Abstract: The present invention discloses a controlling method and the system for smart home, each home appliance records a user's plurality of times of operations, and each sensor module in home appliance collects information on multiple times of working modes of each appliance, sends to the router wirelessly; the router analyzes busiest working periods and according working modes of each appliance, based on multiple times' working modes of each appliance and according working periods; router transmits control signals wirelessly to radio module installed in each appliance, to control each corresponding appliance run automatically in the working mode corresponding to the busiest working periods; the said controlling method and system operates simply, controls automatically without any manual operations, close to users' usage habits, and more humanized, it has freed a user's hands, avoided relying too much on mobile terminals, brought a great convenience to users.

Abstract: A method of designing an optimized heating and cooling system includes: (1) automatically importing data from an energy model into an optimization model; (2) simulating energy use of a virtual heating and cooling system operating a thermal source or sink with the optimization model based upon the data from the energy model to obtain an optimized system design; (3) developing controls for an actual heating and cooling system based upon the optimized system design; and (4) automatically exporting the controls directly to a controller for the actual heating and cooling system.

Abstract: A power demand and supply control apparatus includes a target value acquiring unit, an information collecting unit, an adjustment amount determining unit, and a communication unit. The acquiring unit acquires a target value of a total adjustment amount. The collecting unit collects information of registered power amount of each of the requesting targets. The determining unit determines an adjustment amount for each of the requesting targets based on the registered power amount of each of the requesting targets and the target value of the total adjustment amount. The communication unit notifies each of the requesting targets of the adjustment amount determined. The information of the registered power amount includes information of an executable time and date. The determining unit extracts a requesting target that has the executable time and date included in the power adjustment execution time and date, and determines the adjustment amount.

Abstract: A photovoltaic energy system includes a photovoltaic field configured to convert solar energy into electrical energy, a power inverter configured to control an electric power output of the photovoltaic field, and a controller. The controller can determine optimal power setpoints for the power inverter by optimizing a value function that includes a penalty cost for failing to comply with a ramp rate limit and photovoltaic revenue.

Abstract: A system and method for creating and making use of customer profiles, including energy consumption patterns. Devices within a service point, using the active load director, may be subject to control events, often based on customer preferences. These control events cause the service point to use less power. Data associated with these control events, as well as related environment data, are used to create an energy consumption profile for each service point. This can be used by the utility to determine which service points are the best targets for energy consumption. In addition, an intelligent load rotation algorithm determines how to prevent the same service points from being picked first each time the utility wants to conserve power.

Abstract: A method for controlling power in a microgrid that includes power sources, loads and at least one connection to a main grid where a transformer is arranged to transfer electric power between the microgrid and the main grid is disclosed. The method includes: monitoring the power balance within the microgrid; monitoring the transformer, including monitoring the transformer temperature; and detecting a need for overloading the transformer based on the power balance within the microgrid. Especially, the method includes: determining a load profile for the transformer based on the power balance within the microgrid; determining a prognosis of the transformer temperature based on the load profile; and determining a schedule for power control of the microgrid, which determining of a schedule for power control includes analyzing the prognosis of the transformer temperature.

Abstract: An optimization system for a central plant includes a processing circuit configured to receive load prediction data indicating building energy loads and utility rate data indicating a price of one or more resources consumed by equipment of the central plant to serve the building energy loads. The optimization system includes a high level optimization module configured to generate an objective function that expresses a total monetary cost of operating the central plant over an optimization period as a function of the utility rate data and an amount of the one or more resources consumed by the central plant equipment. The optimization system includes a demand charge module configured to modify the objective function to account for a demand charge indicating a cost associated with maximum power consumption during a demand charge period. The high level optimization module is configured to optimize the objective function over the demand charge period.

Abstract: A method for balancing electric consumption generated by a plurality of electric loads includes: estimating an absorbed energy (Ek) by the loads in a predetermined time interval, and if a value of the estimation of absorbed energy by the loads is not within an interval defined by a minimum and a maximum consumption threshold, calculating a quantity of energy to be varied in accordance with a difference between the value of the estimation of the absorbed energy and an expected value of energy, determining a strategy comprising at least an action to be carried out to vary the energy supplied to the single loads, with an aim of reducing the difference between the estimated absorbed energy and the expected value thereof, actuating a scheduling of electric consumption in accordance with a determined strategy.

