Information processing device and information processing method

Abstract

An information processing device (10) comprises: an acquisition unit (11) that acquires power demand prediction, power supply prediction, power unit price, and an incentive for power saving; a calculation unit (12) that calculates, on the basis of the acquired incentive, a power saving probability which is the probability of power saving by a consumer; and an optimization unit (13) that optimizes, by multi-objective optimization using the calculated power saving probability, at least one of a power cost and a power supply-demand gap on the basis of the power demand prediction, the power supply prediction, and the power unit price.

Description

Information processing device and information processing method

[0001] This disclosure relates to an information processing device and an information processing method.

[0002] In recent years, the concentration of electricity demand at certain times has placed an excessive burden on the supply infrastructure, hindering the efficient operation of power plants. As a countermeasure, Patent Document 1 below discloses a technology related to electricity supply and demand adjustment called demand response, in which consumers temporarily control the power they receive in response to fluctuations in the amount of electricity generated by the power supply company.

[0003] Japanese Patent Publication No. 2018-207745

[0004] Patent Document 1 discloses calculating the cost of responding to demand, including incentives for saving electricity, and determining the amount of electricity used for charging and discharging a battery and the amount of power consumption adjustment of the load that minimizes the sum of multiple costs, including the calculated demand response cost, over multiple time periods. However, Patent Document 1 does not describe calculating the probability that consumers will save electricity considering the provision of incentives, and there is room for improvement in optimizing costs related to power supply and demand adjustment by considering such probabilities.

[0005] Therefore, this disclosure aims to optimize at least one of electricity costs and the electricity supply-demand gap by multi-objective optimization using the probability that consumers will conserve electricity when incentives are provided.

[0006] The information processing device relating to this disclosure includes: an acquisition unit that acquires power demand forecasts, power supply forecasts, power unit prices, and incentives for saving electricity; a calculation unit that calculates a power saving probability, which is the probability that a consumer will save electricity, based on the incentives and the power unit prices; and an optimization unit that optimizes at least one of the power cost and the power supply-demand gap based on the power demand forecasts, power supply forecasts, and power unit prices by multi-objective optimization using the power saving probability.

[0007] According to this disclosure, at least one of electricity costs and the electricity supply-demand gap can be optimized by multi-objective optimization using the probability that consumers will conserve electricity when incentives are provided. This can greatly contribute to preventing electricity demand from being concentrated at certain times.

[0008] This is a diagram illustrating the overall system configuration, including the information processing device. It also shows a flowchart of the processes performed by the information processing device, an example of a generated control schedule, and an example of the information processing device's hardware configuration.

[0009] Hereinafter, an embodiment of the information processing apparatus and information processing method relating to this disclosure will be described with reference to the drawings.

[0010] [Configuration of the system including the information processing device] Figure 1 shows a configuration diagram of system 1 including the information processing device 10 according to this disclosure. As shown in Figure 1, system 1 comprises the information processing device 10 and the database 20.

[0011] Database 20 stores data used to optimize electricity costs and the electricity supply-demand gap. Electricity cost refers to the cost of electricity consumed during each time period. The electricity supply-demand gap is the difference between the amount of electricity supplied and the amount of electricity consumed during each time period. Examples of data stored in Database 20 include electricity demand forecasts, electricity supply forecasts, electricity unit prices, incentives for energy conservation, and customer energy conservation history.

[0012] Electricity demand forecasting is a prediction of the demand for electricity during each time period. Examples of electricity demand forecasting include time-series data of electricity demand from residences, commercial facilities, factories, etc. Electricity demand forecasting may also include the power used to charge electric vehicles (hereinafter also referred to as "EVs"). Electricity supply forecasting is a prediction of the amount of electricity supplied during each time period. Electricity supply forecasting includes electricity supply from thermal power plants, nuclear power plants, and renewable energy sources. Renewable energy sources include electricity supply from solar power plants, wind power plants, biomass power plants, geothermal power plants, hydroelectric power plants, tidal power plants, wave power plants, etc. Electricity supply from renewable energy sources may be modeled as a probability distribution, and data that takes uncertainty into account may be recorded in database 20. Electricity unit price is the unit price for the amount of electricity consumed. The electricity unit price may be set using dynamic pricing, where the price fluctuates, or it may be set using electricity prices calculated by a Virtual Power Plant (hereinafter also referred to as "VPP") for times of surplus or shortage of power generation. An incentive for saving electricity is an incentive provided for taking actions that lead to saving electricity. For example, this incentive may include supplying electricity to customers at an incentive price lower than the normal unit price when electricity demand is low. A customer's electricity saving history includes, for example, a history of when a customer took electricity-saving actions by time of day, and a history of when a customer took electricity-saving actions due to an incentive.

