Intelligent wireless energy sharing is provided. An ad hoc wireless mesh network that includes a plurality of energy nodes is formed using a set of machine learning models. The plurality of energy nodes is comprised of the energy node and a set of other energy nodes. A source energy node and a sink energy node in the plurality of energy nodes is identified using the set of machine learning models in response to forming the ad hoc wireless mesh network. Energy is wirelessly transferred from the energy node as the source energy node to the sink energy node via the ad hoc wireless mesh network utilizing a transceiver.

A power transfer system is presented including a smart road having a first power line, a second power line, and a plurality of wireless smart road power receive components, a plurality of electric vehicles (EVs) traveling on the smart road, each of the EVs having a wireless EV power transfer component and a wireless EV power receive component, and a power grid electrically connected to the first and second power lines of the smart road to receive power from the plurality of EVs as the plurality of EVs travel on the smart road.

A system for analyzing energy usage measures one or more parameters indicative of energy usage for a plurality of sub-circuits, where the sampling rate for the measuring is substantially continuous, and automatically transmits information related to at least one of the measured parameters at a rate that enables monitoring of current energy usage. The system further detects a significant change in a measured parameter, determines whether the significant change in the measured parameter is caused by a change in energy usage, and automatically transmits information related to the significant change in the measured parameter caused by the change in energy usage after detecting the significant change.

A load control system for controlling the amount of power delivered from an AC power source to a plurality of electrical load includes a plurality of energy controllers. Each energy controller is operable to control at least one of the electrical loads. The load control system also includes a first broadcast controller that has a first antenna and a second antenna. The first antenna is arranged in a first position and the second antenna is arranged in a second position that is orthogonal to the first position. The broadcast controller is operable to transmit a first wireless signal via the first antenna and a second wireless signal via the second antenna. Each of the energy controllers is operable to receive at least one of the first and second wireless signals, and to control the respective load in response to the received wireless signal.

The present disclosure provides a computer-implemented method for recommending one or more control schemes for controlling peak loading conditions and abrupt changes in energy pricing of one or more built environments associated with renewable energy sources. The computer-implemented method includes collection of a first set of statistical data, fetching of a second set of statistical data, accumulation of a third set of statistical data, reception of a fourth set of statistical data and gathering of fifth set of statistical data. Further, the computer-implemented method includes analysis of the first set of statistical data, the second set of statistical data, the third set of statistical data, the fourth set of statistical data and the fifth set of statistical data. In addition, the computer-implemented method includes recommendation of one or more control schemes to a plurality of energy consuming devices and a plurality of energy storage and supply means.

A computer-implemented method and system is provided. The system manipulates load curves corresponding to time-variant energy demand and consumption of a built environment. The system analyzes a first, second, third, fourth and a fifth set of data. The first set of data is associated with energy consuming devices. The second set of data is associated with an occupancy behavior of users. The third set of data is associated with energy storage and supply means. The fourth set of data is associated with environmental sensors. The fifth set of data is associated with energy pricing models. The system executes control routines for controlling peak loading conditions associated with the built environment. The system manipulates an optimized operating state of the energy consuming devices. The system integrates the energy storage and supply means for optimal reduction of the peak level of energy demand concentrated over the limited period of time.

A computer-implemented method and system is provided. The system adaptively switches prediction strategies to optimize time-variant energy demand and consumption of built environments associated with renewable energy sources. The system analyzes a first, second, third, fourth and a fifth set of statistical data. The system derives of a set of prediction strategies for controlled and directional execution of analysis and evaluation of a set of predictions for optimum usage and operation of the plurality of energy consuming devices. The system monitors a set of factors corresponding to the set of prediction strategies and switches a prediction strategy from the set of derived prediction strategies. The system predicts a set of predictions for identification of a potential future time-variant energy demand and consumption and predicts a set of predictions. The system manipulates an operational state of the plurality of energy consuming devices and the plurality of energy storage and supply means.

The present disclosure provides a computer-implemented method for prioritizing one or more instructional control strategies to reduce time-variant energy demand of a built environment associated with renewable energy sources. The computer-implemented method includes collection of a first set of statistical data, fetching of a second set of statistical data, accumulation of a third set of statistical data, reception of a fourth set of statistical data and gathering of fifth set of statistical data. Further, the computer-implemented method includes parsing and comparison of the first set of statistical data, the second set of statistical data, the third set of statistical data, the fourth set of statistical data and the fifth set of statistical data. In addition, the computer-implemented method includes identification and prioritization of one or more instructional control strategies to reduce the time-variant energy demand associated with the built environment.

Disclosed embodiments relate to a power metering system, a method and a system for monitoring power consumed by loads by using the power metering system. In some embodiments, the system for monitoring power consumed by loads includes an external power supply source, a renewable energy sources, a distribution board, one or more power metering systems, and a monitoring server.

Systems and methods for the creation of a centrally controlled DC and AC power rail system within a structure. The rails utilize a centralized controller along with a plurality of distributed controllers to allow for power in the rails to be selectively distributed or not distributed to outlets attached to the rails. This allows for power to be distributed without the need for users to utilize hardwired switches, but to instead utilize generally wireless switch modules, which may be implemented in hardware and/or software to control the outlets. It also allows for devices designed to utilize DC power to be directly supplied with such power from the DC power rail without the need to include onboard AC-DC converters with each device.