System and method for electricity spot prices-based intelligent real-time regulation of electrical appliance

Abstract

Disclosed in the present invention is a system for electricity spot prices-based intelligent real-time regulation of an electrical appliance, comprising a cloud computing system deployed in the cloud, and an edge computing system and an electrical appliance which are disposed in each indoor environment. The cloud computing system comprises a first communication module, a database module, and a first logic control module; and each edge computing system comprises a second communication module, a sensor module, a data storage module, a second logic control module, and an electrical appliance control module. Also disclosed in the present invention is a method for electricity spot prices-based intelligent real-time regulation of an electrical appliance. The introduction of the edge computing systems increases the stability of the system; by means of integration of the system, an ordinary electrical appliance can sense changes in electricity spot prices in real time, so that the electrical appliance can instantly acquire and respond to electricity price fluctuations and operate more economically and efficiently; and complex optimization calculation is conveniently performed on the basis of electricity prices and user requirements, helping to provide a personalized and efficient electricity usage scheme.

Description

A system and method for intelligent real-time regulation of electrical appliances based on electricity spot prices Technical Field

[0001] The present invention relates to the field of electrical appliance control, and in particular to a system and method for intelligently regulating electrical appliances in real time based on electricity spot prices. Background Art

[0002] In many European countries, electricity markets use spot prices, which fluctuate significantly daily. Figure 1 shows the fluctuations in spot prices in the Finnish market over a 24-hour period on August 18, 2023. Within a single day, electricity prices can fluctuate several times or even dozens of times, from the highest to the lowest. Furthermore, spot prices for a specific period of time are often known in advance. This volatility is a hallmark of the electricity market.

[0003] High-power appliances like electric heaters, water heaters, and dehumidifiers consume a lot of electricity when electricity prices are high, but save a lot when prices are low. Therefore, leveraging the lower end of the electricity spot price range while avoiding the higher end is a crucial issue. Traditional appliance control systems often lack real-time price sensitivity and flexibility, preventing residents and businesses from effectively adjusting appliance usage based on price fluctuations.

[0004] In the context of electricity price fluctuations in the power market, the following problems and challenges exist:

[0005] 1. High energy costs: Residents and businesses often do not have sufficient intelligent control systems to fully utilize periods of low electricity prices, resulting in high energy costs during periods of high electricity prices.

[0006] 2. Lack of real-time response: Traditional electrical equipment control systems lack real-time response capabilities and are unable to intelligently control the operation of electrical equipment based on instantaneous changes in electricity prices.

[0007] 3. The balance between comfort and economy: While seeking to reduce energy costs, residents and businesses need to ensure that indoor comfort is not affected. This is a balance issue.

[0008] Summary of the Invention

[0009] In order to solve the problems existing in the prior art, the present invention provides a system for intelligently adjusting electrical appliances in real time based on electricity spot prices, which can respond in real time and save electricity costs.

[0010] Another object of the present invention is to provide a method for intelligently regulating electrical appliances in real time based on the spot price of electricity.

[0011] To this end, the present invention adopts the following technical solutions:

[0012] A system for intelligently regulating electrical appliances in real time based on spot electricity prices, comprising a cloud computing system deployed in the cloud and an edge computing system and electrical appliances located in each indoor environment. The cloud computing system comprises a first communication module, a database module, and a first logic control module; each edge computing system comprises a second communication module, a sensor module, a data storage module, a second logic control module, and an electrical appliance control module.

[0013] The sensor module is used to collect local environment information;

[0014] The second communication module is used to communicate with the first communication module, send local status information, obtain electricity price requests and appliance setting requests to it, and receive appliance setting instructions and electricity price information fed back by the cloud computing system;

[0015] The data storage module is used to store the local status information and the electricity price information fed back by the cloud computing system;

[0016] The second logic control module is used to control the operation of the second communication module, the sensor module, the data storage module and the electrical control module;

[0017] The electrical appliance control module is configured to control the electrical appliance after receiving an electrical appliance setting instruction and / or electricity price information fed back from the cloud computing system;

[0018] The first communication module is used to obtain electricity price information for a period of time in the future from a third party; is used to receive the appliance setting request and local status information sent by the second communication module, and send instructions and / or response data to the second communication module;

[0019] The database module is used to store the local status information received by the first communication module and the electricity price information obtained for a period of time in the future;

[0020] The first logic control module is used to control the first communication module and the database module.

[0021] In the above system, the local status information includes:

[0022] The unique identifier is used to distinguish which edge computing system the information comes from.

[0023] Timestamp information, used to distinguish the sending time of the information;

[0024] Appliance status information, used to indicate the on / off status of the appliance when the information is sent;

[0025] The sensor reading information is used to indicate the local environmental information collected by the sensor module when sending the information, and the local environmental information includes but is not limited to temperature and humidity.

[0026] In the above-mentioned system, the request to set the appliance includes: an identification bit; regional information; an operating mode, including but not limited to heating, cooling, dehumidification and / or humidification, and the first logic control module of the cloud computing system knows through the operating mode whether the type of device connected to the appliance control module is a heating device, a cooling device, a dehumidification device and / or a humidification device; a minimum acceptance threshold; a maximum acceptance threshold; and sensor reading information.

