Abstract: The present disclosure provides systems and methods for managing a temperature of a battery energy storage system (“BESS”). A method may comprise identifying operating temperature limitations of the BESS; obtaining a forecast horizon comprising a forecast of external environmental conditions for a time period; identifying a charging/discharging schedule of the BESS; simulating operation of the BESS for the time period for each of a plurality of sequences of thermal management modes according to the charging/discharging schedule and the forecast horizon, the simulating generating an energy consumption and an operating temperature forecast of for each of the plurality of sequences of thermal management modes; selecting a sequence of thermal management modes of the plurality of sequences; and operating the equipment according to the selected sequence of thermal management modes.

Abstract: Techniques are presented for scheduling the charging of electric vehicles (EVs) that protect the resources of local low voltage distribution networks. From utilities, data on local low voltage distribution networks, such as the rating of a distribution transformer through which a group of EVs are supplied, is provided to a load manager application. Telematics information on vehicle usage is provided from the EVs, such as by way of the original equipment manufacturer. From these data, the load manager application determines schedules for charging the group of EVs through a shared low voltage distribution network so that the capabilities of the local low voltage distribution network are not exceeded while meeting the needs of the EV user. Charging schedules are then transmitted to the on-board control systems of the EVs for implementation.

Abstract: IR drop predictions are obtained using a maximum convolutional neural network. A circuit structure is partitioned into a grid. For cells of the circuit structure in sub-intervals of a clock period, power consumption of the cell is amortized into a set of grid tiles that include portions of the cell, thus forming a set of power maps. The power maps are applied to a neural network to generate IR drop predictions for the circuit structure.

Abstract: A method for operating an electric island power network having a renewable energy generation plant, a conventional energy generation plant, an energy store, and an energy consumer, includes: defining first operating parameters for the network for when a frequency and/or voltage of the network is outside defined limits; operating the network using the first operating parameters causing the frequency and voltage of the network to both be within the defined limits; defining second operating parameters for the network after the expiration of a defined time span over which the frequency and voltage have remained within the defined limits, the second operating parameters being defined such that operating the network using the second operating parameters causes the network to operate cost-optimally. If verified that the second operating parameters ensure that the frequency and voltage remain within the defined limits, operation using the second operating parameters is maintained. Otherwise, it is discontinued.

Abstract: Physical and/or financial instruments may optimally hedge the cash flow of one or more renewable energy generators based on a desired risk and return profile of renewable infrastructure investors. Baseline revenues may be determined based on forward-looking electricity market price scenarios corresponding to qualified market products intended for sale from the renewable energy generators. Risk and return metrics of cash flows of the renewable energy generators may be determined. At least one physical hedge and/or financial hedge may be added. The size and operation of the renewable energy generators along with any physical hedges, or financial hedges, or both physical and financial hedges, may be optimized across multiple market price scenarios of qualified market products to optimize investor-tailored risk and return utility functions.

Abstract: According to an embodiment, disclosed are a DC-DC converter for compensating for a ripple, in a solar linked energy storage system, and a control method thereof. In particular, disclosed is a DC-DC converter for compensating for a ripple generated in a DC link where a single phase inverter and a converter are connected. The DC-DC converter may obtain a frequency of a grid to compensate for the ripple.

Abstract: A method for designing a battery storage at a connection point of a load to an energy supply network. In the method, at least two different load types are determined. A load profile of the load before a connection and an operation of the battery storage is recorded. The load profile is evaluated and the load is assigned to one of the load types depending on the evaluation. The battery storage is designed so that the load is assigned to a different load type after the battery storage has been connected and operated.

Abstract: A method of managing energy by use of processing logic that comprises a load processor as a cloud service is provided. The method includes receiving power load information from a data collection system located at a building and using a cloud analysis layer that employs machine-learning and artificial intelligence for optimization control, analyzing the received power load information to disaggregate load waveform signals and identify device-based power loads by use of a neural network to perform historical device demand and performance analysis to generate device-based demand forecasting, generating demand forecasts for the building to mitigate peak demand based on analysis of a power draw signal and the generated device-based demand forecasting, and determining whether the generated demand forecast for the building is to peak in a near future, based on threshold values of at least one of generated device-based demand forecasting, power price or cost information, and user behavior analysis.

