In one aspect, there is provided a method comprising: receiving consumption data comprising readings from one or more utility meters associated with a property comprising one or more devices, the one or more devices comprising one or more devices of interest; determining, in the received consumption data, one or more positive consumption variations indicative of switching on of one or more of the devices and/or one or more negative consumption variations indicative of switching off of one or more of the devices; identifying one or more events associated with the one or more devices, based on the determined variations, by matching one or more positive variations with one or more negative variations; grouping the identified one or more events into one or more blocks, each block corresponding to an occurrence of usage of a device of the property; classifying the one or more blocks into one or more predetermined clusters, the one or more predetermined clusters comprising a respective predetermined cluster associated with each device of interest of the property; and determining an occurrence and/or an absence of usage of the one or more devices of interest of the property, based on the classification into the one or more predetermined clusters.

A whole building or campus entity, energy management and energy control optimization, data presentation and visualization dashboard system including a computer processor service provider system, wherein the computer processor service provider system comprises a cloud-based computer processor service provider system comprising: at least one application server comprising the whole building or campus entity, energy management and energy control optimization system executing in the cloud-based computer processor service provider system; and an onsite energy controller coupled to the cloud-based computer processor service provider system by an electronic communications network; wherein said onsite energy controller comprises: at least one electronic computer processor; and at least one electronic computer memory coupled to said at least one electronic computer processor; wherein said at least one electronic computer processor of said onsite energy controller is configured to: reduce energy and demand costs through whole building or campus entity energy management of a building or campus entity load including by being configured to at least one or more of: optimize energy usage; optimize energy generation; maximize demand reduction and utility savings through optimal control of: the energy usage, and the energy generation, or reduce energy costs; and use of at least one energy storage system device; simulate said building or campus entity load by use of at least one or more of: predictive analytics; or receive user inputs of an event schedule that impacts said building or campus entity load; perform optimization at the start of the billing cycle to establish a demand reduction target achievable with the at least one energy storage system device implemented; set a periodic schedule for discharge of the at least one energy storage system device and allocate a portion of energy storage capacity to a reserve to be used in the event of volatility in said building or campus entity load; determine whether there is unused capacity of the at least one energy storage system device at an end of a period, and responsive to the determination, redistribute any of the unused capacity to a remaining period of the periodic schedule; and reset the demand reduction target for each time-of-use period to what was achieved in the previous period for the same billing period; and wherein said at least one electronic computer processor of said onsite energy controller is configured to: display output interactively via an electronic dashboard graphical user interface (GUI) comprising at least one GUI element enabling interactive user receipt of input and provision of output relating to a

A method and system for controlling the transfer of electrical power between a first electrical network and a second electrical network is disclosed. The method includes receiving at the second electrical network pricing information from the first electrical network, the pricing information associated with the supply of electrical power between the first electrical network and the second electrical network and modifying a demand characteristic of the second electrical network based on the pricing information.

This disclosure relates generally to methods and systems for determining the power load disaggregation profile of a building. Most of the conventional techniques are algorithmic centric, specific to certain scenarios and does not employ the low-sampling rate data due to the complexity involved. Present disclosure determines the power load disaggregation profile of the building using the low-sampling rate power consumption data accurately. According to the present disclosure, firstly, the background power loads are detected and removed from the low-sampled data samples. Next, a robust event detection mechanism is employed to detect the events when the change in the power consumption occurred, and such events are paired using the iterative pairing technique. Further, a set of event clusters are formed using the density-based clustering technique and lastly, each of the set of event clusters are classified with each appliance type using a rule-based classification technique.

Systems and methods for automatically soliciting the purchase of a first or second machine-related resource in a forward market, wherein the first resource and the second resource are distinct instances of the same type of resource, are described. A sample system may include a fleet of machines, each having a resource requirement comprising at least two of: a compute resource, a spectrum resource, or a network bandwidth resource. The system may include an circuits to aggregate data corresponding to the machine-related resources from at least a behavioral data source, to determine a substitution cost of a second resource; to determine a machine-related resource acquisition value; and to automatically solicit a purchase, in a forward market, of one of the first resource or the second resource in response to the determined substitution cost of the second resource.

A method and device for controlling decentralized loads in an energy network, including at least two controllable loads, wherein the controllable loads are in particular energy consumers and/or energy storages and/or energy producers distributed over several buildings or building complexes. The loads are connected to a central energy supply by means of an energy supply line for providing power to the loads. In doing to, at least two controllable loads are provided. In the method, an inter-building load forecast is determined for several loads. In addition, an inter-building operational plan is created for at least two controllable loads on the basis of the inter-building load forecast. The controllable loads are driven on the basis of the inter-building operational plan and by means of the climate profiles of generated control data.

A system, including: a controlling unit for performing a first control for reducing power charging amounts for the multiple movable batteries, and/or a second control for increasing power supplying amounts from the multiple movable batteries to outsides, in response to a first request for requesting power consumption to be reduced, and a third control for increasing power charging amounts for the multiple movable batteries, and/or a fourth control for reducing power supplying amounts from the multiple movable batteries to outsides, in response to a second request for requesting power consumption to be increased; and an allocating unit for allocating by which of the first control, the second control, the third control, or the fourth control, the each of the multiple movable batteries provides the electrical power network with an electrical power resource in each of multiple timeframes in a future.

A computer-implemented method is provided for performing energy disaggregation of a distribution system-level net-load measurements using continuous-point-on-wave (CPOW) measurement units. The method uses a processor coupled with a memory stored instructions implementing the method using neural networks including an encoder network, a featuer extractor, a separator network, a decoder network stored in the memory, wherein the instructions, when executed by the processor carry out at steps of the method include generating net-load time series data from voltage and current measurements via the CPOW measurement units, generating a compressed latent space representation from the net-load time series, converting the net-load time series into time-frequency domain, passing time domain cotextual information with the converted time-frequency domain representation of net-load time series to the feature extractor, estimating two weight matrices to be multiplied with an output from the encoder network and learning temporal features of a native load and a photovoltaic (PV) generation, transforming weighted latent representation corresponding the native load and the PV generation into time-domain representations, and predicting the native load and the PV generation at distribution system-level from the transformed time domain representations corresponding to the native load and PV generations.

A fault-responsive power system and method using active line current balancing. First and second supply-side currents flowing from at least one power supply and into first and second conductor pairs, respectively, are measured. First and second remote-side currents flowing from the first and second conductor pairs and into first and second power converters, respectively, are measured. The outputs of the first and second power converters are electrically coupled together in parallel and deliver power to a load. The first and second remote-side currents are balanced in response to measurements of the first and second remote-side currents while power is being delivered. When a difference between the first and second supply-side currents at least meets a magnitude threshold, the first and second supply-side currents are reduced until the difference is less than the magnitude threshold.

An edge device, method and computer program product may perform, include or cause performance of various operations. The operations include determining an amount of electrical energy that an edge device is expected to have available from a local renewable energy system during each of a plurality of time periods and determining an amount of electrical energy that the edge device is expected to use for operations during each of the plurality of time periods. The operations further include identifying a first one of the time periods for which the determined amount of electrical energy that the edge device is expected to have available from the local renewable energy system is greater than the determined amount of electrical energy that the edge device is expected to use for operations and causing a cooling system that cools the edge device to perform an overcooling operation during the identified first time period.