[0013] The information processing device 10 shown in Figure 1 can utilize various types of information processing devices as hardware (smartphones, mobile phones, smartwatches, wearable devices, laptops, desktop computers, servers, etc.).

[0014] The information processing device 10 includes an acquisition unit 11, a calculation unit 12, an optimization unit 13, and a generation unit 14 in order to realize the functions related to this disclosure. The functions of each unit will be described below.

[0015] The acquisition unit 11 is a functional unit that acquires data from the database 20 to be used for optimizing electricity costs and the electricity supply-demand gap. Specifically, the acquisition unit 11 acquires electricity demand forecasts, electricity supply forecasts, electricity unit prices, incentives for saving electricity, and the electricity saving history of consumers, which are stored in the database 20.

[0016] The calculation unit 12 is a functional unit that calculates the probability of a customer saving electricity, which is the probability that a customer will save electricity, based on the electricity unit price, incentives for saving electricity, and the customer's electricity saving history. The electricity saving probability is calculated, for example, using a logistic regression model. In this case, the electricity saving probability P at time t is... 節電 (t) is calculated by the following formula (1). ｔ C is the incentive rate per unit of time t. ｔ B is the electricity unit price at time t. ｔ This is the customer's power saving history, β ０ ~β ３ These are the coefficients for each of the explanatory variables in the logistic regression model.

[0017] The calculation unit 12 may classify the consumers by clustering and adjust the coefficients of the logistic regression model for each of the classified consumers. For example, the calculation unit 12 may classify consumers into three groups: an active energy-saving group that can be encouraged to change their behavior with little incentive, a price-sensitive group that immediately saves energy when the price of electricity rises, and a group that does not change their behavior and is unresponsive to changes in incentives and the price of electricity. As a clustering technique, for example, K-means clustering can be used. Once consumers are classified in this way, the probability of saving energy is calculated for each classified consumer. Therefore, the calculation unit 12 can calculate a more accurate probability of saving energy for each consumer.

[0018] The calculation unit 12 may update the power saving probability by reflecting the latest power saving actions. For example, the calculation unit 12 calculates the power saving probability P at time t based on the latest power saving actions. 最新 (t) is calculated, and the calculated power saving probability P 最新Bayesian update using (t) may be used to update the power saving probability P(t) at time t calculated based on the power saving history to the power saving probability P at time t+1 節電 (t+1). In this case, the power saving probability P at time t+1 節電 (t+1) is calculated by the following equation (2).

[0019] Here, α is calculated by the following equation (3).

[0020] Thereby, the power saving probability reflecting the latest power saving behavior is calculated. Therefore, the calculation unit 12 can calculate a more accurate and latest power saving probability.

[0021] The optimization unit 13 is a functional unit that optimizes power cost and power supply-demand gap by multi-objective optimization using the power saving probability. Multi-objective optimization refers to optimizing a plurality of objective functions in order to improve a plurality of characteristics that vary according to variables in a situation where a plurality of objective functions (characteristics) are given. A solution in which the plurality of objective functions are optimized is referred to as a Pareto solution. The objective function f for power cost in the multi-objective optimization of the present disclosure １ and the objective function f for power supply-demand gap ２ are expressed as the following equations (4) and (5). As parameters related to the objective function, C ｔ is defined as the unit power price at time t, P ｔ (x ｔ ) is defined as the power demand prediction at time t, and S ｔ is defined as the power supply prediction at time t. The power demand prediction P ｔ (x ｔ ) may include charging power of an electric vehicle (hereinafter also referred to as "EV"). The power supply prediction S ｔ may include supply power of renewable energy.