[0027] In the above system, the electrical equipment includes an electric heater, an electric refrigerator, an air conditioner, an electric water heater, an electric dehumidifier and / or a ventilation system.

[0028] A method for real-time adjustment of electrical appliances using the above system comprises the following steps:

[0029] Step 700: The edge computing system sends local status information to the cloud computing system; the edge computing system sends an appliance setting request to the cloud computing system;

[0030] Step 701: After the first communication module receives the appliance setting instruction, the first logic control module 3 first determines whether the sensor reading information collected by the edge computing system in the indoor environment is still within the threshold range set by the user. If so, step 703 is executed; otherwise, step 702 is executed.

[0031] Step 702: The first logic control module sends an instruction to turn on or off the appliance to the edge computing system via the first communication module, so that the sensor reading quickly returns to the set threshold range.

[0032] Step 703: the first logic control module queries the database module for all relevant local status information according to the identification bit, and calculates the rate of change v1 or v2 of the sensor reading when the electrical device is turned on or off according to the local status information obtained;

[0033] Step 704: The first logic control module queries the database module for electricity price information for the corresponding region in the future period based on the region information, and evaluates whether the current electricity price is high, low, or somewhere in between compared to the electricity price in the future period.

[0034] In step 705, the first logic control module continues to determine whether the current electricity price is low and whether the sensor reading will quickly reach the minimum or maximum acceptance threshold after the appliance is turned on; if so, execute step 706; otherwise, execute step 707;

[0035] Step 706: The first logic control module responds to the appliance setting request sent by the first communication module to the edge computing system, causing it to turn on the appliance to take advantage of the low electricity price period.

[0036] In step 707, the first logic control module continues to determine whether the current electricity price is high and whether the sensor reading will quickly reach the minimum or maximum acceptance threshold after the appliance is turned off; if so, execute step 708; otherwise, execute step 709;

[0037] Step 708: The first logic control module sends an electrical appliance shutdown instruction to the edge computing system through the first communication module, causing the edge computing system to shut down electrical equipment to avoid periods of high electricity prices.

[0038] In step 709, the first logic control module first calculates the time t2 when the sensor reading reaches the minimum or maximum acceptance threshold after the appliance is turned off. The calculation method is the same as that of step 707. Then, it is checked whether there is a cheaper electricity price within the time period t2. If so, step 708 is performed; otherwise, step 706 is performed.

[0039] Preferably, in step 700, the local status information is collected every 5-60 minutes; the edge computing system 200 sends an appliance setting request 600 to the cloud computing system 300 every 30-60 minutes;

[0040] In step 703, the method for calculating the rate of change v1 or v2 of the sensor reading when the electrical device is turned on or off is as follows:

[0041] sorting the information read from the database module by time stamp and checking the appliance status information in every two consecutive messages;

[0042] If the appliance status in both messages is shown as on, then the difference in the sensor readings in the two messages is the sensor change value when the appliance is turned on. Dividing it by the timestamp difference gives the sensor reading change rate v1 when the appliance is turned on.

[0043] Conversely, if the appliance status of two consecutive messages is both off, then the difference in sensor readings in the two messages is the sensor change value when the appliance is off, and then divided by the timestamp difference to obtain the sensor reading change rate v2 when the appliance is off.

[0044] Preferably, in step 704, the cloud computing system uses a standard normal distribution to divide the electricity price: Assume that the current electricity price is P0, the electricity price of the next time period is P1, and so on until the electricity price of the last time period n that can be predicted is P n, then the average value of the electricity price μ is: μ=(P0+P1+...+P n ) / n;

[0045] Then calculate the variance σ 2 : σ 2 =[(P0-μ) 2 +(P1-μ) 2 +...+(P n -μ) 2 ] / n;

[0046] Then the standard deviation is σ;

[0047] If P0 is less than (μ-σ), the current electricity price is considered to be low; if P0 is greater than (μ+σ), the current electricity price is considered to be high.

[0048] In step 705, the time t1 to reach the threshold is calculated using the difference △1 between the current sensor reading and the minimum or maximum acceptance threshold, where t1 = △1 / v1. The difference △1 is calculated based on different operating modes. When t1 is greater than a time threshold, it is considered that the sensor reading will not reach the minimum or maximum acceptance threshold soon after the appliance is turned on. Preferably, the threshold is 0.5 hours.

[0049] In step 707, the time t2 to reach the threshold is calculated using the difference Δ2 between the current sensor reading and the minimum or maximum acceptance threshold, where t2 = Δ2 / v2. The difference Δ2 is calculated according to different modes. When t2 is greater than a time threshold, it is considered that the sensor reading will not reach the minimum or maximum acceptance threshold soon after the appliance is turned off. Preferably, the threshold is 0.5 hours.

