Home energy-based water heater control method, device, equipment and storage medium

Abstract

The present application relates to the technical field of smart home, and discloses a water heater control method, device and equipment based on home energy and a storage medium, comprising: obtaining historical power data of a target family in a first time period, weather data in the first time period and weather prediction data for the next day; calculating the hourly residual power of each hour in the first time period based on the historical power data in the first time period; calculating the hourly predicted residual power for the next day; calculating the predicted power maximum value, determining the predicted power maximum time period corresponding to the predicted power maximum value; calculating the maximum water heating power required by the water heater of the target family, and determining whether to execute an energy plan based on the maximum water heating power required and the predicted power maximum value; and when it is determined to execute the energy plan, controlling the water heater to heat water in the predicted power maximum time period, thereby avoiding additional power purchase, further saving energy and power purchase cost, and reducing the use cost of the smart home.

Description

Technical Field

[0001] This invention relates to the field of smart home technology, specifically to a method, device, equipment, and storage medium for controlling water heaters based on household energy. Background Technology

[0002] Home energy storage is a crucial component of distributed energy, driving the transformation of the power system from centralized power supply to a combination of centralized and distributed power supply. Currently, the continuous growth of centralized, fluctuating renewable energy capacity and increasing electricity demand are exacerbating problems such as power shortages, low power quality, and high electricity prices. Distributed energy supply can achieve cost savings in transmission and distribution, resulting in lower costs, improved power quality, and energy efficiency. For households, home energy storage can reduce electricity costs and serve as an emergency backup power source, enhancing the reliability of household power supply. For the power grid, configuring energy storage can transfer electricity over time to help balance generation capacity and electricity demand.

[0003] In related technologies, since water heaters consume a large proportion of electricity among home appliances, users often need to heat the water in advance to reach a suitable temperature before use. For families that have installed solar panels, smart meters, and water heaters and are using SmartHome, the lack of a suitable water heating schedule often leads to energy waste and unnecessary money and energy consumption. Summary of the Invention

[0004] In view of this, the present invention provides a water heater control method, device, equipment and storage medium based on household energy to solve the problem of lack of suitable water heating time scheduling.

[0005] In a first aspect, the present invention provides a water heater control method based on household energy, comprising:

[0006] Obtain historical electricity consumption data, weather data, and next day's weather forecast data for the target household in the first time period.

[0007] Based on the historical electricity data of the first time period in the past, the hourly remaining electricity for each hour of the first time period in the past is calculated;

[0008] Based on the weather data of the first time period in the past, the weather forecast data for the next day, and the hourly remaining power, the predicted hourly remaining power for the next day is calculated.

[0009] Based on the hourly forecast of remaining electricity for the next day, calculate the maximum forecasted electricity value and determine the time period corresponding to the maximum forecasted electricity value.

[0010] Calculate the maximum electricity required for the target household's water heater to heat water, and determine whether to implement the energy plan based on the maximum electricity required for heating water and the predicted maximum electricity.

[0011] When determining the implementation of an energy plan, control the water heater to heat water during the period of highest predicted electricity consumption.

[0012] In this invention, historical data is used to calculate the remaining electricity consumption per hour in the past, and the weather for the next hour is obtained. By comparing the weather for the next hour with the weather at the same time in the past, the remaining electricity consumption for the next hour is predicted, the maximum predicted electricity consumption and the corresponding time period are determined, and the water heater is controlled to heat water during this time period. This avoids the need to purchase additional electricity for heating water, further saving energy and electricity costs, and reducing the cost of using smart homes.

[0013] In one optional implementation, the target household's historical electricity consumption data over the past first time period includes: the target household's hourly water heater electricity consumption and hourly electricity sales over the past first time period;

[0014] Based on historical electricity data from the first time period, the remaining hourly electricity for each hour of the first time period is calculated, including:

[0015] Based on historical electricity data from the first time period in the past, determine whether the electricity sold in the current hour during the first time period in the past is greater than zero;

[0016] If the electricity sold in the current hour of the first past time period is not greater than zero, the remaining electricity for the current hour of the first past time period is determined to be zero.