[0022] In the present disclosure, the optimization unit 13 optimizes the above objective functions in consideration of the power saving probability P 実測 (t). The objective functions considering the power saving probability are expressed as the following equations (6) and (7).

[0023] The optimization unit 13 may set constraints on parameters related to the objective function when performing optimization. For example, the power demand forecast P may be a constraint. ｔ (x ｔ The EV charging power included in ) is, Power supply forecast S ｔ Setting that charging is only possible when there is a surplus, power supply forecast S ｔ One possible measure is to set a higher incentive unit price if the probability of the renewable energy supply included in the plan falling below a predetermined supply amount is higher than a predetermined probability.

[0024] The generation unit 14 is a functional unit that generates control schedules for devices owned by the customer. Specifically, the generation unit 14 may generate control schedules for devices owned by the customer based on optimized power costs and power supply-demand gaps. Examples of devices include electric vehicles, storage batteries, and home appliances. Examples of home appliances include air conditioners, refrigerators, and washing machines. The generation unit 14 may transfer the generated control schedules to external output devices, etc.

[0025] [Regarding the processing performed in the information processing device] Next, an example of the processing performed in the information processing device 10 configured as described above (processing related to the information processing method of this disclosure) will be explained in accordance with the flowchart in Figure 2.

[0026] The information processing device 10 receives an optimization request from the acquisition unit 11 at set intervals, which requests the optimization of power costs and the power supply-demand gap. The acquisition unit 11 receives the optimization request, which triggers the start of the process shown in Figure 2. Alternatively, an optimization request generated by the operator of the information processing device 10 operating the information processing device 10 may be transferred to the acquisition unit 11, and the acquisition unit 11 receives the optimization request, which triggers the start of the process shown in Figure 2.

[0027] In the process of Figure 2, first, the acquisition unit 11 receives the aforementioned optimization request, and using the received optimization request as a trigger, the acquisition unit 11 acquires data used for optimizing the power cost and the power supply-demand gap from the database 20 (step S11). Specifically, the acquisition unit 11 acquires the power demand forecast, power supply forecast, power unit price, incentive for power saving, and power saving history of consumers stored in the database 20.

[0028] Next, the calculation unit 12 calculates the power saving probability of the consumer based on the power unit price, the incentive for power saving, and the power saving history of the consumer acquired by the acquisition unit 11 (step S12).

[0029] Next, the optimization unit 13 optimizes the power cost and the power supply-demand gap by multi-objective optimization using the power saving probability of the consumer calculated by the calculation unit 12 (step S13). Thereby, the optimization unit 13 calculates a Pareto solution in the objective functions of the power cost and the power supply-demand gap.

[0030] Then, the generation unit 14 generates a device control schedule based on the Pareto solution in the objective functions of the power cost and the power supply-demand gap optimized by the optimization unit 13 (step S14).

[0031] Figure 3 shows an example of the generated control schedule. As shown in Figure 3, the power that can be consumed for each time period by the devices owned by each consumer is displayed. For example, for the storage battery of consumer A, it is shown that control is performed to discharge 2 kW of power in the time period from 6:00 to 9:00, charge 3 kW of power in the time period from 9:00 to 15:00, and discharge 4 kW of power in the time period from 15:00 to 22:00.

[0032] According to the embodiments described above, it is possible to optimize the power cost and the power supply-demand gap by multi-objective optimization using the probability that a consumer will save power, in which the provision of incentives for power saving is taken into consideration. Accordingly, this can greatly contribute to preventing power demand from being concentrated in a partial time period.

[0033] [Various variations of System 1]

[0034] The calculation unit 12 shows an example of calculating a customer's energy saving probability based on the unit price of electricity, incentives for energy saving, and the customer's energy saving history. However, the calculation unit 12 does not necessarily need to use the customer's energy saving history when calculating the energy saving probability. If the customer's energy saving history is not used when calculating the energy saving probability, the acquisition unit 11 does not need to acquire the customer's energy saving history.

[0035] The optimization unit 13 has shown an example of optimizing both electricity costs and the electricity supply-demand gap, but it is also possible to optimize at least one of the electricity costs and the electricity supply-demand gap. In this case as well, it is possible to prevent electricity demand from being concentrated in certain times.