[0050] In the above step 701, when the first communication module fails to receive the appliance setting instruction, the edge computing system starts edge computing, including the following steps:

[0051] Step 801: The second logic control module determines whether the sensor reading information collected by the edge computing system in the indoor environment is still within the minimum or maximum acceptance threshold. If not, execute step 802; if yes, execute step 803;

[0052] Step 802: The second logic control module turns on or off the appliance according to the current working mode of the appliance control module. The method of turning on or off is the same as that of step 702.

[0053] Step 803: The second logic control module calculates the rate of change of the sensor reading when the appliance is turned on or off based on the information stored in the data storage module. The calculation method is the same as that of step 703.

[0054] Step 804: The second logic control module evaluates whether the current electricity price is high, low, or somewhere in between compared to the electricity price in the future. The electricity price information is obtained by sending a request for electricity price information to the cloud computing system. The algorithm is the same as that of step 704.

[0055] In step 805, the second logic control module continues to determine whether the current electricity price is low and whether the sensor reading reaches the minimum or maximum acceptance threshold soon after the appliance is turned on. If yes, step 806 is executed; otherwise, step 807 is executed. The determination method is the same as step 705.

[0056] Step 806 , the second logic control module controls the appliance control module to turn on the appliance to take advantage of the low electricity price period;

[0057] In step 807, the second logic control module continues to determine whether the current electricity price is high and whether the sensor reading will quickly reach the minimum or maximum acceptance threshold after the appliance is turned off. If yes, execute step 808; otherwise, execute step 809. The determination method is the same as step 707.

[0058] Step 808: The second logic control module controls the electrical appliance control module to shut down electrical appliances to avoid periods of high electricity prices.

[0059] In step 809, the second logic control module first calculates the time t2 when the sensor reading reaches the minimum or maximum acceptance threshold after the appliance is turned off, using the same calculation method as step 807; then checks whether there is a cheaper electricity price within the t2 time period. If so, execute step 808; otherwise, execute step 806.

[0060] The present invention is applicable to optimizing electricity prices for heaters, refrigerators, dehumidifiers, humidifiers, etc.

[0061] Compared with the prior art, the present invention has the following beneficial effects:

[0062] 1. Leveraging the powerful computing capabilities of cloud computing systems, the present invention is able to process large amounts of local status information. This enables the system to analyze more historical data and provide more accurate and intelligent appliance operation optimization solutions.

[0063] 2. Through the intelligent algorithms integrated into the cloud computing system, complex optimization calculations can be performed based on multiple factors such as electricity prices and user needs, thereby helping to provide personalized and efficient electrical equipment operation plans.

[0064] 3. In this invention, the introduction of the edge computing system increases the stability of the system. When the cloud computing system encounters problems or fails to respond, the edge computing system can provide basic optimization services, enabling the system to continue to provide services under various circumstances.

[0065] 4. The method of the present invention enables common electrical equipment to perceive changes in electricity spot prices in real time through system integration, so that electrical equipment can immediately obtain and respond to electricity price fluctuations and operate in a more economical and efficient manner.

[0066] 5. The method of the present invention allows users to customize the operation of electrical devices according to their needs and preferences. This user customization increases the flexibility of the system, thereby being able to adapt to the needs of different users and usage scenarios.

[0067] 6. The method of the present invention adjusts the operation of electrical equipment by sensing changes in electricity prices in real time. This method maximizes the energy-saving potential of electrical equipment and reduces electricity costs.

[0068] 7. The method of the present invention is applicable to a variety of electrical equipment, including but not limited to heaters, refrigerators, dehumidifiers, humidifiers, etc., providing the possibility of optimizing electricity prices for different types of equipment.

[0069] The core innovation of the present invention is that it can respond to electricity price fluctuations in real time, allowing electrical equipment to operate during periods of low electricity prices and avoid operation during periods of high electricity prices, thereby maximizing energy conservation and economic benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] Figure 1 shows the changes in the spot price of electricity in Finland over a 24-hour period on August 18, 2023;

[0071] FIG2 is a schematic diagram of the overall composition of the energy-saving system of the present invention;

[0072] FIG3 is a block diagram of the edge computing system in the energy-saving system of the present invention;

[0073] FIG4 is a schematic diagram of the composition of a cloud computing system in the energy-saving system of the present invention;

[0074] FIG5 is a block diagram showing the composition of local status information in the present invention;

[0075] FIG6 is a block diagram of a method for obtaining an electricity price request according to the present invention;

[0076] FIG7 is a block diagram of a configuration of an electrical appliance request according to the present invention;

[0077] FIG8 is a flow chart of the cloud computing method of the present invention;

[0078] FIG9 is a flow chart of the edge computing method in the present invention.

[0079] Where: 101. Cloud 102. Indoor environment 200. Edge computing system 201. Second communication module 202. Sensor module 203. Data storage module 204. Second logic control module 205. Appliance control module 300. Cloud computing system 301. First communication module 302. Database module 303. First logic control module 400. Local status information 401. Identifier 402. Timestamp 403. Appliance status information 404. Sensor reading information 500. Get electricity price request 501. Regional information 600. Set appliance request 601. Working mode 602. Minimum acceptance threshold 603. Maximum acceptance threshold DETAILED DESCRIPTION

[0080] The technical solution of the present invention is described in detail below with reference to the accompanying drawings and embodiments.