Abstract: A method for controlling production of one or more refined resources by an energy provider includes predicting a demand for the refined resources by one or more consumers of the refined resources as a function of an incentive offered by the energy provider. The method further includes performing an optimization of an objective function subject to a constraint based on the predicted demand for the refined resources to determine an amount of the refined resources for the energy provider to produce and a value of the incentive at multiple times within a time period. The method also includes providing setpoints for equipment of the energy provider that cause the equipment to produce the amount of the refined resources determined by performing the optimization.

Abstract: An approach and system incorporating obtaining a semantic model for a device, identifying properties from the semantic model, processing the properties, obtaining a property role, identifying a property role filter that correlates to the property role, and generating parameter values associated with the property role filter.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for performing thermal modeling. In one aspect, a method includes receiving monitoring data comprising temperature data measured inside a site, mode data, and state data, receiving weather data descriptive of weather at the site, and aligning the received temperature data, mode data, and state data with the received weather data. The method also includes determining an internal heat gain representing an amount of heat generated at the site irrespective of the heating or cooling system, determining at least one of a thermal product for the site or a thermal potential for the heating or cooling system, generating, based on the internal gain and the thermal product or the thermal potential, a thermal model for the site, and providing, as output, the generated thermal model.

Abstract: Systems, methods, and devices may enable management of a renewable power asset. A control device may generate a Day-Ahead (DA) pricing model, a Real-Time (RT) pricing model and a renewable generation model for the renewable power asset. Optimal DA commitments may be determined, and an optimal RT schedule estimated. A DA power delivery strategy and an RT power delivery strategy may be determined. The determined DA and RT power delivery strategies may be evaluated based on obtained real power prices. The DA and RT power delivery strategies may be redetermined, and the renewable power asset may be controlled to deliver power the DA and RT power delivery strategies. The value of the renewable power asset may be maximized while bounding financial risks and returns associated with scheduling the renewable power asset as tailored to risk preferences of the renewable power asset owner or operator.

Abstract: An autonomous electric vehicle comprises a battery electrically connected to the electric motor for powering the electric motor and a battery status prediction module to predict, based on event data from a mobile device of a user of the vehicle, whether the vehicle will be parked at a destination and a time period when the vehicle will be parked. The battery status prediction module predicts a predicted battery status at the end of the time period based on a temperature profile for the time period obtained from a remote weather server. The battery status prediction module determines if the predicted battery status at the end of the time period will have at least a minimum battery capacity to travel a distance to a charging station determined by the battery status prediction module from the destination and a location of the charging station.

Abstract: The present disclosure provides systems and methods for an operation of an electric power plant comprising a renewable energy resource and an energy storage device. The method may comprise determining, at a first time, a forecast of predicted energy production by the electric power plant over a time period subsequent to the first time based on a forecast for the time period; detecting a current state of charge of the energy storage device; calculating a range of automatic generation controls the electric power plant is capable of satisfying for the time period based on the forecast of predicted energy production and the detected current state of charge of the energy storage device; and signaling, from the electric power plant to a central utility controlling a power grid, the range of automatic generation controls the electric power plant is capable of satisfying for the time period.

Abstract: A rated power supply system powered by PoE receives power from external power sources and supplies power to PDs. The system connects with PDs through PoE output interfaces. The system has a PoE analog controller to turn on/off of the output power of all the PoE output interfaces. The PoE analog controller also detects the output current of all the PoE output interfaces. The system has a packet switch controller, a power state detecting circuit and a voltage conversion circuit. The voltage conversion circuit merges the power received from external power sources to generate a first voltage. The CPU calculates an output current upper limit based on the first voltage. The CPU gets a total output current from the PoE analog controller. If the total output current exceeds the output current upper limit, the CPU will turn off the PoE output interfaces according to power output priorities of the PoE output interfaces.

Abstract: A content consumption monitor (CCM) generates intent data that identifies topics of real-time interest to users. The CCM uses the intent data to direct information to more interested audiences and reduce the information noise/overload that may prevent users from viewing information. This targeted information may increase user conversion rates for seminars, advertising, documents downloads, or any other activity associated with published information. In one example, the CCM generates the intent data from third party content. Using a wide variety of third party content enables the CCM to better identify current user interests. The CCM may aggregate the intent data for different demographics, such as for companies, job positions, age, gender, or geographic locations.