[0017] In this method, by judging the electricity sold by the household, it is easier and more intuitive to determine whether the remaining electricity in each past hour was zero, thus making it easier for users to determine the current energy status of their household.

[0018] In one alternative implementation, it further includes:

[0019] When the electricity sold in the current hour of the first past time period is greater than zero, calculate the difference between the electricity sold in the current hour of the first past time period and the electricity sold in the previous hour of the first past time period, and the difference between the electricity consumed by the water heater in the current hour of the first past time period and the electricity consumed by the water heater in the previous hour of the first past time period. Add the difference in electricity sold and the difference in electricity consumed by the water heater to obtain the remaining electricity for the current hour of the first past time period.

[0020] In this method, when the household's electricity sales are not zero, the remaining electricity for each past hour can be calculated more simply and intuitively by adding the difference between the electricity sales of the current hour and the electricity consumed by the water heater.

[0021] In one optional implementation, the weather data of the past first time period and the weather forecast data for the next day include: the weather information code corresponding to the weather of each hour of the past first time period and the weather information code corresponding to the forecast weather of each hour of the next day.

[0022] Based on past weather data for the first time period, next day's weather forecast data, and hourly remaining power, the predicted hourly remaining power for the next day is calculated, including:

[0023] Based on the weather information code corresponding to the forecast weather for each hour of the next day, filter the historical moments in the first time period that have the same weather information code as the forecast weather for each hour of the next day, and obtain the hourly remaining battery power for the filtered historical moments.

[0024] The minimum hourly remaining power at each historical moment is determined as the predicted hourly remaining power for the next day.

[0025] In this method, weather information codes are assigned to the weather data, facilitating the processing and comparison of the weather dataset. By comparing the weather at the same time in the past, the remaining power capacity at the same time the following day is predicted, further improving the reliability and accuracy of the remaining power capacity prediction.

[0026] In one optional implementation, based on the predicted remaining power consumption for the next day's hours, the maximum predicted power consumption is calculated, and the time period corresponding to the maximum predicted power consumption is determined, including:

[0027] Based on the predicted remaining electricity for the next day, the sum of the predicted remaining electricity for the next day from the current time to the next time is calculated until the sum of the predicted electricity for each time period is obtained. The interval between the current time and the next time is the time required for the water heater to heat water.

[0028] The maximum value of the sum of predicted electricity consumption for each time period is selected and recorded as the maximum predicted electricity consumption. The time period corresponding to the maximum predicted electricity consumption is then determined.

[0029] In this method, the predicted electricity consumption for the next day is divided into multiple time periods, with the water heater heating time period as the step size. The sum of the predicted electricity consumption for each time period is calculated, and the maximum predicted electricity consumption and the maximum time period are selected. This determines the arrangement of the water heating time period, which facilitates subsequent control of the water heater heating.

[0030] In one optional implementation, determining whether to execute an energy plan based on the maximum electricity required for boiling water and the predicted maximum electricity required includes:

[0031] The safety valve is determined based on the maximum amount of electricity required to boil water;

[0032] Determine whether the predicted maximum power consumption is greater than the maximum power consumption required to boil water, and determine whether the predicted maximum power consumption is greater than the safety valve value;

[0033] If the predicted maximum power consumption is greater than the maximum power consumption required for boiling water and the predicted maximum power consumption is greater than the safety valve value, then the energy plan will be executed.

[0034] If the predicted maximum power consumption is not greater than the maximum power consumption required for boiling water or the predicted maximum power consumption is not greater than the safety valve value, the energy plan will not be executed.

[0035] In this method, since boiling water requires a large amount of electricity, before using the remaining electricity to control the water heater to boil water, it is necessary to determine whether the predicted maximum electricity consumption can support the water heater to boil water, and whether participating in the energy plan will lead to the purchase of electricity. If the predicted maximum electricity consumption can support the water heater to boil water and will not lead to the purchase of electricity, the water heater can be controlled to boil water during the period of the predicted maximum electricity consumption. This reduces the purchase of electricity and allows the user to receive subsidies from participating in the energy plan, further reducing the cost of use.