[0081] The method of the present invention for intelligently regulating electrical appliances in real time based on electricity spot prices combines cloud computing and edge computing.

[0082] The system of the present invention for intelligently adjusting electrical appliances in real time based on spot electricity prices is composed as shown in Figure 2, including a cloud computing system 300 deployed in the cloud 101 and an edge computing system 200 and electrical equipment set in each indoor environment 102.

[0083] 3 , each of the edge computing systems 200 includes: a second communication module 201 , a sensor module 202 , a data storage module 203 , a second logic control module 204 and an electrical control module 205 .

[0084] Specifically, the second communication module 201 is used to communicate with the cloud computing system 300. The communication method includes, but is not limited to, a computer network. The sensor module 202 is used to collect local environmental information, including, but not limited to, temperature and humidity. The data storage module 203 is used to store the local environmental information collected by the sensor module 202 and electricity price information for a future period obtained from the cloud computing system 300.

[0085] The second logic control module 204 is used to control the operation of the communication module 201, the sensor module 202, the data storage module 203, and the appliance control module 205. Specifically, the second logic control module 204 controls the sensor module 202 to collect local environmental information; controls the second communication module 201 to send request information to the cloud computing system 300 and receive instructions and response data from the cloud computing system 300; upon receiving the response data from the cloud computing system 300, stores the response data in the data storage module 203; and upon receiving the instructions and response data, controls the appliance control module 205 to control the appliance.

[0086] The electrical appliance control module 205 is used to control the electrical appliance on and off. The control methods include but are not limited to direct connection between the electrical appliance and the electrical appliance control module and / or infrared remote control.

[0087] The electrical equipment includes but is not limited to electric heaters, electric refrigerators, air conditioners, electric water heaters, electric dehumidifiers, ventilation systems, etc.

[0088] 3 , a cloud computing system 300 deployed on a cloud 101 includes a first communication module 301, a database module 302, and a first logic control module 303. The cloud 101 may be Amazon Cloud or other suitable cloud.

[0089] Specifically, the first communication module 301 is configured to communicate with the edge computing system 200 via the second communication module 201. Such communication includes receiving a request for obtaining electricity prices for a future period of time and / or the local status information from the edge computing system 200, and sending instructions to the edge computing system 200. The cloud computing system 300 also obtains future electricity price information from a third party, including but not limited to the Nordpool electricity market.

[0090] The database module 302 is responsible for storing data information, including electricity price information for the future period and local environment information sent by the edge computing system 200.

[0091] The first logic control module 303 is responsible for controlling other modules in the cloud computing system 300. The functions of the logic control module 303 include: controlling the first communication module 301 to obtain electricity price information for a period of time in the future from a third party; after obtaining the electricity price information for the period of time in the future, storing it in the database module 302; when the edge computing module 200 sends local environment information to the cloud computing system 300 through the second communication module 201, storing it in the database module 302; when the edge computing module 200 sends a request to the cloud computing system 300 to obtain electricity price information for a period of time in the future, retrieving response data, i.e., the electricity price information, from the database module 302 and sending it to the edge computing module 200; and when the edge computing module 200 sends an instruction, sending a response instruction to the edge computing module 200 through calculation.

[0092] The information sent by the edge computing system 200 to the cloud computing system 300 includes local status information 400, a request to obtain electricity prices 500, and a request to set an appliance 600.

[0093] As shown in FIG5 , the local status information 400 includes:

[0094] a. Identification bit 401, which is unique and used to distinguish which edge computing system the information comes from. A MAC address can be used as the identification bit. Identification bit 401 can be obtained by querying the second communication module 201.

[0095] b. Timestamp 402 is used to distinguish the specific time when the information is sent, and can be obtained by querying a third-party server.

[0096] c. Appliance status information 403, used to indicate the on / off status of the appliance when the information is sent.

[0097] d. Sensor reading information 404, used to indicate local environmental information collected by the sensor module 202 when sending the information, including but not limited to temperature and humidity.

[0098] The electricity price may vary based on the region. The cloud computing system 300 may send the electricity price of the corresponding region in the future period to the edge computing system 200 based on the region information 501 .

[0099] As shown in FIG6 , the electricity price acquisition request 500 includes region information 501 and an identification bit 401 .

[0100] As shown in FIG7 , the appliance setting request 600 includes:

[0101] (1) Identification bit 401;

[0102] (2) Regional information 501, such as "Finland";

[0103] (3) Working mode 601. The working mode includes but is not limited to heating, cooling, dehumidification, humidification, etc. Through the working mode, the cloud computing system 300 can know whether the type of device connected to the edge computing system 200 is a heating device, a cooling device, a dehumidification device, or a humidification device.

[0104] (4) Minimum acceptance threshold 602. In some usage scenarios, users may require setting a minimum acceptance threshold. For example, in an electric heating scenario, users may require that the minimum temperature not exceed 18 degrees Celsius.