Abstract: The energy management method comprises a step of harvesting (E1) of an electrical energy from an energy source (1), notably from a renewable energy source and a step of distribution (E2) of the harvested electrical energy configured to be controlled in such a way as to, in a first configuration, inject at least a part of the harvested electrical energy into an electrical network (2), and, in a second configuration, inject at least a part of the harvested electrical energy into a processing system (3) configured in such a way as to store it in a tank (4). A step of control (E3) of the distribution of the electrical energy (E2) comprises a step of determination of a state of storage of the tank (4) and a step of determination of a time to delivery for at least a predetermined part of the content of the tank (4).

Abstract: A computer-implemented method includes receiving power measurement data for a photovoltaic (PV)-based energy generation (EG) sites, determining if cloud cover is present over the EG site based on a difference between a present and historical power output for the EG site, calculating a density of the cloud cover over the EG site based on the present and historical power outputs, and controlling load characteristics of the EG site based on the determined presence and calculated density of the cloud cover. The density of the cloud cover is based on a percentage difference in power output between the present power output and the historical power output. A vector for the cloud cover can be determined based on movement of a detected storm system with a boundary defined by a location of a plurality of EG sites, or by a movement of the cloud density from one EG site to the next.

Abstract: Described herein are methods and systems, including computer program products, for determining a load control schedule for energy control devices using a load shifting optimization model and applying the load control schedule to adjust the energy control devices. A server receives thermodynamic models, energy price data and energy load forecast data. The server generates price probability distribution curves based upon the price data and load probability distribution curves based upon the load forecast data. The server executes a load shifting optimization model to determine a profit probability distribution curve for demand response decision rules. The server determines a profit curve that has an optimal profit value. The server generates a load control schedule based upon the optimal profit curve and generates operational parameters for energy control devices using the load control schedule. The server transmits the operational parameters to the energy control devices to adjust operational parameters.

Abstract: A method of controlling an electricity production station including at least one renewable energy source and an energy accumulation system, allowing an operator to commit, at an electrical distribution network manager, to a power profile PG that the station will be able to deliver over a forthcoming time period. The declared power profile must, furthermore, comply with constraints imposed by the manager of the electricity distribution network. Non-compliance with this commitment may be subject to penalties. It is then incumbent on the operator to best optimize the method of controlling the electricity production station so as to maximize the electrical power fed into the network, while complying, in so far as possible, over a certain tolerance range, with the power profile commitment PG.

Abstract: The present application discloses a monitoring method and apparatus.

Abstract: A first estimator estimates first power to be generated by a photovoltaic power generation apparatus during an interested period. A second estimator estimates second power to be consumed by an electric load during the interested period. A power purchasing cost calculator calculates, when there is a shortfall in the first power compared to the second power, a cost to be paid for receiving, from a power grid, power for compensating for the shortfall. A determiner compares an amount of money to be paid to a customer facility in accordance with a trading term when power is supplied from the power storage apparatus to the power grid, with the cost calculated by the power purchasing cost calculator. A controller causes the power storage apparatus to the electric load when the cost to be paid to the customer facility is equal to or less than the cost.

Abstract: A system for managing devices supplied through a power grid is proposed. Each device is configured to consume over time electric power according to at least one respective power profile when operating. The system comprises at least one unit interfaced with the devices for exchanging data. The at least one unit collects power profile data indicative of the power profiles of the devices, and generates a time schedule of the device operations by distributing over time start times of the power profiles in such a way that at any time the total power consumption of the devices is kept under a maximum power threshold of the power grid.

Abstract: An apparatus for prioritizing a demand response program event based on occupancy for one or more buildings of one or more building types participating in a demand response program is provided. The apparatus has an occupancy estimator and a dispatch controller. The occupancy estimator is configured to receive energy consumption and outside temperature streams corresponding to a portion of the one or more buildings, and is configured to employ occupancy components for each of the one or more buildings within the portion to process the streams, and is configured to generate occupancy levels corresponding to the one or more buildings within the portion, where the occupancy components and the occupancy levels are generated by exclusively processing the streams, and is configured to assign the occupancy levels to remaining ones of the one or more buildings not in the first portion.

Abstract: A prediction value calculator calculates a prediction value of an electricity fee based on the amount of electric power supplied from a commercial electric power source to electric equipment within an electricity fee calculation period. A selector selects, as a first-stage control, either one control of an electric power saving control for the electric equipment and a charge-discharge control in which the electric power supplied from the commercial electric power source is stored in a storage battery in a first time slot and the electric power stored in the storage battery is supplied to the electric equipment in a second time slot in which the unit cost of the electric power supplied from the commercial electric power source is greater than that in the first time slot. A controller executes the first-stage control selected by the selector when the prediction value calculated by the prediction value calculator is greater than a targeted value of the electricity fee.