Abstract: A storage device includes a non-volatile memory including a plurality of non-volatile memory cells, a buffer memory configured to temporarily store write data to be written to the non-volatile memory or read data read from the non-volatile memory, and a controller configured to receive a sleep mode signal from an external host. When the sleep mode signal is received by the controller, the controller is configured to block a first power supplied to the non-volatile memory and set the buffer memory to one of a first mode in which a second power is blocked from being supplied to the buffer memory and a second mode in which the buffer memory operates with low power. The write data stored in the buffer memory is written to the non-volatile memory when the buffer memory is set to the first mode.

Abstract: The present is directed to systems, methods, and devices for Zero Touch Provisioning (“ZTP”). The system can include a data center computing network, and at least one server coupled with the data center computing network. The server can receive a trigger signal from a triggering device coupled to a data center hardware network, transmit an executable script to the triggering device, which executable script instructs the triggering device to generate a definition file including a unique identifier of the triggering device, receive the definition file from the triggering device, identify a configuration file associated with the triggering device based at least in part on the unique identifier, and provision the triggering device by delivering the configuration file to the triggering device.

Abstract: Data samples are selected for power consumption management. This includes receiving samples associated with respective times, distributed in a sliding time window as current samples and in a past period as past samples. Selected past samples are determined by keeping a first share of the past samples, including most recent ones, and a second share through eliminating among the past samples deprived from the first share, called a complementary share, part of the past samples in function of at least some of the current samples and of elimination conditions depending on similarity criteria applied to at least the first and complementary shares. The selected past samples are provided with the current samples for power consumption management. Applications to power failure detection and power consumption dynamic adaptation.

Abstract: A method and system are provided to calculate a future behavioral data and identify a relative causal impact of external factors affecting the data. Behavioral data and data for one or more external factors are harvested for a first time period. New behavioral data is harvested for a second time period. New data for the second time period is harvested. Based on a second training algorithm, a forecast time series value of a future behavioral data for a third time period that is after the second time period is calculated. A relative causal impact between each external factor and the predicted time series value of the behavioral data, for the third time period, is identified.

Abstract: A distributed energy resource (DER) device is coupled to a utility meter in a “behind-the-meter” configuration. The utility meter analyzes a commitment generated by the DER device to determine a specific operation performed by the DER device at a particular time. The utility meter analyzes metrology data to identify an “event” associated with the particular time and then attempts to map the identified event back to the DER device based on a library of events associated with different DER devices. The utility meter also attempts to map the identified event to the specific operation set forth in the commitment. If the utility meter can successfully map the identified event to both the DER device and to the specific operation set forth in the commitment, then the utility meter generates a validated commitment. The validated commitment can be used to facilitate an energy market settlement process.

Abstract: Systems and methods for identifying optimal siting locations for an energy storage system. Siting locations are identified based on a value index derived from pricing data associated with a plurality of nodes on an electrical grid. An index is derived for each selected node of the plurality of nodes to produce a siting recommendation.

Abstract: The present invention belongs to the field of information technology, involving the techniques of fuzzy modeling, reinforcement learning, parallel computing, etc. It is a method combining granular computing and reinforcement learning for construction of long-term prediction interval and determination of its structure. Adopting real industrial data, the present invention constructs multi-layer structure for assigning information granularity in unequal length and establishes corresponding optimization model at first. Then considering the importance of the structure on prediction accuracy, Monte-Carlo method is deployed to learn the structural parameters. Based on the optimal multi-layer granular computing structure along with implementing parallel computing strategy, the long-term prediction intervals of gaseous generation and consumption are finally obtained. The proposed method exhibits superiority on accuracy and computing efficiency which satisfies the demand of real-world application.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for performing thermal modeling. In one aspect, a method includes receiving monitoring data comprising temperature data measured inside a site, mode data, and state data, receiving weather data descriptive of weather at the site, and aligning the received temperature data, mode data, and state data with the received weather data. The method also includes determining an internal heat gain representing an amount of heat generated at the site irrespective of the heating or cooling system, determining at least one of a thermal product for the site or a thermal potential for the heating or cooling system, generating, based on the internal gain and the thermal product or the thermal potential, a thermal model for the site, and providing, as output, the generated thermal model.

Abstract: The present disclosure relates to a power management method and apparatus, a computing device, a medium, and a product. The power management method includes a monitoring step, a prediction step, an error calculation step and an adjustment step including adjusting power supply plan or a power demand of a user when at least one of a first error is greater than a first predetermined threshold or a second error is greater than a second predetermined threshold.