[0036] The optimization unit 13 may further optimize carbon dioxide emissions by multi-objective optimization. In this case, the objective function f for carbon dioxide emissions is ３ E is shown in the following equation (8). Here E ｔ This indicates the amount of carbon dioxide per unit of power consumption over a period of time t.

[0037] In this case, the optimization unit 13 calculates the Pareto solution by optimizing the objective functions of equations (6), (7), and (8), thereby optimizing electricity costs, the electricity supply-demand gap, and carbon dioxide emissions. This allows for the optimization of the objective function to include not only electricity costs and the electricity supply-demand gap, but also the environmental impact of carbon dioxide emissions.

[0038] Although the example shown illustrates the database 20 being implemented outside the information processing device 10, the database 20 may also be implemented inside the information processing device 10.

[0039] The gist of this disclosure is found in the following [1] to [8].

[0040] [1] An information processing device comprising: an acquisition unit that acquires electricity demand forecasts, electricity supply forecasts, electricity unit prices, and incentives for saving electricity; a calculation unit that calculates an electricity saving probability, which is the probability that a consumer will save electricity, based on the incentives and the electricity unit prices; and an optimization unit that optimizes at least one of electricity costs and the electricity supply-demand gap based on the electricity demand forecasts, electricity supply forecasts, and electricity unit prices by multi-objective optimization using the electricity saving probability.

[0041] [2] The information processing apparatus according to [1], wherein the acquisition unit further acquires the electricity saving history of the consumer, and the calculation unit further calculates the electricity saving probability based on the electricity saving history.

[0042] [3] The information processing apparatus according to [1] or [2], wherein the calculation unit calculates the power saving probability using a logistic regression model.

[0043] [4] The information processing apparatus according to [3], wherein the calculation unit classifies the customers by clustering and adjusts the coefficients of the logistic regression model for each of the classified customers.

[0044] [5] The information processing apparatus according to any one of [1] to [4], wherein the calculation unit calculates the latest power saving probability based on the latest power saving action and updates the power saving probability by Bayesian updating using the latest power saving probability.

[0045] [6] The information processing apparatus according to any one of [1] to [5], wherein the optimization unit further optimizes carbon dioxide emissions by the multi-objective optimization.

[0046] [7] The information processing apparatus according to any one of [1] to [6], further comprising a generation unit that generates a control schedule for a device owned by the customer based on at least one of the power cost and the power supply gap.

[0047] [8] An information processing method comprising: a step of an information processing device obtaining a power demand forecast, a power supply forecast, a power unit price, and an incentive for saving electricity; a step of the information processing device calculating a probability of saving electricity, which is the probability that a consumer will save electricity, based on the incentive and the power unit price; and a step of the information processing device optimizing at least one of the power cost and the power supply gap based on the power demand forecast, the power supply forecast, and the power unit price by multi-objective optimization using the probability of saving electricity.

[0048] [Explanation of terms, explanation of hardware configuration (Figure 4), etc.] The block diagram used in the description of the above embodiment shows functional units. These functional blocks (components) are realized by any combination of at least one of hardware and software. Furthermore, the method of realizing each functional block is not particularly limited. That is, each functional block may be realized using one device that is physically or logically coupled, or it may be realized using two or more physically or logically separated devices that are directly or indirectly connected (for example, using wired, wireless, etc.). A functional block may be realized by combining the above one device or the above multiple devices with software.

[0049] Functions include, but are not limited to, judgment, decision, determination, calculation, calculation, processing, derivation, investigation, exploration, confirmation, reception, transmission, output, access, resolution, selection, selection, establishment, comparison, assumption, expectation, assumption, broadcasting, notifying, communicating, forwarding, configuring, reconfiguring, allocating (mapping), and assigning. For example, a functional block (configuration part) that enables transmission is called a transmitting unit or transmitter. In all cases, as mentioned above, the method of implementation is not particularly limited.

[0050] For example, the information processing device in one embodiment of the present disclosure may function as a computer that performs processing of the information processing method of the present disclosure. Figure 4 is a diagram showing an example of the hardware configuration of the information processing device 10 according to one embodiment of the present disclosure. The above-described information processing device 10 may be physically configured as a computer device including a processor 1001, memory 1002, storage 1003, communication device 1004, input device 1005, output device 1006, bus 1007, etc.