[0105] (5) Maximum acceptance threshold 603. In some usage scenarios, users may require setting a maximum acceptance threshold. For example, in an electric heating scenario, users may require that the maximum temperature not exceed 22 degrees Celsius.

[0106] (6) Sensor reading information 404.

[0107] The identification bit 401 in the local status information 400 , the electricity price acquisition request 500 , and the appliance setting request 600 is the same.

[0108] In the above system:

[0109] The second communication module 201 may be communication hardware connected to a local area network (LAN), WLAN, Zigbee, Bluetooth, infrared (IR), GSM / GPRS, CDMA, WCDMA or LTE;

[0110] The sensor module 202 may be a temperature sensor, a humidity sensor, etc.;

[0111] The data storage module 203 and the database module 302 may be a read-only memory (ROM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a flash memory, a data disk, an optical memory, or a magnetic memory;

[0112] The second logic control module 204 may be a microprocessor or the like;

[0113] The electrical appliance control module 205 may be a remote control socket and / or an infrared remote control;

[0114] The first communication module 301 may be communication hardware connected to a local area network (LAN) or a WLAN;

[0115] The first logic control module 303 may be a central processing unit (CPU) or the like.

[0116] The second logic control module 204 controls the second communication module 201 to send local status information to the cloud computing system once every period of time, for example, 5-60 minutes. Local environmental information can also be sent to the cloud computing system 300 when a status change occurs (for example, a change in the on / off status of an appliance and / or a change in sensor reading information). When the edge computing system 200 sends the local environmental information to the cloud computing system 300, it can also store the timestamp 402 and appliance status information 403 in the data storage module 203. Due to the limited storage capacity of the edge computing system 200, new information can overwrite old information in a first-in-first-out (FIFO) manner.

[0117] After receiving the local status information sent by the edge computing system 200 , the cloud computing system 300 stores the corresponding identification bit 401 , timestamp 402 , appliance status information 403 , and sensor reading information 404 into the database module 302 .

[0118] The second logic control module 204 may also control the second communication module 201 to send an appliance configuration request to the cloud computing system 300 at intervals (30-60 minutes). Alternatively, the second communication module 201 may send an appliance configuration request to the cloud computing system 300 when a state changes (e.g., a change in the appliance's on / off state or a change in sensor readings).

[0119] After receiving the appliance setting request 600 sent by the edge computing system 200, the cloud computing system 300 performs a calculation as shown in FIG8 , including the following steps:

[0120] In step 701, the first logic control module 303 first determines whether the sensor reading information 404 collected by the edge computing system in the indoor environment is still within the threshold range set by the user. For example, in the electric heating scenario, the user requires that the temperature must not be lower than MIN = 18 degrees Celsius and must not be higher than MAX = 22 degrees Celsius. If the current temperature sensor reading is 19 degrees Celsius, it is still within the threshold range. In this case, the process proceeds to step 703; otherwise (the sensor reading is lower than MIN or higher than MAX), the process proceeds to step 702.

[0121] In step 702, the first logic control module 303 sends an appliance power-on or power-off command to the edge computing system 200 via the first communication module 301, thereby quickly restoring the sensor reading to within the threshold range. The specific instructions for powering on or off the appliance are shown in Table 1. Upon receiving the appliance power-on or power-off command, the second logic control module 204 of the edge computing system 200 controls the appliance control module 205 to power on or off the appliance.

[0122] In step 703, the logic control module 303 queries the database module 302 for all relevant information entries based on the identification bit 401, including the timestamp 402, the appliance status information 403, and the sensor reading information 404. Based on the queried information, the rate of change of the sensor reading when the appliance is turned on or off is calculated. The details are as follows:

[0123] First, the information entries read from the database are sorted by timestamp 402. The appliance status information 403 is checked in every two consecutive information entries. If the appliance status of both entries indicates that the appliance is on, then the difference in the sensor reading information 404 between the two entries is the sensor change value when the appliance is on. Dividing this by the difference in the two timestamps 402 gives the sensor reading change rate (v1) when the appliance is on. Conversely, if the appliance status of two consecutive entries indicates that the appliance is off, then the difference in the sensor reading information 404 between the two entries is the sensor change value when the appliance is off. Dividing this by the difference in the two timestamps 402 gives the sensor reading change rate (v2) when the appliance is off. Cloud computing systems can take advantage of their large storage capacity to calculate more accurate change rates.

[0124] In step 704, the first logic control module 303 queries the database module 302 for electricity price information for the corresponding region in the future based on the regional information 501. For example, in Nordpool, electricity price information changes every hour. The first logic control module 303 then evaluates whether the current electricity price is high, low, or somewhere in between compared to the electricity price in the future.

[0125] The cloud computing system 300 can use the standard normal distribution to divide the electricity price. Assume that the current electricity price is P0, the electricity price of the next time period is P1, and the electricity price of the last time period n that can be predicted is P n First calculate the average value μ: μ=(P0+P1+...+P n ) / n.