Abstract: A power management method includes a step A of specifying an influence of a distributed power supply on a power demand-supply balance by a power management server, the power management server managing a plurality of facilities and the distributed power supply being individually provided in each of the plurality of facilities; and a step B of transmitting distributed power supply information from a local control apparatus to the power management server, the local control apparatus being individually provided in each of the plurality of facilities and the distributed power supply information including information indicating an operation state of the distributed power supply, wherein the step A includes a step of specifying the influence on the power demand-supply-demand balance based on the distributed power supply information.

Abstract: 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.

Abstract: Method includes recording biomarker information being indicative of at least one biological state of user remaining in lighting control environment over time frame, biomarker information being generated by at least one physiological sensor remaining with user in lighting control environment over time frame; recording light control settings for at least one light remaining in lighting control environment with user and with physiological sensor generating biomarker information over time frame; and training a neural network to determine correlations between biological state of user remaining in lighting control environment over time frame and lighting effects caused by at least one light remaining in lighting control environment with user over time frame, based on recordings of biomarker information and recordings of light control settings, and utilizing the correlations for controlling the at least one light. Computer readable medium for executing method.

Abstract: The present invention provides a method for determining consumption of appliances within a surveyed household in which there are no sensors for measuring consumption of specific appliances.

Abstract: A power transfer managing apparatus includes: a demand information acquiring unit that acquires information indicating power demand in a power network; a selecting unit that selects, as a vehicle to be caused to perform power transfer with the power network according to the power demand, a vehicle provided with a driving power source having an accumulated energy amount larger than a predetermined accumulation amount, and having an accumulatable energy amount larger than a predetermined accumulatable amount; and a notification control unit that makes a user of the vehicle that is selected by the selecting unit notified.

Abstract: A method for predicting when energy consumption will exceed normal production capacity for buildings including generating data sets for each of the buildings, where energy consumption values within each set are shifted by one of a plurality of lag values relative to time and temperature values, and where each lag value is different; performing a regression model analysis on each set to yield corresponding regression model parameters and a corresponding residual; determining a least valued residual indicating a corresponding energy lag for each of the buildings; using outside temperatures, regression model parameters, and energy lags for all of the buildings to estimate a cumulative energy consumption for the buildings, and to predict the time when energy consumption will exceed normal production capacity; and receiving the time when energy consumption will exceed normal production capacity, and preparing and commencing exceptional measures required to manage the energy consumption.

Abstract: The present invention relates to a power system having a plurality wind turbine generators and a power plant controller arranged to communicate with the plurality of wind turbines generators, where each of the plurality of wind turbine generator being related to a wind turbine controller, the wind turbine controller being arranged to control an active power output in its related wind turbine generator according to an active power set point received from the wind power plant controller; a first subset of wind turbine generators operating at an active power output unrestricted of the active power set point; and a second subset of wind turbine generators operating according to an active power set point; and wherein the wind power plant controller communicates the active power set point, in accordance with the active power output of the first subset of the plurality of wind turbines generators, so as to reduce active power fluctuation of the aggregated active power output of the first and second subset of the plu

Abstract: The present invention is generally directed to systems and methods for optimizing energy usage in a household. For example, methods for optimizing energy usage in a household may include steps of: receiving, using an energy optimization device, entire energy profile data associated with the household; obtaining, using the energy optimization device, time of use (TOU) energy pricing structure; processing, the entire energy profile data to generate disaggregated appliance level data related to one or more appliances used in the household; retrieving historical patterns of energy usage of the household during both peak and non-peak time periods; applying a behavior shift analysis on the disaggregated data based at least in part on the TOU energy pricing structure, disaggregated data, and historical patterns of the energy usage; and predicting potential energy savings based at least in part on the behavior shift analysis.

Abstract: A method for simulation of behavior and/or performance of an internet-of-things system uses an internet-of-things simulation environment. The simulated internet-of-things system includes at least a plurality of simulated internet-of-things communication devices in a communication network providing radio coverage in a predetermined geographical area. Via transmission of internet-of-things payload data between the simulated internet-of-things communication devices and the communication network, the simulated internet-of-things system provides a simulated internet-of-things service.

Abstract: The disclosure includes methods, systems and apparatus for predictive management of efficient selecting and receiving of retail electric utility service to a facility for a period, by automated selecting of a retail utility service provider corresponding to a selected least cost path of predicted rate plan choices across the period, wherein costs of all possible, viable time-bounded predicted rate plan choices are determined for predicted consumer usage where a predicted market of retail rate formulas for the period are predicted in relation to at least one variable, such as weather.

