In energy markets in which bidding process is used to sell energy, it is important that a mechanism for deciding bidding amount is in place. State of the art systems in this domain have the disadvantage that they rely on simulation data, and also they make certain assumptions, and both the factors can affect accuracy of results when the systems are deployed and are expected to handle practical scenarios. The disclosure herein generally relates to energy markets, and, more particularly, to a method and a system for Reinforcement Learning (RL) based model for generating bids. The system trains a RL agent using historical data with respect to competitor bids places and Market Clearing Prices (MCPs). The RL agent then processes real-time inputs and generates bidding recommendations.

Embodiments implement non-intrusive load monitoring using a novel learning scheme. A trained machine learning model configured to disaggregate device energy usage from household energy usage can be stored, where the machine learning model is trained to predict energy usage for a target device from household energy usage. Household energy usage over a period of time can be received, where the household energy usage includes energy consumed by the target device and energy consumed by a plurality of other devices. Using the trained machine learning model, energy usage for the target device over the period of time can be predicted based on the received household energy usage.

A system and method for determining seasonal energy consumption with the aid of a digital computer is provided. Through a power metering energy loads for a building situated in a known location are assessed as measured over a seasonal time period. Outdoor temperatures for the building are assessed as measured over the seasonal time period through a temperature monitoring infrastructure. A digital computer comprising a processor and a memory that is adapted to store program instructions for execution by the processor is operated, the program instructions capable of: expressing each energy load as a function of the outdoor temperature measured at the same time of the seasonal time period in point-intercept form; and taking a slope of the point-intercept form as the fuel rate of energy consumption during the seasonal time period.

A controller for a central plant having subplants operating to produce resources consumed by a building. The controller includes a processing circuit including a processor and memory storing instructions executed by the processor. The memory includes an offline rank generator that receives historical subplant allocation data and generates subplant ranks based on the historical subplant allocation data, each of the subplant ranks is associated with one of the subplants and defines a priority of each subplant with respect to production of a resource relative to other subplants that produce the resource. The memory also includes a high level optimizer that uses the subplant ranks associated with each of the subplants to determine resource allocation of the subplants according to the subplant ranks. The processing circuit is operates the subplants according to the resource allocation.

Cooperation in supply and demand balance of renewable energy in a region is implemented by operation of infrastructures of an infrastructure service such as water supply. In an energy management system including: a processor; and a storage device, the processor predicts an electric power supply amount utilizing renewable energy in a predetermined region, predicts an electric power demand amount in the region, predicts a demand amount of an infrastructure service different from an electric power service in the region, predicts an electric power demand amount corresponding to the infrastructure service on the basis of the predicted demand amount of the infrastructure service in the region, and determines use time of electric power corresponding to the infrastructure service such that the electric power demand amount in the region approaches the electric power supply amount.

A computer-implemented method for providing an estimated utility expenditure to a user may include: obtaining, via one or more processors, historical transactional data of one or more customers other than the user from one or more transactional entities, wherein the historical transactional data includes: at least one address of a given customer of the one or more customers; and a historical utility expenditure associated with the at least one address; generating, via the one or more processors, a heatmap based on the historical transactional data of the one or more customers via one or more algorithms, wherein the heatmap is indicative of at least the estimated utility expenditure associated with the at least one address during a predetermined period; and causing a display of a user device associated with the user to demonstrate the heatmap.

Various embodiments of the teachings herein include an energy management method for an energy system. The method may include: calculating a power prediction for use of an energy storage unit of the energy system for a time range; and determining, on the basis of the calculated power prediction, an externally usable partial storage capacity of the energy storage unit. The partial storage capacity according to the power prediction is not used by the energy system within the time range. The determined partial storage capacity is provided for use external to the energy system.

Provided are energy device control systems for distributed grid subsystem that control a first power demand of a plurality of appliances. The control system comprises a graphical user interface configured to accept a user input indicative of a first demand and dynamic allocation flexibility associated with the a respective energy device; a communication interface configured to aggregate dynamic allocation values from a plurality of system nodes including at least the user input indicative of a first demand and the dynamic allocation flexibility; and at least one processor programmed to: generate a learning model for evaluating dynamic future allocation with future energy execution prediction, wherein the dynamic future allocation includes at least energy operational information based on a categorization of energy usage at a plurality of respective energy devices; and trigger energy generation on the energy grid at respective generator nodes according to the learning model and dynamic projections.

Provided is a method for operating an electrical storage station on an electrical supply network. The network has electrical consumers, the storage station, and at least one wind power installation to generate electrical power from wind. The method includes generating electrical power by way of the installation as generated wind power, and feeding a feed-in power into the network. The electrical feed-in power at least results from the generated wind power and a storage power taken up or output by the storage station. The feeding of the feed-in power into the network is controlled depending on a station state of charge and a wind and/or power forecast. Changes in the feed-in power over time are controlled depending on the wind and/or power forecast and a limit gradient is specified to limit the changes in the feed-in power thereto. The limit gradient is specified depending on the wind and/or power forecast.

A controller, a system including such a controller, and a method for controlling or managing discharge or charge of a plurality of battery packs are provided. The controller includes one or more processor and at least one tangible, non-transitory machine readable medium encoded with one or more programs configured to perform steps to determining a voltage distribution parameter of each battery pack based on its maximum voltage, its minimum voltage for discharge, and a present voltage, assign ranks to the plurality of battery packs based on the voltage distribution parameters, and determine a respective power discharge or charge based on the rank of each battery pack and a total power demand. The controller provides signals with instructions to the plurality of battery packs and/or the one or more power converters for discharging power from or charging power to the plurality of battery packs.