[0036] In one optional implementation, the weather information code includes a standard weather information code and a non-standard weather information code. The standard weather information code includes weather information codes corresponding to sunny, cloudy, rainy, and snowy conditions.

[0037] The method also includes: when the weather information code corresponding to the forecast weather for each hour of the next day is a non-standard weather information code, performing fuzzy matching on the non-standard weather information code of the forecast weather for each hour of the next day, and converting the non-standard weather information code of the forecast weather for each hour of the next day into the corresponding standard weather information code.

[0038] This method eliminates the need to classify and judge various weather conditions by setting basic weather conditions such as sunny, cloudy, rainy, and snowy, making it easier to predict the remaining power.

[0039] Secondly, the present invention provides a water heater control device based on household energy, the device comprising:

[0040] The data acquisition module is used to acquire the target household's historical electricity consumption data, weather data, and weather forecast data for the next day in the first time period.

[0041] The hourly remaining power calculation module is used to calculate the hourly remaining power for each hour of the past first time period based on the historical power data of the past first time period.

[0042] The hourly remaining power prediction module is used to calculate the predicted hourly remaining power for the next day based on the weather data of the first time period in the past, the weather forecast data for the next day, and the hourly remaining power.

[0043] The power prediction module is used to predict the remaining power based on the hourly power consumption of the next day, calculate the maximum predicted power consumption, and determine the time period corresponding to the maximum predicted power consumption.

[0044] The energy plan judgment module is used to calculate the maximum amount of electricity required for the target household's water heater to heat water, and based on the maximum amount of electricity required to heat water and the predicted maximum amount of electricity, it determines whether to execute the energy plan.

[0045] The energy plan execution module is used to control the water heater to heat water during the period of highest predicted electricity consumption when it is determined to execute the energy plan.

[0046] Thirdly, the present invention provides a computer device, comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to perform the water heater control method based on household energy as described in the first aspect or any corresponding embodiment thereof.

[0047] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to execute the home energy-based water heater control method described in the first aspect or any corresponding embodiment thereof. Attached Figure Description

[0048] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0049] Figure 1 This is a schematic flowchart of a water heater control method based on household energy according to an embodiment of the present invention;

[0050] Figure 2 This is a table showing the weather and remaining electricity for a family during the week, according to an embodiment of the present invention.

[0051] Figure 3 This is a schematic flowchart of another water heater control method based on household energy according to an embodiment of the present invention.

[0052] Figure 4 This is a schematic flowchart of another water heater control method based on household energy according to an embodiment of the present invention.

[0053] Figure 5 This is a schematic flowchart of another water heater control method based on household energy according to an embodiment of the present invention.

[0054] Figure 6This is a structural block diagram of a water heater control device based on household energy according to an embodiment of the present invention;

[0055] Figure 7 This is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. Detailed Implementation

[0056] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0057] In related technologies, since water heaters consume a large proportion of electricity among home appliances, users often need to heat the water in advance to reach a suitable temperature before use. For families that have installed solar panels, smart meters, and water heaters and are using SmartHome, the lack of a suitable water heating schedule often leads to energy waste and unnecessary money and energy consumption.

[0058] To address the aforementioned problems, this invention provides a water heater control method based on household energy, used in a computer device. It should be noted that the executing entity can be a water heater control device based on household energy. This device can be implemented as part or all of the computer device through software, hardware, or a combination of both. The computer device can be a terminal, client, or server. The server can be a single server or a server cluster composed of multiple servers. In this embodiment, the terminal can be a smartphone, personal computer, tablet computer, or other smart hardware device. The following method embodiments all use a computer device as the executing entity for illustration.