[0126] Next, calculate the variance σ 2 : σ 2 =[(P0-μ) 2 +(P1-μ) 2 +...+(P n -μ) 2 ] / n.

[0127] The standard deviation is σ. If P0 is less than (μ-σ), the current electricity price can be considered low. If P0 is greater than (μ+σ), the current electricity price can be considered high.

[0128] In step 705, the first logic control module 303 continues to determine whether the current electricity price is low and whether the sensor reading quickly reaches the minimum or maximum acceptance threshold after the appliance is turned on (i.e., whether the current sensor reading is significantly different from the target threshold). If so, the process proceeds to step 706; otherwise, the process proceeds to step 707.

[0129] Here's how to determine whether the sensor reading reaches the threshold quickly after turning on the appliance:

[0130] In step 703, we have obtained the rate of change of the sensor reading after the appliance is turned on (v1). The time to reach the threshold (t1) can be calculated using the difference (Δ1) between the current sensor reading and MIN or MAX: t1 = Δ1 / v1.

[0131] △1 is calculated according to different working modes 601, as shown in Table 2:

[0132] Table 2

[0133] When t1 is greater than a time threshold (for example, half an hour), it is considered that the sensor reading will not reach the MIN or MAX threshold soon after the appliance is turned on.

[0134] In step 706, the logic control module 303 responds to the appliance setup request 600 sent by the edge computing system 200 through the first communication module 301, and sends a command to turn on the electrical device to take advantage of the low electricity price period. The second logic control module 204 controls the electrical control module 205 to turn on the electrical device.

[0135] In step 707, the logic control module 303 continues to determine whether the current electricity price is high and whether the sensor reading reaches the minimum or maximum acceptance threshold soon after the appliance is turned off (i.e., whether the current sensor reading differs significantly from the minimum or maximum acceptance threshold (MIN or MAX)). If so, the process proceeds to step 708; otherwise, the process proceeds to step 709.

[0136] Here's how to determine if the sensor reading reaches the minimum or maximum acceptance threshold soon after turning off the appliance:

[0137] In step 703, we have obtained the rate of change of the sensor reading after the appliance is turned off (v2). The time to reach the threshold (t2) can be calculated using the difference (Δ2) between the current sensor reading and MIN or MAX: t2 = Δ2 / v2.

[0138] △2 is calculated according to different modes, as shown in Table 3:

[0139] Table 3

[0140] When t2 is greater than a time threshold (for example, half an hour), it is considered that the sensor reading will not reach the MIN or MAX threshold soon after the appliance is turned off.

[0141] In step 708, the logic control module 303 returns a shutdown instruction to the edge computing system via the communication module 301 to avoid high electricity price periods. After receiving the shutdown instruction, the second logic control module 204 of the edge computing system 200 controls the appliance control module 205 to shut down the appliance.

[0142] In step 709, the first logic control module 303 first calculates the time (t2) after the sensor reading reaches the minimum or maximum acceptance threshold after the appliance is turned off, using the same calculation method as step 707. Next, the module checks whether there is a cheaper electricity rate during the t2 period. If so, the module proceeds to step 708; otherwise, the module proceeds to step 706.

[0143] In some cases, the cloud computing system cannot respond to the edge computing system's request to set up appliances in a timely manner. These situations may include network problems or excessive edge computing system requests. In such cases, the edge computing system can perform edge computing. The specific calculation method is shown in Figure 9 and includes:

[0144] In step 801, the second logic control module 204 determines whether the sensor reading information 404 collected by the edge computing system in the indoor environment is still within the minimum or maximum acceptance threshold (the user-set threshold MIN / MAX) range, similar to step 701. If not, step 802 is executed; if so, step 803 is executed.

[0145] In step 802 , the second logic control module 204 turns on or off the appliance according to the current working mode of the appliance control module 205 . The method of turning on or off is the same as that in step 702 .

[0146] In step 803, the logic control module 204 calculates the rate of change of the sensor reading when the appliance is turned on or off based on the information stored in the data storage module 203. The calculation method is similar to that in step 703. Due to the limited storage space of the edge computing system, the calculated rate accuracy will be lower than that of the cloud computing system.

[0147] In step 804, the logic control module 204 evaluates whether the current electricity price is high, low, or somewhere in between compared to the electricity price for a period of time in the future. The electricity price information is obtained by requesting electricity price information from the cloud computing system. The algorithm is the same as that for step 704.

[0148] In step 805, the logic control module 204 continues to determine whether the current electricity price is low and whether the sensor reading quickly reaches the minimum or maximum acceptance threshold after the appliance is turned on. If so, the process proceeds to step 806; otherwise, the process proceeds to step 807. The determination method is the same as in step 705.

[0149] In step 806 , the logic control module 204 controls the appliance control module 205 to turn on the appliances to take advantage of the low electricity price period.

[0150] In step 807, the logic control module 204 continues to determine whether the current electricity price is high and whether the sensor reading quickly reaches the minimum or maximum acceptance threshold after the appliance is turned off. If so, the process proceeds to step 808; otherwise, the process proceeds to step 809. The determination method is the same as in step 707.