[0051] In the following explanation, the term "device" can be replaced with "circuit," "device," "unit," etc. The hardware configuration of the information processing device 10 may include one or more of the devices shown in the figure, or it may be configured to omit some of the devices.

[0052] Each function in the information processing device 10 is realized by loading predetermined software (programs) onto hardware such as the processor 1001 and memory 1002, which allows the processor 1001 to perform calculations, control communication by the communication device 1004, and control at least one of data reading and writing in the memory 1002 and storage 1003.

[0053] The processor 1001 controls the entire computer, for example, by running an operating system. The processor 1001 may be composed of a central processing unit (CPU) that includes interfaces with peripheral devices, control units, arithmetic units, registers, etc.

[0054] Furthermore, the processor 1001 reads programs (program code), software modules, data, etc., from at least one of the storage 1003 and the communication device 1004 into the memory 1002 and executes various processes accordingly. The program used is one that causes the computer to execute at least a part of the operations described in the above embodiment. For example, the acquisition unit 11 of the information processing device 10 may be implemented by a control program stored in the memory 1002 and operated on the processor 1001, and other functional blocks may be implemented similarly. The above-described various processes have been explained as being executed by one processor 1001, but they may be executed simultaneously or sequentially by two or more processors 1001. The processor 1001 may be implemented by one or more chips. The program may also be transmitted from a network via a telecommunications line.

[0055] The memory 1002 is a computer-readable recording medium and may consist of at least one of the following: ROM (Read Only Memory), EPROM (Erasable Programmable ROM), EEPROM (Electrically Erasable Programmable ROM), RAM (Random Access Memory), etc. The memory 1002 may also be called a register, cache, main memory, etc. The memory 1002 can store executable programs (program code), software modules, etc., for carrying out a wireless communication method according to one embodiment of the present disclosure.

[0056] The storage 1003 is a computer-readable recording medium and may consist of at least one of the following: an optical disc such as a CD-ROM (Compact Disc ROM), a hard disk drive, a flexible disk, a magneto-optical disk (e.g., a compact disc, a digital multipurpose disc, a Blu-ray® disc), a smart card, flash memory (e.g., a card, a stick, a key drive), a floppy® disk, a magnetic strip, etc. The storage 1003 may also be called an auxiliary storage device. The above-mentioned storage medium may be, for example, a database, server, or other suitable medium including at least one of the memory 1002 and the storage 1003.

[0057] The communication device 1004 is hardware (transceiver / receiver device) for communicating between computers via at least one of a wired network and a wireless network, and is also referred to as a network device, network controller, network card, communication module, etc. The communication device 1004 may be configured to include, for example, a high-frequency switch, duplexer, filter, frequency synthesizer, etc., in order to implement at least one of frequency division duplex (FDD) and time division duplex (TDD).

[0058] The input device 1005 is an input device that accepts input from an external source (e.g., a keyboard, mouse, microphone, switch, button, sensor, etc.). The output device 1006 is an output device that outputs to an external source (e.g., a display, speaker, LED lamp, etc.). The input device 1005 and the output device 1006 may be configured as an integrated unit (e.g., a touch panel).

[0059] Furthermore, each device, such as the processor 1001 and the memory 1002, is connected by a bus 1007 for communicating information. The bus 1007 may be configured using a single bus, or different buses may be configured for each device.

[0060] Furthermore, the information processing device 10 may be configured to include hardware such as a microprocessor, a digital signal processor (DSP), an ASIC (Application Specific Integrated Circuit), a PLD (Programmable Logic Device), and an FPGA (Field Programmable Gate Array), and some or all of each functional block may be realized by such hardware. For example, the processor 1001 may be implemented using at least one of these hardware components.

[0061] The notification of information is not limited to the embodiments described herein and may be carried out by other means. For example, the notification of information may be carried out by physical layer signaling (e.g., DCI (Downlink Control Information), UCI (Uplink Control Information)), upper layer signaling (e.g., RRC (Radio Resource Control) signaling, MAC (Medium Access Control) signaling, broadcast information (MIB (Master Information Block), SIB (System Information Block))), other signals, or combinations thereof. RRC signaling may also be called RRC messages, and may be, for example, RRC Connection Setup messages, RRC Connection Reconfiguration messages, etc.