[0059] The computer equipment in this embodiment is suitable for households that have installed solar panels, smart meters, and specified models of water heaters and are using SmartHome. In these households, the solar power generator is connected to the power grid, allowing excess electricity to be sold to the grid or consumed by the household itself. This invention provides a water heater control method based on household energy. By using historical data, it calculates the remaining electricity consumption for each hour in the past, obtains the weather forecast for the next day, and compares the weather forecast for the next day with the weather forecast for the same time in the past. This predicts the maximum electricity consumption and the corresponding time period, allowing the water heater to heat water during this time period. This avoids the need to purchase additional electricity for heating water, further saving energy and electricity costs, and reducing the overall cost of using a smart home.

[0060] According to an embodiment of the present invention, a method for controlling a water heater based on household energy is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0061] This embodiment provides a water heater control method based on household energy, which can be used in the aforementioned computer equipment. Figure 1 This is a flowchart of a water heater control method based on household energy according to an embodiment of the present invention, such as... Figure 1 As shown, the process includes the following steps:

[0062] Step S101: Obtain the target household's historical electricity consumption data, weather data, and weather forecast data for the next day for the first time period.

[0063] In one example, before performing calculations, relevant device data needs to be collected, recorded, and stored as historical data for predictive calculations. Specifically, this includes:

[0064] 1) Collect weather information. Weather information is obtained through weather forecast services. Weather information is stored in hourly increments, with daily weather information stored.

[0065] 2) Collect the power consumption data (power_consumption_hp) generated by the electric water heater devices. This data represents the electricity consumption of the water heaters in the household. If there are multiple water heater devices in the household, the total electricity consumption of all water heater devices is calculated as the sum of their power consumption. The data collection period for the total electricity consumption of all water heaters in the household is recorded once per hour.

[0066] 3) Collect data from the smart meter (SW) device, including: `power_purchase` (household electricity consumption) and `power_sell` (household electricity sales). The smart meter device has the function of recording the electricity consumption and sales values. The data collection period for the smart meter device is hourly, collecting the `power_purchase` and `power_sell` values ​​for household electricity consumption. Specifically, the electricity generated is also recorded by the smart meter. Store the collected and acquired information in a history table. Key field information is referenced below.

[0067] {

[0068] home_id: Data type is String, representing the family ID for which data was collected;

[0069] timestamp: Data type is Long, representing the UTC timestamp information of the collected data;

[0070] weather_code: The data type is Integer, representing the weather code information for the current hour.

[0071] power_purchase: Data type Long, the electricity consumption value recorded by the smart meter at the current time;

[0072] power_sell: Data type is Long, the amount of electricity sold as recorded by the smart meter at the current time;

[0073] power_consumption_hp: Data type Long, the total energy consumed by the target household's electric water heater at the current time;

[0074] When storing data, the collected device data and corresponding weather information are stored together at the same time for later use in predicting power supply based on future weather information. As shown in Table 1, Table 1 shows the historical data of a certain household.

[0075] Table 1

[0076]

[0077]

[0078] The purpose of the energy plan is to utilize excess daytime electricity for heating water in the water heater. The energy plan is set for the water heater, and participation in the energy plan determines whether water is heated earlier and during the specified time period. Because the energy plan is related to daytime surplus electricity, the relevant data for the calculation period during historical data analysis is set to be the data set from 9:00 AM to 5:00 PM.

[0079] Retrieve historical data of the target household from the historical database for a certain period of time (taking 7 days as an example), including: the hourly collection data set of the total power consumption of the water heater power_consumption_hp in the past 7 days, the hourly collection data set of the powerSell of the electricity meter, and the hourly weather_code record data set;

[0080] Each historical data entry from 9:00 to 17:00 each day for the past 7 days includes information such as: {home_id, timestamp, weather_code, power_purchase, power_sell, power_consumption_hp}.

[0081] Step S102: Based on the historical power consumption data of the first time period, calculate the hourly remaining power consumption for each hour of the first time period.

[0082] In one example, the data structure table obtained by calculating the remaining power for each hour over the past week is: {home_id,timestamp,weather_code,power_purchase,power_sell,power_consumption_hp,powerSurplus}, where PowerSurplus is the remaining power for each hour over the past week.

[0083] Step S103: Based on the weather data of the first time period in the past, the weather forecast data for the next day, and the hourly remaining power, calculate the predicted hourly remaining power for the next day.

