An example operation includes one or more of determining a modified use of energy by an entity, based on a current environmental factor, and providing an energy offset to the entity based on the modified use. The offset derives an alternate benefit to overcome a negative balance by another entity.

A gateway system for a building comprises: a first relay to couple a first line of the gateway system to a first grid line of a power grid; a second relay to couple a second line of the gateway system to a second grid line of the power grid; a neutral line coupled to a neutral grid line of the power grid; a first electric-vehicle (EV) line coupled to the first line of the gateway system, the first EV line configured for being coupled to a first line of an EV charging connector; a second EV line coupled to the second line of the gateway system, the second EV line configured for being coupled to a second line of the EV charging connector, wherein the EV charging connector has no neutral line; and a balancing converter coupled to the first and second lines of the gateway system.

Embodiments generate machine learning predictions to discover target device energy usage. One or more trained machine learning models configured to discover target device energy usage from source location energy usage can be stored. Multiple instances of source location energy usage over a period of time can be received for a given source location. Using the trained machine learning model, multiple discovery predictions for the received instances of source location energy usage can be generated, the discovery predictions comprising a prediction about a presence of target device energy usage within the instances of source location energy usage. And based on the multiple discovery predictions, an overall prediction about a presence of target device energy usage within the given source location's energy usage over the period of time can be generated.

Methods, systems, and apparatus for an energy or fuel sharing network system. The energy or fuel sharing network system includes an in-house fuel cell apparatus that is coupled or included within a home. The in-house fuel cell apparatus includes a generation and distribution unit. The generation and distribution unit is configured to generate energy or fuel and provide the energy or fuel to a vehicle. The energy or fuel sharing network system includes an energy or fuel sharing platform. The energy or fuel sharing platform includes a processor. The processor is configured to determine a location of the in-house fuel cell apparatus, and provide the location of the in-house fuel cell apparatus to the vehicle or a user device.

This invention relates to a mobile charging unit (1), particularly for one or more electric vehicles (4), of the type including rechargeable batteries (41), comprising a mobile charging vehicle (2), and a charging apparatus (3), installed on said charging vehicle (2), having, in turn: an energy accumulation group (5), equipped with accumulators (51) for containing energy for charging said electric vehicles (4); an inverter (6), connected to said accumulators (51), comprising a DC-AC-DC converter (62) to convert the direct current coming from said accumulators (51) into alternating current, wherein said inverter (6) is connectable to an alternating current network (9) and is adapted to transform the alternating current of said alternating current network (9) into direct current for charging said accumulators (51); and an internal control system (7), connected to said inverter (6), adapted to control the operation of said inverter (6); said charging apparatus (3) is characterised in that said inverter (6) further comprises a direct current bus (63), connected to said DC-AC-DC converter (62), and a DC-DC converter (61), from direct current into direct current, connected to said direct current bus (63) and connectable to the rechargeable batteries (41) of said vehicle (4). This invention also relates to a method (200) for selecting a mobile charging unit (1) and a management system (100) for managing a mobile charging unit (1), the accumulated energy and the delivery of charges on request.

Techniques for performing an emissions demand response event are described. In an example, a cloud-based HVAC control server system obtains a history of emissions rates. Based on the history of emissions rates, a future time period of predicted high emissions is identified. An emission demand response event participation level of an account mapped to a thermostat is determined for the future time period of predicted high emissions. The emissions demand response event participation level may be one of a plurality of emissions demand response event participation levels. based on the emissions demand response event participation level of the account, an emissions demand response event is generated during the future time period of predicted high emissions. The cloud-based HVAC control server system then causes a thermostat to control an HVAC system in accordance with the generated emissions demand response event.

Cooling device 1, in particular a freezer 2, having a closable cooling space 3, an electrically operated cooling circuit, and preferably a cold storage pack 4, wherein the at least one closable cooling space 3 and the cold storage pack 4 can be cooled by the electrically operated cooling circuit. The cooling device has a power distributor 5 for distributing electrical power of at least one regenerative power source 6 to an electrically operated cooling circuit of the cooling device 1 and to at least one further electricity consuming device 7. In addition, the power distributor 5 has a control system with a computing unit 23, a memory 24 and priority logic. The priority logic is used to preferentially supply the electrically operated cooling circuit of the cooling device 1 with electricity if there is a lack of electrical power of the at least one regenerative power source 6.

A building system for building data point prediction, the building system comprising one or more memory devices configured to store instructions, that, when executed by one or more processors, cause the one or more processors to receive first building data for a building data point of a building and generate training data, the training data comprising a probability distribution sequence comprising a first probability distribution for the building data point. The instructions cause the one or more processors to train a prediction model based on the training data, receive second building data for the building data point, and predict, for one or more time-steps into the future, one or more second probability distributions with the second building data based on the prediction model, each of the one or more second probability distributions being a probability distribution for the building data point at one of the one or more time-steps.

Disclosed is an all-in-one emergency shed that can supply electricity and potable drinkable water during an emergency when electrical grid power is not available and water sources may be contaminated. The emergency shed can use battery banks from electric cars to store electrical energy for emergencies and can store electrical energy in electric car battery banks that are charged during off-peak times and resold to the utility company during peak demand periods.

A method for operating a power generating facility connected to a power distribution grid having an uncertain power generation condition includes predicting a probabilistic power flow forecast in a transmission line of the power distribution grid for a period of time, wherein the transmission line is electrically coupled to the power generating facility, predicting, using the probabilistic power flow forecast, a probability of congestion over the transmission line of the power distribution grid during the period of time, generating a mitigation plan, including a load adjustment on the transmission line, using the probability of congestion predicted over the transmission line and a thermal limit constraint of the transmission line, wherein the mitigation plan balances load adjustment and an overlimit line capacity on the transmission line, and controlling the power generating facility, using the mitigation plan, to achieve load modification and mitigate the probability of congestion predicted in the transmission line.