Abstract: In an approach for estimating solar array power generation for an installation of a solar array, a processor determines a location of an installation of a solar array. A processor determines, over a course of a specified time interval, a path of traversal of a source of energy relative to the location of the installation of the solar array. A processor scans an area surrounding the solar array. A processor estimates, based, at least in part, on the path of traversal of the source of energy and the area surrounding the solar array, an amount of power to be generated by the solar array.

Abstract: The present disclosure provides a fast model generating and solving method for security-constrained power system operation simulation, which includes: obtaining information of all branches and nodes which are involved during operation simulation time period, calculating original-node impedance matrix, load shifting distribution factor original matrix and generator shifting distribution factor original matrix of all involved branches; correcting the load shifting distribution factor original matrix and the generator shifting distribution factor original matrix according to the on-off state of branches; obtaining output of each generator unit at each time period according to no-security-constraint unit commitment model, and determining overload of each branch again; solving iteratively until no branch is overloaded, and obtaining output of each generator unit at each time period under security constraint of operation simulation for the current simulation day, performing operating simulation for the rest simulat

Abstract: A device includes a network interface and a processor. The network interface is configured to receive one or more preferences of a customer related to the charging of at least one backup device of the consumer. The network interface is also configured to receive at least one notification of at least one predicted power disturbance. The processor is configured to utilize the one or more consumer preferences and the at least one notification to generate a charging schedule of the at least one backup device and to generate one or more charging notifications for charging the at least one backup device. The one or more charging notifications are based on the charging schedule.

Abstract: A streams platform is used. Multiple streams of electricity usage data are received, each from an electrical meter providing periodic updates to electrical usage for devices connected to the electrical meter. Weather information is received corresponding to locations where the electrical meters are. Real-time predictive modeling of electricity demand is performed based on the received multiple streams of electricity usage data and the received weather information, at least by performing: updating a state space model for electrical load curves using the usage data from the streams and the weather, wherein the updating uses current load observations for the multiple streams for a current time period; and creating forecast(s) for the electricity demand. The forecast(s) of the electricity demand are output. Appliance-level predictions may be made and used, and substitution effects and load management functions may be performed.

Abstract: An energy audit system (1400), including at least one controller (1410) which may obtain attribute information (AI) for an energy consuming space, the AI including information related to a plurality of attributes and corresponding attribute choice information for each of the plurality of attributes; determine dependencies of each of the plurality of attributes; determine a ranking of each of the attributes based upon a dependency score of each of the attributes; determine an optimized workflow based upon the ranking of each of the attributes; and/or renders a user interface (UI) in accordance with the optimized workflow.

Abstract: Systems and methods are provided for estimating power breakdowns for a set of one or more appliances inside a building by exploiting a small number of power meters and data indicative of binary power states of individual appliances of such set. In one aspect, a breakdown estimation problem is solved within a tree configuration, and utilizing a single power meter and data indicative of binary power states of a plurality of appliances. Based at least in part on such solution, an estimation quality metric is derived. In another aspect, such metric can be exploited in a methodology for optimally placing additional power meters to increase the estimation certainty for individual appliances to a desired or intended level. Estimated power breakdown and energy breakdown—individually or collectively referred to as consumption breakdown—rely on measurements and numerical simulations, and can be evaluated in exemplary electrical network utilizing binary sensors.

Abstract: The present disclosure is directed to systems and methods for controlling an electrical system using simulation-based setpoints. Some embodiments include control methods that enable recalculation of the optimal setpoints during demand windows. Some embodiments include a multi-mode controller to control an electrical system in a charge mode and a demand mode. Some embodiments include techniques for load and generation learning and prediction. Some embodiments include consideration of external data, such as weather.

Abstract: An autonomous energy supply network has energy producers and energy consumers that are driven by a local control device. The following steps reduce the parameterization outlay required for the operation of the autonomous energy supply network: provision of model data of the autonomous energy supply network in a data memory of a computing device superordinate to the local control device, the model data specifying the respective energy producers and their operating parameters; determination of an operating plan for the autonomous energy supply network with the computing device by using the model data, the operating plan specifying the operating state of the autonomous energy supply network during a particular time interval; transmission of the operating plan to the local control device; and driving of the energy producers and/or the energy consumers according to the specifications of the operating plan by the local control device.