Abstract: A control system includes a controller that executes, a first control process including acquiring data indicating a state of the facility device, and performing an operation for control of the state of the facility device based on the acquired data, and a second control process including the acquisition and operational processes, in which an amount of processing is less than that of the first control process. The control system also includes a switch that performs, a control to maintain or switch a state of the first control process to the stop state, and switch a state of the second control process to an execution state, and a control to switch a state of one of the first and second control processes to the execution state, based on predicted amounts of decrease in an electric power cost of and increase in the operation cost.

Abstract: Systems, methods, and devices may enable management of a renewable power asset. A control device may generate a Day-Ahead (DA) pricing model, a Real-Time (RT) pricing model and a renewable generation model for the renewable power asset. Optimal DA commitments may be determined, and an optimal RT schedule estimated. A DA power delivery strategy and an RT power delivery strategy may be determined. The determined DA and RT power delivery strategies may be evaluated based on obtained real power prices. The DA and RT power delivery strategies may be redetermined, and the renewable power asset may be controlled to deliver power the DA and RT power delivery strategies. The value of the renewable power asset may be maximized while bounding financial risks and returns associated with scheduling the renewable power asset as tailored to risk preferences of the renewable power asset owner or operator.

Abstract: The present invention relates to a power system having a plurality wind turbine generators and a power plant controller arranged to communicate with the plurality of wind turbines generators, where each of the plurality of wind turbine generator being related to a wind turbine controller, the wind turbine controller being arranged to control an active power output in its related wind turbine generator according to an active power set point received from the wind power plant controller; a first subset of wind turbine generators operating at an active power output unrestricted of the active power set point; and a second subset of wind turbine generators operating according to an active power set point; and wherein the wind power plant controller communicates the active power set point, in accordance with the active power output of the first subset of the plurality of wind turbines generators, so as to reduce active power fluctuation of the aggregated active power output of the first and second subset of the plu

Abstract: An industrial information hub (IDH) serves as a single industrial ecosystem platform where multiple participants can deliver repeatable and standardized services relevant to their core competencies. The IIH system is centered around the development of an ecosystem that creates and delivers value to users—including industrial enterprises, OEMs, system integrators, vendors, etc.—through the aggregation of digital content and domain expertise. The IIH system serves as a trusted information broker between the ecosystem and the OT environments of plant facilities, and provides a platform for connecting assets, contextualizing asset data and providing secure access to the ecosystem. Additionally, the IIH system provides tools and support to OEMs and other subject matter experts, allowing those experts to enable their digital assets for use in the ecosystem. The IIH system reduces the cost and risks for digital modeling of industrial assets.

Abstract: A resource management system, which manages a resource consumed by a user, includes: a processor that executes a program; and a storage device that stores the program. The processor executes: a resource consumption amount acquisition process of acquiring a resource consumption amount of the user; a user characteristic estimation process of estimating characteristics of the user based on the resource consumption amount; a target calculation process of calculating a target value related to the resource consumption amount for which the user needs to cooperate based on the characteristics of the user estimated by the user characteristic estimation process; a cooperation request generation process of generating cooperation request data related to reduction of the resource consumption amount for the user based on the target value calculated by the target calculation process; and an output process of outputting the cooperation request data generated by the cooperation request generation process.

Abstract: Systems, methods and apparatuses are provided for reducing peak energy demand and to smooth intermittent energy profiles from onsite variable energy sources and loads. Some embodiments use system level and device level analysis and optimization to adaptively adjust the operation of a behind the meter energy storage (BMES) to smooth out energy generation variabilities and follow a reference load signal, including at short time resolutions.

Abstract: Energy and demand costs reduction method through management of an entity load using an energy storage system; simulating the entity load including using predictive analytics; or receiving user inputs of an event schedule impacting entity load; performing optimization at start of billing cycle including establishing a demand reduction target achievable with an energy storage system device implemented; setting a periodic schedule for energy storage system discharge and allocating a portion of energy storage capacity to a reserve to be used in event of volatility in entity load; determining any unused capacity of the energy storage system at an end of a period, and responsive to the determining, redistributing any unused capacity to a remaining period of the schedule; and resetting the demand reduction target for each time-of-use period to what was achieved previously for the same billing period.

