to cut power to a plurality of electrical power consuming loads within an electrical power distribution network to reduce a peak power demand within an electrical power grid during a peak power demand. Unlike conventional demand response systems, the controller in each consumer residence includes both a distributed control based on the ability to track individual 24 hour usage patterns and selectively delay the demand response signal on individual resistive heating loads based on usage patterns for the purpose of reducing a likelihood of consumers experiencing effects of the reduced peak power demand.