[0084] In one example, since obtaining the next day's weather information after 9 PM is more accurate, hourly weather information for the next day is obtained every night after 9 PM. The weather information for the past 7 days is compared with the weather information for tomorrow, and data within the same hour where the weather for the past 7 days and the next day's weather are consistent is recorded as {home_id, timestamp, weather_code, powerSurplus}. The weather forecast information for the target family's area for tomorrow (with relatively accurate GPS location) is queried, and the weather forecast for the past 7 days is compared with the weather forecast for tomorrow. Data within the same hour where the weather for the past 7 days and the next day's weather are consistent is recorded. Since multiple values ​​may be obtained based on past weather and time data for the hourly remaining battery power, the smaller value is selected as the predicted remaining battery power for the corresponding hour tomorrow.

[0085] Figure 2 This is a table showing the weather and remaining electricity for a household during a week, according to an embodiment of the present invention. Figure 2 As shown, the weather at 12:00 PM tomorrow will be sunny. Looking at the weather over the past 7 days, the weather at 12:00 PM 6 days ago, 3 days ago, 2 days ago, and 1 day ago was also sunny. Therefore, the remaining power at 12:00 PM 6 days ago, 3 days ago, 2 days ago, and 1 day ago were all recorded. The minimum value among these records is taken as the predicted remaining power at 12:00 PM tomorrow. The predicted remaining power at 12:00 PM tomorrow is 1400.

[0086] Step S104: Based on the predicted remaining power for the next day, calculate the maximum predicted power and determine the time period corresponding to the maximum predicted power.

[0087] In one example, let's assume the default heating time for an electric water heater implementing an energy plan is three hours. The sum of the predicted remaining power consumption for three consecutive hours, starting from each hour as a first time point, is recorded as the maximum predicted remaining power consumption at that first time point, forming new time-based data. Based on the hourly predictions, the sum of the predicted remaining power consumption (Predicted PowerSurplus) for each consecutive three-hour period is calculated, and the maximum value is taken as PowerPeak. The time point information corresponding to PowerPeak is recorded as PowerPeakHour. PowerPeak represents the maximum predicted remaining power consumption within a continuous time period; PowerPeak is used in subsequent steps to calculate whether to allow the water heater to participate in the energy plan to heat water in advance, while PowerPeakHour is used to set the time point when the water heater starts heating water.

[0088] Step S105: Calculate the maximum electricity required for the target household's water heater to heat water. Based on the maximum electricity required for heating water and the predicted maximum electricity, determine whether to execute the energy plan.

[0089] In one example, since the purpose of the energy plan is to use excess daytime electricity for heating water in a water heater, reducing electricity purchases while also receiving subsidies, the energy plan is executed when the predicted maximum electricity consumption can meet the needs of daytime water heating.

[0090] Step S106: When determining to execute the energy plan, control the water heater to heat water during the period of predicted maximum electricity consumption.

[0091] In one example, if it is determined that an energy plan will be executed, an energy plan Shift is issued to the current household's electric water heater device HP, which controls the water heater to heat water during the period of highest predicted electricity consumption.

[0092] The water heater control method based on household energy provided in this embodiment calculates the remaining electricity consumption per hour in the past using historical data, obtains the weather forecast for the next hour, and compares the weather forecast for the next hour with the weather forecast for the same time in the past to predict the remaining electricity consumption per hour in the next day. It then determines the maximum predicted electricity consumption and the corresponding time period, controls the water heater to heat water during this time period, avoids the need to purchase additional electricity for heating water, further saves energy and electricity costs, and reduces the cost of using smart homes.

[0093] This embodiment provides a water heater control method based on household energy, which can be used in the aforementioned computer equipment. Figure 3 This is a flowchart of another water heater control method based on household energy according to an embodiment of the present invention, such as... Figure 3 As shown, the process includes the following steps:

[0094] Step S301: Obtain the target household's historical electricity consumption data for the first time period, weather data for the first time period, and weather forecast data for the next day. For details, please refer to [link to relevant documentation]. Figure 1 Step S101 of the illustrated embodiment will not be described again here.