Abstract: Systems, methods and apparatuses to generate a Fingerprint of a Physical Location for Placement of Virtual Objects are disclosed. In one aspect, embodiments of the present disclosure include a method, which may be implemented on a system, to generate a fingerprint of the physical location using the location metadata including one or more of, the geometric features and the physical objects. The method can further include tracking device movement in the physical location to determine a position of the device relative to the fingerprint of the physical location and rendering or placing the virtual object in the physical location based on the position of the device and the fingerprint of the physical location.

Abstract: A system for machine-learning for prediction grid carbon emissions includes a computing device configured to receive, from a first local grid monitoring device monitoring a first local grid, a plurality of first power output quantities of a plurality of power generators in the first local grid, train an emission projection machine-learning process, wherein training further includes compiling a plurality of training data entries, each training data entry correlating a plurality of past power output quantities with at least a reported carbon emission datum and training the emission projection machine-learning process using the plurality of training data entries, and generate, using the emission projection machine-learning process, a projected carbon emission rate as a function of the plurality of power output quantities.

Abstract: One aspect of the present disclosure involves dynamically performing power capping with respect to a group of computing systems. Different priority levels can be assigned to at least some of the individual computing systems within the group of computing systems. Individual power limits can be set for the plurality of individual computing systems based at least in part on the different priority levels and utilization levels of the plurality of individual computing systems. Another aspect of the present disclosure involves dynamically performing power capping with respect to various subsystems of a computing system. Different priority levels can be assigned to at least some of the plurality of individual subsystems within the computing system. Individual power limits can be set for the plurality of individual subsystems based at least in part on the different priority levels and current power consumption of the plurality of individual subsystems.

Abstract: A power distribution grid for a facility, such as a data center, is located within the facility. The power distribution grid includes a plurality of power transport elements arranged in a grid pattern and nodes located at intersections of the grid pattern. Electrical loads are supplied power via respective nodes of the power distribution grid. Also, each node is supplied power from more than two transport elements, such that one or more transport elements can fail and electrical loads connected to a particular node associated with the failed transport elements continue to receive electrical power supplied to the particular node from at least two different transport elements.

Abstract: A control system for a central plant having devices serving energy loads of a building. The control system includes a central plant optimizer wizard generator that receives user input and generates a central plant model for the control system. The central plant optimizer wizard generator includes an equipment model generator that receives user input and generates equipment models for the devices in the central plant, a device layer generator that generates device layers, an asset layer generator that generates asset layers, and a scaled load profile generator that generates a scaled building load profile of the building using the asset layers and the user data. The central plant optimizer wizard generator generates the central plant model using the asset layers and the scaled building load profile. A demand response optimizer uses the central plant model to determine control decisions for the devices included in the central plant.

Abstract: A system and method for identification of a landing site for aerial vehicles are provided. In one embodiment, the method includes processing image data pertaining to a potential site area. The method further includes identifying existing building infrastructure and defining boundaries of the identified existing building infrastructure in the potential site area, based on the processed image data. The method also includes identifying existing road infrastructure adjacent to the boundaries of the identified existing building infrastructure and defining boundaries of the identified existing road infrastructure, based on the processed image data. The method further includes calculating a feasibility score of the existing building infrastructure by analyzing the identified existing building infrastructure and the identified existing road infrastructure.

Abstract: A power management server comprises a controller configured to select based on a predetermined standard, from among a plurality of facilities including a distributed power supply, a first facility in which first processing is applied to the distributed power supply and a second facility in which second processing is applied to the distributed power supply. The first processing is processing of controlling an output power of the distributed power supply by using a first target value as a target value for purchased power purchased by the facility from a power provider. The second processing is processing of controlling the output power of the distributed power supply by using a second target value as the target value for the purchased power, the second target value being greater than the first target value.

Abstract: Disclosed is a laundry scheduling apparatus. The apparatus includes a communication unit, an output unit, and a processor configured to pair with at least one washing machine via the communication unit, obtain laundry preference parameters of a user generated by learning based on at least one of a deep learning algorithm or a machine learning algorithm, using at least one of a laundry log of the user or laundry satisfaction information of the user as input data, generate laundry scheduling information by using washing machine information about the paired at least one washing machine, the laundry preference parameters, and laundry item information obtained via at least one of a user input unit, an interface unit, or a sensor, and cause the output unit to output the laundry scheduling information.