Abstract: The disclosed embodiments provide a system for processing data. During operation, the system obtains a set of components of a time-series performance metric associated with an anomaly in a performance of one or more monitored systems. For each component in the set of components, the system performs a statistical hypothesis test on the component to assess a deviation of the component from a baseline value of the component. When the statistical hypothesis test identifies a statistically significant deviation of the component from the baseline value, the system outputs an alert comprising a root cause of the anomaly that is represented by the statistically significant deviation of the component from the baseline value.

Abstract: An information processing method is described and includes establishing a first communication channel between a central control device and at least one smart home device through a first communication module of the central control device; downloading from a cloud device and installing an application program corresponding to the at least one smart home device; generating a first control instruction based on a control logic of a first application program upon obtaining description information of the first control instruction; and sending the first control instruction to a first smart home device corresponding to the first application program through the first communication channel so as to control the first smart home device to respond, wherein the description information of the first control instruction indicates changing a state of the first smart home device. A central control device is also described.

Abstract: Various embodiments manage energy generation in a power generation and distribution system. In one embodiment, a set of residual load data is obtained for a given period of time measured at one or more nodes within a power generation and distribution system. The set of residual load data encodes a set of power flow signals. The set of residual load data is analyzed. An amount of power contributed to the set of residual load data by at least one energy generator class is determined based on the analysis of the set of residual load data.

Abstract: A data center operable using only electric power based on renewable energy. The data center includes at least one device driven by the electric power, a storage battery for storing the electric power, and a controller for switching the operating mode of the device over the course of time on the basis of predicted values for the amount of electric power generated using renewable energy, the amount of electric power stored in the storage battery, and the amount of electric power consumed by the device.

Abstract: An energy management system is provided. The system includes an information collector which is configured to collect a first information in accordance with elapsing of time, including information on energy consumption of a subject of energy management or information on factors involved in a change of the energy consumption; and a display controller which is configured to collate a second information in which is included the first information, which is collected by the information collector, or specified energy performance for each second unit divided by one or a plurality of boundaries of an operating status which changes in accordance with the elapsing of time of the subject of energy management for each first unit which is divided by one or a plurality of physical or logical boundaries of the subject of energy management, based on the first information collected by the information collector to be the first unit and the second unit to cause the collated result to be displayed on a display.

Abstract: According to the inventive method, the power consumed on an electric grid (10), by preselected consumers (42, 44, 46, 48, 50, 52) being able to accept a predetermined decrease in power over a predetermined time range, is regulated. This method comprises the following successive steps: sending a control entity, regularly and by each preselected consumer (42, 44, 46, 48, 50, 52), a message (M1) containing the power consumed by said preselected consumer (42, 44, 46, 48, 50, 52); using the control entity (60) to compute a maximum power reduction value for all of the preselected consumers (42, 44, 46, 48, 50, 52) and a maximum duration of that power reduction; transmitting, using the control entity (60) and to a grid manager (62), computed values and maximum power durations; and sending, from the grid manager (62) to the control entity (60), a command order (79) commanding a power reduction for a selected duration.

Abstract: In a direct load control system supporting frequency control of an electrical grid, loads are grouped into a plurality of load aggregations based on electrical power draw characteristics of the loads, e.g. power draw magnitude characteristic and transient slew rate. A best-fit dispatch signal is generated for each load aggregation to match a desired load response with a sum of predicted load responses of the aggregations each operating in accord with its best-fit dispatch signal. For each load aggregation, its best-fit dispatch signal is communicated to the loads of the load aggregation, and the loads of the load aggregation are operated in accord with the best-fit dispatch signal communicated to the loads. The desired load response may be generated based on historical Automatic Generation Control (AGC) signal data and at least one of (1) a weather forecast and (2) daily, weekly, and seasonal cycles.

Abstract: A control device (100) includes a communication unit (102) which receives power consumption information of a power control target from an electric energy meter, an acquisition unit (104) which acquires demand control information from at least one power supply source, and a control unit (106) which controls operation of the power control target based on the power consumption information and the demand control information.