[0095] Step S302: Based on the historical power consumption data of the first time period in the past, calculate the hourly remaining power consumption for each hour of the first time period in the past.

[0096] Specifically, the target household's historical electricity consumption data for the first time period includes: the target household's hourly water heater electricity consumption and hourly electricity sales for the first time period. Step S302 includes:

[0097] Step S3021: Based on the historical electricity data of the first time period in the past, determine whether the electricity sold in the current hour of the first time period in the past is greater than zero.

[0098] Step S3022: If the electricity sold in the current hour of the past first time period is not greater than zero, determine that the remaining electricity for the current hour of the past first time period is zero.

[0099] Step S3023: When the electricity sold in the current hour of the past first time period is greater than zero, calculate the difference between the electricity sold in the current hour of the past first time period and the electricity sold in the previous hour of the past first time period, and the difference between the electricity consumed by the water heater in the current hour of the past first time period and the electricity consumed by the water heater in the previous hour of the past first time period. Add the difference in electricity sold and the difference in electricity consumed by the water heater to obtain the remaining electricity for the current hour of the past first time period.

[0100] In one example, to determine that the water heater uses surplus electricity, a condition needs to be added: electricity sold > 0. This means that even after the water heater finishes heating the water, there is still residual electricity generated, indicating that the water heater is using generated electricity, not purchased electricity. Using historical data from the past 7 days, each historical data point from 9:00 AM to 5:00 PM contains information such as {home_id, timestamp, weather_code, power_purchase, power_sell, power_consumption_hp}. The remaining electricity (PowerSurplus) for each hour over the past 7 days is calculated, including:

[0101] When power_sell = 0, the remaining power powerSurplus = 0.

[0102] When the sold power (power_sell) > 0, the remaining power (powerSurplus) is calculated as follows:

[0103] powerSurplus = Increment of power_sell + Increment of power_consumption_hp

[0104] The increment of `power_sell` is the current hour's `power_sell` minus the previous hour's `power_sell` value; the increment of `power_consumption_hp` is the actual power consumption of the water heater, obtained by subtracting the previous hour's `power_consumption_hp` value from the current hour's `power_consumption_hp` data. The resulting data structure table is: {home_id, timestamp, weather_code, power_purchase, power_sell, power_consumption_hp, powerSurplus}.

[0105] Step S303: Based on the weather data from the previous first time period, the weather forecast data for the next day, and the hourly remaining power, calculate the predicted hourly remaining power for the next day. For details, please refer to [link to details]. Figure 1 Step S103 of the illustrated embodiment will not be described again here.

[0106] Step S304: Based on the predicted remaining electricity for the next day's hours, calculate the maximum predicted electricity value and determine the time period corresponding to the maximum predicted electricity value. For details, please refer to [link to details]. Figure 1 Step S104 of the illustrated embodiment will not be described again here.

[0107] Step S305: Calculate the maximum electricity required for the target household's water heater to heat water. Based on the maximum electricity required for heating water and the predicted maximum electricity consumption, determine whether to execute the energy plan. For details, please refer to [link to relevant documentation]. Figure 1 Step S105 of the illustrated embodiment will not be described again here.

[0108] Step S306: When determining to execute the energy plan, control the water heater to heat water during the period of predicted maximum electricity consumption. See details below. Figure 1 Step S106 of the illustrated embodiment will not be described again here.

[0109] The water heater control method based on household energy provided in this embodiment can more easily and intuitively determine whether the remaining electricity in each past hour is zero by judging the household's electricity sales, thus making it easier for users to determine the current energy status of their household. When the household's electricity sales are not zero, the remaining electricity in each past hour can be calculated more easily and intuitively by adding the difference between the electricity sales in the current hour and the electricity consumed by the water heater.

[0110] This embodiment provides a water heater control method based on household energy, which can be used in the aforementioned computer equipment. Figure 4 This is a flowchart of another water heater control method based on household energy according to an embodiment of the present invention, such as... Figure 4 As shown, the process includes the following steps:

[0111] Step S401: Obtain the target household's historical electricity consumption data for the first time period, weather data for the first time period, and weather forecast data for the next day. For details, please refer to [link to relevant documentation]. Figure 3 Step S301 of the illustrated embodiment will not be described again here.