[0151] In step 808 , the logic control module 204 controls the appliance control module 205 to turn off appliances to avoid periods of high electricity prices.

[0152] In step 809, the logic control module 204 first calculates the time (t2) after the sensor reading reaches the minimum or maximum acceptance threshold after the appliance is turned off, using the same calculation method as step 807. It then checks whether there is a cheaper electricity rate during time period t2. If so, the process proceeds to step 808; otherwise, the process proceeds to step 806.

Claims

1. A system for intelligently and real - time adjusting electrical appliances based on electricity spot prices, characterized in that, it includes a cloud computing system deployed in the cloud, and an edge computing system and electrical appliances installed in each indoor environment. The cloud computing system includes a first communication module, a database module, and a first logic control module; each of the edge computing systems includes a second communication module, a sensor module, a data storage module, a second logic control module, and an electrical appliance control module; wherein: The sensor module is used to collect local environmental information; The second communication module is used to communicate with the first communication module, send local status information, electricity price request, and electrical appliance setting request to it, and receive the electrical appliance setting instruction and electricity price information fed back by the cloud computing system; The data storage module is used to store the local status information and the electricity price information fed back by the cloud computing system; The second logic control module is used to control the operation of the second communication module, the sensor module, the data storage module, and the electrical appliance control module; The electrical appliance control module is used to control the electrical appliances after receiving the electrical appliance setting instruction and / or the electricity price information fed back by the cloud computing system; The first communication module is used to obtain electricity price information for a future period of time from a third party; used to receive the electrical appliance setting request and local status information sent by the second communication module, and send instructions and / or response data to the second communication module; The database module is used to store the local status information received by the first communication module and the electricity price information for a future period of time obtained; The first logic control module is used to control the first communication module and the database module.

2. The system according to claim 1, characterized in that, The local status information includes: An identification bit, which is unique and used to distinguish which edge computing system the information comes from; Timestamp information, used to distinguish the sending time of the information; Electrical appliance status information, used to indicate the on - off status of the electrical appliance when the information is sent; Sensor reading information, used to indicate the local environmental information collected by the sensor module when the information is sent, and the local environmental information includes but is not limited to temperature and humidity.

3. The system according to claim 2, characterized in that, The electrical appliance setting request 600 includes: An identification bit; Region information; A working mode, including but not limited to heating, cooling, dehumidifying, and / or humidifying. The first logic control module of the cloud computing system knows the type of equipment connected to the electrical appliance control module, such as a heating device, a cooling device, a dehumidifying device, and / or a humidifying device, through the working mode; The minimum acceptance threshold; The maximum acceptance threshold; Sensor reading information.

4. The system according to claim 3, characterized in that: The electrical appliances include electric heaters, electric refrigerators, air conditioners, electric water heaters, electric dehumidifiers, and / or ventilation systems.

5. A method for real - time adjusting electrical appliances using the system according to claim 4, characterized in that it includes the following steps: Step 700, the edge computing system sends local status information to the cloud computing system; the edge computing system sends an electrical appliance setting request to the cloud computing system; Step 701, after the first communication module receives the electrical appliance setting instruction, the first logic control module 3 first determines whether the sensor reading information collected by the edge computing system in the indoor environment is still within the threshold range set by the user. If so, execute Step 703; Otherwise, execute Step 702; Step 702, the first logic control module sends an instruction to turn on or off the electrical appliance to the edge computing system through the first communication module, so that the sensor reading quickly returns to the set threshold range; Step 703, the first logic control module queries all relevant local status information in the database module according to the identification bit, and calculates the change rate v of the sensor reading when the electrical device is turned on or off according to the queried local status information 1 or v 2 ; Step 704, the first logic control module queries the electricity price information of the corresponding region in the database module within a certain period of time in the future according to the region information, and evaluates whether the current electricity price is a high electricity price, a low electricity price, or between the two compared with the electricity price in the future period; Step 705, the first logic control module continues to judge whether the current electricity price is a low electricity price, and whether the sensor reading will quickly reach the minimum or maximum acceptance threshold after the electrical appliance is turned on. If so, execute Step 706; Otherwise, execute Step 707; Step 706, the first logic control module responds to the electrical appliance setting request sent to the edge computing system through the first communication module, and enables it to turn on the electrical appliance to utilize the low electricity price period; Step 707, the first logic control module continues to judge whether the current electricity price is a high electricity price, and whether the sensor reading will quickly reach the minimum or maximum acceptance threshold after the electrical appliance is turned off. If so, execute Step 708; Otherwise, execute Step 709; Step 708, the first logic control module sends an instruction to turn off the electrical appliance to the edge computing system through the first communication module, so that it turns off the electrical appliance device to avoid the high electricity price period; Step 709, the first logic control module first calculates the time t when the sensor reading reaches the minimum or maximum acceptance threshold after the electrical appliance is turned off. The calculation method is the same as that in step 707. Then, it checks whether there is a cheaper electricity price within the time period t. If there is, step 708 is performed; otherwise, step 706 is performed. 2 , which is the same as the calculation method in step 707, and then checks whether there is a cheaper electricity price within the time period t 2 . If there is, step 708 is performed; otherwise, step 706 is performed.