Abstract: A management system and a management method according to the invention calculate, on the basis of collected weather information, an operation record of a device, a state value and an acceptable range set for an area where the device is installed, and power-suppression-time during which electric power is suppressed, change-time during which the state value of the area is changed in response to the power-suppression-time by operations of the device, generate, using the calculated change-time, a control-schedule for controlling the device, and control the device. This enables reduction of electric power usage with the state value set for the area where the device is installed kept within the range in accordance with the power-suppression-time.

Abstract: A system and method for management of one or more grid-scale Energy Storage Systems (GSSs), including generating an optimal GSS schedule in the presence of frequency regulation uncertainties. The GSS scheduling includes determining optimal capacity deployment factors to minimize penalties for failing to provide scheduled energy and frequency regulation up/down services subject to risk constraints; generating a schedule for a GSS unit by performing co-optimization using the optimal capacity deployment factors, the co-optimization including tracking upper and/or lower bounds on a state of charge (SoC) and including the bounds as a hard constraints; and calculating risk indices based on the optimal scheduling for the GSS unit, and outputting an optimal GSS schedule if risk constraints are satisfied. A controller charges and/or discharges energy from GSS units based on the generated optimal GSS schedule.

Abstract: The disclosed subject matter provides systems and methods for allocating resources within an infrastructure, such as an electrical grid, in response to changes to inputs and output demands on the infrastructure, such as energy sources and sinks.

Abstract: A system and method for creating a charging schedule for an electric vehicle that include determining a current state of charge of the electric vehicle. The system and method also include determining an average price per kilowatt-hour of energy to charge the electric vehicle to reach at least one of: a target state of charge of the electric vehicle and a maximum state of charge of the electric vehicle. The system and method further include creating the charging schedule based on the current state of charge and the average price per kilowatt-hour of energy.

Abstract: 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.

Abstract: A method and apparatus for forecasting output power of wind turbine in a wind farm. The present invention provides a method for forecasting output power of a wind turbine in a wind farm, including: generating a corrected data set based on environmental data collected from at least one sensor in the wind farm; correcting a weather forecasting model by using the corrected data set; obtaining a forecast value of wind information at the wind turbine based on the corrected weather forecasting model; and forecasting the output power of the wind turbine based on the forecast value and a power forecasting model.

Abstract: Provided in some embodiments is an uninterruptable electrical power supply system. The system includes an electrical power distribution network (having a consumer side network coupled to one or more electrical loads that consume electrical power and a utility side network that supplies electrical power to the consumer side network), a primary power source (coupled to the utility side network, and that supplies electrical power to the utility side network for supply to the consumer side network), a secondary power source coupled to the utility side network, and a terminal between the consumer side network and the utility side network. The secondary power source supplies backup power to the utility side network (in response to a power shortage on the utility side network) and sinks surplus power from the utility side network (in response to a power surplus on the utility side network).

Abstract: Various examples are provided for power quality of service optimization for microgrids. In one example, among others, a microgrid includes a smart meter configured to control supply of electric power to loads based at least in part upon energy consumption scheduling of the loads, which is based at least in part upon an effective electric generation capacity associated with the microgrid. The energy consumption scheduling can be based at least in part upon estimated power output of a sustainable energy resource of the microgrid and a load demand characterization of the loads. In another example, a system comprises a plurality of microgrids and an advanced metering infrastructure (AMI) configured to monitor operations of the microgrids and to control supply of electric power from sustainable energy resources and energy storage systems to loads of the microgrids via smart meters based at least in part upon energy consumption scheduling of the loads.

Abstract: Apparatus and methods are disclosed for coordination of a population of Thermostatically Controlled Loads (TCLs) with unknown parameters to achieve group objectives including bidding and market clearing strategies designed to motivate self-interested users to realize efficient energy allocation subject to a peak power constraint. In one examples of the disclosed technology, a method of operating a load includes estimating a set of values for unmeasured parameters of the load's thermal environment based on output measurements of the thermal environment, determining an energy response based on the estimated set of values for the unmeasured parameters, and transmitting a bid for power for a finite time period based on the determined energy response to the coordinator. A clearing price is received from the coordinator responsive to the transmitted bid and power is sent to the load responsive to the received clearing price.

Abstract: HVAC load can be shifted to change indoor temperature. A time series change in HVAC load data is used as input modified scenario values that represent an HVAC load shape. The HVAC load shape is selected to meet desired energy savings goals, such as reducing or flattening peak energy consumption load to reduce demand charges, moving HVAC consumption to take advantage of lower utility rates, or moving HVAC consumption to match PV production. Time series change in indoor temperature data can be calculated using only inputs of time series change in the time series HVAC load data combined with thermal mass, thermal conductivity, and HVAC efficiency. The approach is applicable for both winter and summer and can be applied when the building has an on-site PV system.