[0112] Step S402: Based on the historical electricity consumption data of the first time period, calculate the remaining hourly electricity consumption for each hour of the first time period. For details, please refer to [link to details]. Figure 3 Step S302 of the illustrated embodiment will not be described again here.

[0113] Step S403: Based on the weather data of the first time period in the past, the weather forecast data for the next day, and the hourly remaining power, calculate the predicted hourly remaining power for the next day.

[0114] Specifically, the weather data for the first time period in the past and the weather forecast data for the next day include: the weather information code corresponding to the weather for each hour of the first time period in the past and the weather information code corresponding to the forecast weather for each hour of the next day. Step S403 above includes:

[0115] Step S4031: Based on the weather information code corresponding to the predicted weather for each hour of the next day, filter historical moments within the first time period that have the same weather information code as the predicted weather for each hour of the next day, and obtain the hourly remaining battery power for the filtered historical moments.

[0116] Step S4032: Determine the minimum hourly remaining power at each historical time as the predicted hourly remaining power for the next day.

[0117] In one example, the weather information codes for the past 7 days and tomorrow's weather information codes are compared. Data where the weather for the past 7 days and the next day's weather are consistent within the same hour is found and recorded as {home_id, timestamp, weather_code, powerSurplus}. Table 2 shows the weather information for a specific household, along with weekly weather and remaining electricity consumption.

[0118] Table 2

[0119]

[0120]

[0121] The weather will be sunny at 12 noon tomorrow, with a weather information code of 100. Looking at the weather over the past 7 days, the weather at 12 noon 6 days ago, 3 days ago, 2 days ago, and 1 day ago was also sunny, with a weather information code of 100. Record the remaining battery level at 12 noon 6 days ago, 3 days ago, 2 days ago, and 1 day ago.

[0122] The minimum remaining power at 12:00 noon from 6 days ago, 3 days ago, 2 days ago, and 1 day ago is taken as the predicted remaining power at 12:00 noon tomorrow. For example, in Table 2, the predicted remaining power at 12:00 noon tomorrow is 1400.

[0123] In some optional implementations, the weather information code includes standard weather information codes and non-standard weather information codes. The standard weather information code includes weather information codes corresponding to sunny, cloudy, rainy, and snowy conditions. Step S4031 above includes:

[0124] Step a1: When the weather information code corresponding to the forecast weather for each hour of the next day is a non-standard weather information code, perform fuzzy matching on the non-standard weather information code of the forecast weather for each hour of the next day, and convert the non-standard weather information code of the forecast weather for each hour of the next day into the corresponding standard weather information code.

[0125] In one example, when calculating the predicted remaining power based on the next day's weather code information, if no matching weather code is found using the accurate pattern, a fuzzy pattern can be used to obtain the remaining power value corresponding to the time period of the matching weather code. Table 3 shows the correspondence between weather information codes and fuzzy patterns.

[0126] Table 3

[0127]

[0128]

[0129] As can be seen, the standard weather information codes include: sunny (100), cloudy (200), rainy (300), and snowy (400). In fuzzy mode, the weather information code is 400 for sleet; 100 for sunny; 100 for very hot weather; 200 for cloudy; 300 for light rain; 300 for heavy rain or torrential rain; and 400 for heavy snow.

[0130] This method eliminates the need to classify and judge various weather conditions by setting basic weather conditions such as sunny, cloudy, rainy, and snowy, making it easier to predict the remaining power.

[0131] Step S404: Based on the predicted remaining power for the next day, calculate the maximum predicted power and determine the time period corresponding to the maximum predicted power.

[0132] Specifically, step S404 includes:

[0133] Step S4041: Based on the predicted remaining electricity for the next day, calculate the sum of the predicted remaining electricity for the next day from the current time to the next time, until the sum of the predicted electricity for each time period is obtained. The interval between the current time and the next time is the time required for the water heater to heat water.