6. The method according to claim 5, characterized in that: In Step 700, the local status information is collected every 5 - 60 minutes; the edge computing system 200 sends an electrical appliance setting request 600 to the cloud computing system 300 every 30 - 60 minutes; In step 703, calculate the change rate v of the sensor readings when the electrical device is turned on or off 1 or v 2 The method is as follows: Sort the information read from the database module according to the time stamp, and check the electrical appliance status information in every two consecutive pieces of information; If the electrical states in both pieces of information show an "on" state, then the difference in sensor readings between these two pieces of information is the sensor change value when the electrical appliance is on. Divide this by the difference in timestamps to obtain the sensor reading change rate v of the electrical appliance when it is on 1 ; Conversely, if the electrical states of two consecutive pieces of information both show the off state, then the difference in sensor readings between these two pieces of information is the sensor change value when the electrical appliance is off. Dividing this by the difference in timestamps gives the rate of change v of the sensor readings when the electrical appliance is off 2 .

7. The method according to claim 5, characterized in that, In step 704, the cloud computing system uses the standard normal distribution to divide the electricity price: Assume the current electricity price is P 0 , the electricity price for the next time period is P 1 , and the electricity price for the last time period n that can be predicted is P n . Then the average value μ of the electricity price is: μ = (P 0 + P 1 +... + P n ) / n; Then calculate the variance σ 2 : σ 2 = [(P 0 - μ) 2 + (P 1 - μ) 2 +... + (P n - μ) 2 / n; the standard deviation is σ; If P 0 is less than (μ - σ), the current electricity price is considered a low electricity price; if P 0 is greater than (μ + σ), the current electricity price is considered a high electricity price.

8. The method according to claim 5, characterized in that: In step 705, the difference Δ between the current sensor reading and the minimum or maximum acceptance threshold is used 1 to calculate the time t to reach the threshold 1 , t 1 = Δ 1 / v 1 , where the difference Δ 1 is calculated according to different working modes; when t 1 is greater than a time threshold, it is considered that the sensor reading will not reach the minimum or maximum acceptance threshold quickly after the electrical appliance is turned on; Preferably, the threshold is 0.5 hours.

9. The method according to claim 5, characterized in that: In step 707, the difference △ between the current sensor reading and the minimum or maximum acceptable threshold is used 2 to calculate the time t to reach the threshold 2 , t 2 = △ 2 / v 2 ; the difference △ 2 is calculated according to different modes; when t 2 is greater than a time threshold, it is considered that the sensor reading will not reach the minimum or maximum acceptable threshold quickly after the electrical appliance is turned off; Preferably, the threshold is 0.5 hours.

10. The method according to claim 5, characterized in that: In Step 701, when the first communication module fails to receive the electrical appliance setting instruction, the edge computing system starts edge computing, including the following steps: Step 801, the second logic control module judges whether the sensor reading information collected by the edge computing system in the indoor environment is still within the minimum or maximum acceptance threshold. If not, execute Step 802; if so, execute Step 803; Step 802, the second logic control module opens or closes the electrical appliance according to the current working mode of the electrical appliance control module, and the opening or closing method is the same as that in step 702; Step 803, the second logic control module calculates the change rate of the sensor reading when the electrical appliance is opened or closed according to the information stored in the data storage module, and the calculation method is the same as that in step 703; Step 804, the second logic control module evaluates whether the current electricity price is a high electricity price, a low electricity price, or between the two compared with the electricity price in a future period of time. The electricity price information is obtained by sending a request for electricity price information to the cloud computing system, and the algorithm is the same as that in step 704; Step 805, the second logic control module continues to judge whether the current electricity price is a low electricity price and whether the sensor reading will quickly reach the minimum or maximum acceptance threshold after the electrical appliance is turned on. If so, execute step 806; otherwise, execute step 807; the judgment method is the same as that in step 705; Step 806, the second logic control module controls the electrical appliance control module to turn on the electrical appliance to utilize the low electricity price period; Step 807, the second logic control module continues to judge whether the current electricity price is a high electricity price and whether the sensor reading will quickly reach the minimum or maximum acceptance threshold after the electrical appliance is turned off. If so, execute step 808; otherwise, execute step 809; the judgment method is the same as that in step 707; Step 808, the second logic control module controls the electrical appliance control module to turn off the electrical appliance device to avoid the high electricity price period; Step 809: The second logic control module first calculates the time t when the sensor reading reaches the minimum or maximum acceptance threshold after the electrical appliance is turned off, and the calculation method is the same as that in step 807. Then, it checks whether there is a cheaper electricity price within the time period t. If so, step 808 is executed; otherwise, step 806 is executed. 2 , and the calculation method is the same as that in step 807. Then, it checks whether there is a cheaper electricity price within the time period t 2 . If so, step 808 is executed; otherwise, step 806 is executed.

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