[0062] Each aspect / embodiment described in this disclosure refers to LTE (Long Term Evolution), LTE-A (LTE-Advanced), SUPER 3G, IMT-Advanced, 4G (4th generation mobile communication system), 5G (5th generation mobile communication system), 6th generation mobile communication system (6G), xth generation mobile communication system (xG) (xG (where x is, for example, an integer or decimal)), FRA (Future Radio Access), NR (new Radio), New radio access (NX), Future generation radio access (FX), W-CDMA (registered trademark), GSM (registered trademark), CDMA2000, UMB (Ultra Mobile Broadband), IEEE 802.11 (Wi-Fi (registered trademark)), IEEE 802.16 (WiMAX (registered trademark)), IEEE 802.20 may apply to at least one system utilizing UWB (Ultra-WideBand), Bluetooth®, or other appropriate systems, and to next-generation systems extended, modified, created, or defined based thereon. Alternatively, multiple systems may be applied in combination (e.g., a combination of at least one of LTE and LTE-A with 5G).

[0063] The processing procedures, sequences, flowcharts, etc., of each aspect / embodiment described in this disclosure may be reordered, provided they do not contradict each other. For example, the methods described in this disclosure present various step elements using exemplary order and are not limited to the specific order presented.

[0064] The specific operations described in this disclosure as being performed by a base station may, in some cases, be performed by its upper node. In a network consisting of one or more network nodes having a base station, it is clear that various operations performed for communication with a terminal can be performed by the base station and at least one other network node (for example, an MME or S-GW, but not limited to these). Although the above example illustrates the case where there is one other network node besides the base station, it may also be a combination of multiple other network nodes (for example, an MME and an S-GW).

[0065] Information can be output from a higher layer (or lower layer) to a lower layer (or higher layer). Input and output may also occur via multiple network nodes.

[0066] Input and output information may be stored in a specific location (e.g., memory) or managed using a management table. Input and output information may be overwritten, updated, or appended to. Output information may be deleted. Input information may be transmitted to other devices.

[0067] The determination may be made by a value represented by one bit (0 or 1), by a boolean value (true or false), or by a numerical comparison (for example, a comparison with a predetermined value).

[0068] Each aspect / embodiment described in this disclosure may be used individually, in combination, or switched between as needed during implementation. Furthermore, notification of specific information (e.g., notification that "X is") is not limited to explicit notification, but may also be implicit (e.g., by not providing such notification).

[0069] Although the present disclosure has been described in detail above, it will be clear to those skilled in the art that the present disclosure is not limited to the embodiments described herein. The present disclosure can be implemented in modified and altered forms without departing from the intent and scope of the present disclosure as defined by the claims. Therefore, the descriptions in the present disclosure are illustrative and not intended to be restrictive in any way.

[0070] Software should be broadly interpreted to mean instructions, instruction sets, code, code segments, program code, programs, subprograms, software modules, applications, software applications, software packages, routines, subroutines, objects, executable files, execution threads, procedures, functions, and so on, whether they are called software, firmware, middleware, microcode, hardware description languages, or by any other name.

[0071] Furthermore, software, instructions, information, etc., may be transmitted and received via a transmission medium. For example, if software is transmitted from a website, server, or other remote source using at least one of wired technology (such as coaxial cable, fiber optic cable, twisted pair, or digital subscriber line (DSL)) and wireless technology (such as infrared or microwave), then at least one of these wired and wireless technologies is included in the definition of a transmission medium.

[0072] The information, signals, etc. described in this disclosure may be represented using any of the various different techniques. For example, the data, instructions, commands, information, signals, bits, symbols, chips, etc. that may be referred to throughout the above description may be represented by voltage, current, electromagnetic waves, magnetic fields or magnetic particles, optical fields or photons, or any combination thereof.

[0073] In addition, terms used in this disclosure and terms necessary for understanding this disclosure may be replaced with terms having the same or similar meanings. For example, at least one of the channel and symbol may be a signal (signaling). Also, a signal may be a message. Furthermore, a component carrier (CC) may be called a carrier frequency, cell, frequency carrier, etc.