Abstract: Systems and methods for energy distribution for one or more grid-scale Energy Storage Systems (ESSs), including generating one or more time series models to provide forecasted pricing data for one or more markets, determining battery life and degradation costs for one or more batteries in or more ESSs to provide battery life and degradation costs, optimizing bids for the one or more markets to generate optimal bids based on at least one of the forecasted pricing data or the battery life and degradation costs, and distributing energy to or from the one or more ESSs based on the optimal bids generated.

Abstract: A system is adapted to generate a configuration for a service provider system to provide a highly available (HA) service. The system first identifies type stacks that provide the HA service and one or more component types in each type stack. Each type stack is a combination of prototypes that describe features and capabilities of available software providing the HA service. The system estimates, for each component type in the type stacks, a mean-time-to-recover (MTTR) of the HA service based on time for completing an actual recovery action in response to a component failure. The system further estimates service availability provided by each type stack based on the MTTR and a mean-time-to-failure (MTTF) of each component type in the type stack. The system then eliminates one or more of the type stacks that do not satisfy a requested service availability before proceeding to subsequent steps of configuration generation.

Abstract: At least one grid (3000) distributes power to a consumer (7000). The grid (3000) transmits power to the other grid (3000). A grid integrated control apparatus (2000) includes a supply and demand energy information receiver unit (2030) that receives supply and demand energy information indicating a difference between energy supplied to the other grid (3000) and energy supplied from the other grid (3000) with respect to each grid (3000), a cost information receiver unit (2050) that receives cost information indicating a power transmission cost between the grids (3000), and a grid control unit (2020) that determines a transmitted energy between each grid (3000) so as to reduce the total of power transmission costs while satisfying power demand of each grid (3000), on the basis of the supply and demand energy information and the cost information.

Abstract: An energy storage device includes a device controller, an electrical energy converter, an energy storage medium and a power metering device. In a method of managing electrical power consumption by a group of the energy storage devices, a measured power level is output from the power metering device of each energy storage device. The power level represents a rate of electrical energy consumption by the electrical energy converter of the device. The measured power levels of each of the devices are communicated to a system controller using the device controllers. An aggregate power level is calculated based on the measured power levels, and is stored in memory using the system controller. In some embodiments, the aggregate power level is communicated to a remote location, such as an electrical power distribution system.

Abstract: A method for modeling energy efficiency of a site comprises: (1) receiving temperature data associated with the site from a sensor, the temperature data including a starting temperature of the site and a corresponding starting time of a climate control system, and an ending temperature of the site and a corresponding ending time of the climate control system; (2) receiving power consumption data associated with the site from a power meter, the power consumption data including power consumption of the climate control system at the site; and (3) determining the energy transfer function that models an energy efficiency of the site based at least partially on a time it takes for the site to reach the ending temperature from the starting temperature, and the power consumption of the climate control system from the starting time to the ending time, wherein the energy transfer function determines a predicted power consumption as a function of temperature change and time.

Abstract: A consumer electric power control system includes a consumer data storage unit, a consumer daily data creation unit, an electric power instruction value acquisition unit, an electric power reduction scenario creation unit, a consumer data prediction unit, a condition change indicator calculation unit configured to calculate a plurality of condition change indicators for a plurality of electric power reduction scenarios, each of the plurality of condition change indicators representing a degree of difference in tendency between consumer prediction data and consumer daily data, an equipment operation determination unit configured to determine one of the plurality of electric power reduction scenarios based on the plurality of condition change indicators and determine an equipment operation pattern based on the determined one electric power reduction scenario; and an equipment operation control unit configured to control various types of equipment based on the determined equipment operation pattern.

Abstract: An energy management system configured to manage operation states of load apparatuses installed in facility includes an instruction unit (14), a target evaluation unit (16), a presentation control unit (17) and a user device (60). According to a target power-saving ratio in a facility where load apparatuses (30) are installed, the instruction unit (14) controls operations of the load apparatuses (30). The user device (60) has a function for displaying information. The target evaluation unit (16) evaluates consumption of utility energy by the load apparatuses (30) with respect to a predetermined target value. The presentation control unit (17) controls so that the user device (60) displays an icon of which form varies according to an evaluation result by the target evaluation unit (16).