[0074] The terms “system” and “network” as used in this disclosure are interchangeable.

[0075] Furthermore, the information, parameters, etc., described in this disclosure may be expressed using absolute values, relative values ​​from a given value, or other corresponding information. For example, wireless resources may be indicated by an index.

[0076] The names used for the parameters described above are not restrictive in any way. Furthermore, the formulas and other expressions using these parameters may differ from those expressly disclosed in this disclosure. Various channels (e.g., PUCCH, PDCCH, etc.) and information elements can be identified by any suitable name, and therefore, the various names assigned to these various channels and information elements are not restrictive in any way.

[0077] In this disclosure, terms such as “Base Station (BS),” “wireless base station,” “fixed station,” “NodeB,” “eNodeB (eNB),” “gNodeB (gNB),” “access point,” “transmission point,” “reception point,” “transmission / reception point,” “cell,” “sector,” “cell group,” “carrier,” and “component carrier” may be used interchangeably. Base stations may also be referred to by terms such as macrocell, small cell, femtocell, and picocell.

[0078] A base station can accommodate one or more (e.g., three) cells. If a base station accommodates multiple cells, the entire coverage area of ​​the base station can be divided into multiple smaller areas, each of which may also be provided with communication services by a base station subsystem (e.g., a Remote Radio Head (RRH)). The terms “cell” or “sector” refer to part or all of the coverage area of ​​at least one of the base station and / or base station subsystems that provide communication services in that coverage.

[0079] In this disclosure, the transmission of information by a base station to a terminal may be interpreted as the base station instructing the terminal to perform control or operation based on the information.

[0080] In this disclosure, terms such as "Mobile Station (MS)," "user terminal," "User Equipment (UE)," and "terminal" may be used interchangeably.

[0081] A mobile station may also be referred to by those skilled in the art as a subscriber station, mobile unit, subscriber unit, wireless unit, remote unit, mobile device, wireless device, wireless communication device, remote device, mobile subscriber station, access terminal, mobile terminal, wireless terminal, remote terminal, handset, user agent, mobile client, client, or several other appropriate terms.

[0082] At least one of the base station and the mobile station may be called a transmitting device, a receiving device, a communication device, etc. At least one of the base station and the mobile station may also be a device mounted on a mobile unit, the mobile unit itself, etc.

[0083] The term "moving object" refers to any object that can move, regardless of its speed. This also includes cases where the moving object is stationary. The term "moving object" includes, but is not limited to, vehicles, transport vehicles, automobiles, motorcycles, bicycles, connected cars, excavators, bulldozers, wheel loaders, dump trucks, forklifts, trains, buses, handcarts, rickshaws, ships and other watercraft, airplanes, rockets, satellites, drones (registered trademarks), multicopters, quadcopters, balloons, and anything carried on them.

[0084] Furthermore, the mobile entity may be one that autonomously drives based on operational commands. It may be a vehicle (e.g., a car, an airplane), an unmanned mobile entity (e.g., a drone, an autonomous vehicle), or a robot (manned or unmanned). Note that at least one of the base station and the mobile station may be a device that does not necessarily move during communication operations. For example, at least one of the base station and the mobile station may be an IoT (Internet of Things) device such as a sensor.

[0085] Furthermore, the term "base station" in this disclosure may be interpreted as "user terminal." For example, the various aspects / embodiments of this disclosure may be applied to a configuration in which communication between a base station and a user terminal is replaced with communication between multiple user terminals (which may be called, for example, D2D (Device-to-Device), V2X (Vehicle-to-Everything), etc.). Also, terms such as "uplink" and "downlink" may be interpreted as terms corresponding to terminal-to-terminal communication (for example, "side"). For example, uplink channel, downlink channel, etc., may be interpreted as side channel.

[0086] As used in this disclosure, the terms “determining” and “determining” may encompass a wide variety of actions. “Determining” may include, for example, judging, calculating, computing, processing, deriving, investigating, looking up, searching, or inquiring (e.g., searching in a table, database, or other data structure), or ascertaining. “Determining” may also include receiving (e.g., receiving information), transmitting (e.g., sending information), inputting, outputting, or accessing (e.g., accessing data in memory). Furthermore, "judgment" and "decision" can include considering something as having been "judged" or "decided" after resolving, selecting, choosing, establishing, comparing, etc. In other words, "judgment" and "decision" can include considering something as having been "judged" or "decided" after some action. Also, "judgment (decision)" can be reinterpreted as "assuming," "expecting," or "considering."

[0087] The terms “connected,” “coupled,” or any variation thereof, mean any direct or indirect connection or coupling between two or more elements, and may include the presence of one or more intermediate elements between two elements that are “connected” or “coupled” with each other. The coupling or connection between elements may be physical, logical, or a combination thereof. For example, “connection” may be reinterpreted as “access.” As used in this disclosure, two elements may be considered to be “connected” or “coupled” with each other using at least one of one or more wires, cables, and printed electrical connections, and, in some non-limiting and non-exclusive examples, electromagnetic energy having wavelengths in the radio frequency domain, microwave domain, and optical (both visible and invisible) domain.

[0088] In this disclosure, the phrase "based on" does not mean "based solely on" unless otherwise specified. In other words, the phrase "based on" means both "based solely on" and "based at least on."

[0089] Any reference to elements using the designations “first,” “second,” etc., as used in this disclosure does not generally limit the quantity or order of those elements. These designations may be used in this disclosure as a convenient way to distinguish between two or more elements. Accordingly, references to the first and second elements do not imply that only two elements may be employed, or that the first element must precede the second element in any way.

[0090] In the configuration of each of the above devices, "means" may be replaced with "part," "circuit," "device," etc.

[0091] Where the terms “include,” “including,” and variations thereof are used in this disclosure, these terms are intended to be inclusive, as is the term “comprising.” Furthermore, the term “or” as used in this disclosure is not intended to mean exclusive OR.

[0092] In this disclosure, if articles are added through translation, such as a, an, and the in English, this disclosure may include the fact that the noun following these articles is plural.

[0093] In this disclosure, the term "A and B are different" may mean "A and B are different from each other." The term may also mean "A and B are each different from C." Terms such as "separate" and "combine" may be interpreted similarly to "different."

[0094] 1...System, 10...Information processing device, 11...Acquisition unit, 12...Calculation unit, 13...Optimization unit, 14...Generation unit, 20...Database, 1001...Processor, 1002...Memory, 1003...Storage, 1004...Communication device, 1005...Input device, 1006...Output device, 1007...Bus.

Claims

1. An information processing device comprising: an acquisition unit that acquires electricity demand forecasts, electricity supply forecasts, electricity unit prices, and incentives for saving electricity; a calculation unit that calculates an electricity saving probability, which is the probability that a consumer will save electricity, based on the incentives and the electricity unit prices; and an optimization unit that optimizes at least one of electricity costs and the electricity supply-demand gap based on the electricity demand forecasts, electricity supply forecasts, and electricity unit prices by multi-objective optimization using the electricity saving probability.

2. The information processing apparatus according to claim 1, wherein the acquisition unit further acquires the customer's power saving history, and the calculation unit further calculates the power saving probability based on the power saving history.

3. The information processing apparatus according to claim 1, wherein the calculation unit calculates the power saving probability using a logistic regression model.

4. The information processing apparatus according to claim 3, wherein the calculation unit classifies the customers by clustering and adjusts the coefficients of the logistic regression model for each of the classified customers.

5. The information processing apparatus according to claim 1, wherein the calculation unit calculates the latest power saving probability based on the latest power saving actions, and updates the power saving probability by Bayesian updating using the latest power saving probability.

6. The information processing apparatus according to claim 1, wherein the optimization unit further optimizes carbon dioxide emissions by the multi-objective optimization.

7. The information processing apparatus according to claim 1, further comprising a generation unit that generates a control schedule for a device owned by the customer based on at least one of the power cost and the power supply-demand gap.

8. An information processing method comprising: a step of an information processing device obtaining a power demand forecast, a power supply forecast, a power unit price, and an incentive for saving electricity; a step of the information processing device calculating a probability of saving electricity, which is the probability that a consumer will save electricity, based on the incentive and the power unit price; and a step of the information processing device optimizing at least one of the power cost and the power supply gap based on the power demand forecast, the power supply forecast, and the power unit price by multi-objective optimization using the probability of saving electricity.

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