[0134] Step S4042: Filter the maximum value of the sum of predicted power consumption in each time period, record it as the maximum predicted power consumption, and determine the time period with the maximum predicted power consumption corresponding to the maximum predicted power consumption.

[0135] In one example, the sum of predicted remaining power (Predicted PowerSurplus) over three consecutive hours is calculated, and the maximum value is taken as the predicted remaining power (PowerPeak), including:

[0136] Query the weather forecast for your home's area tomorrow (GPS location is relatively accurate). Compare the weather forecast for the past 7 days with tomorrow's forecast, and record the instances where the weather in the past 7 days and the next day's weather (within the same hour) are consistent. The Predicted PowerSurplus data includes the predicted remaining power for each hour. Multiple values ​​may be obtained based on past weather and time data; if multiple values ​​exist, select the smaller value as the predicted remaining power for the corresponding hour tomorrow.

[0137] After calculating the predicted remaining power data for each hour, the sum of the predicted remaining power for three consecutive hours starting from a certain first time point is recorded as the maximum predicted remaining power at that first time point, forming new time reference data. Table 4 is a time correspondence table of the sum of predicted remaining power for three consecutive hours, as shown in Table 4:

[0138] Table 4

[0139]

[0140] Based on the hourly forecast values, calculate the sum of predicted remaining power (PredictedPowerSurplus) for every three consecutive hours, and take the maximum value as PowerPeak. Record the time point information corresponding to PowerPeak as PowerPeakHour. PowerPeak is the maximum predicted remaining power sum within a continuous period. PowerPeak is used in subsequent steps to calculate whether to allow the water heater to participate in the energy plan to heat water in advance, and PowerPeakHour is used to set the time point when the water heater starts heating water.

[0141] Step S405: Calculate the maximum electricity required for the target household's water heater to heat water. Based on the maximum electricity required for heating water and the predicted maximum electricity consumption, determine whether to execute the energy plan. For details, please refer to [link to relevant documentation]. Figure 3 Step S305 of the illustrated embodiment will not be described again here.

[0142] Step S406: When determining to execute the energy plan, control the water heater to heat water during the period of predicted maximum electricity consumption. See details below. Figure 3 Step S306 of the illustrated embodiment will not be described again here.

[0143] The water heater control method based on household energy provided in this embodiment facilitates the processing and comparison of weather datasets by setting weather information codes. By comparing the weather at the same time in the past, the remaining electricity consumption at the same time the following day is predicted, further improving the reliability and accuracy of remaining electricity consumption prediction. Using the water heater heating period as a step size, the predicted hourly electricity consumption for the following day is divided into multiple time periods. The sum of the predicted electricity consumption for each time period is calculated, and the maximum predicted electricity consumption and the maximum time period are selected to determine the arrangement of the heating period, facilitating subsequent control of the water heater's heating process.

[0144] This embodiment provides a water heater control method based on household energy, which can be used in the aforementioned computer equipment. Figure 5 This is a flowchart of another water heater control method based on household energy according to an embodiment of the present invention, such as... Figure 5 As shown, the process includes the following steps:

[0145] Step S501: Obtain the target household's historical electricity consumption data for the first time period, weather data for the first time period, and weather forecast data for the next day. For details, please refer to [link to relevant documentation]. Figure 4 Step S401 of the illustrated embodiment will not be described again here.

[0146] Step S502: Based on the historical electricity consumption data of the first time period, calculate the remaining hourly electricity consumption for each hour of the first time period. For details, please refer to [link to details]. Figure 4 Step S402 of the illustrated embodiment will not be described again here.

[0147] Step S503: Based on the weather data from the first time period, the weather forecast data for the next day, and the hourly remaining power, calculate the predicted hourly remaining power for the next day. For details, please refer to [link to details]. Figure 4 Step S403 of the illustrated embodiment will not be described again here.

[0148] Step S504: Based on the predicted remaining electricity for the next day's hours, calculate the maximum predicted electricity value and determine the time period corresponding to the maximum predicted electricity value. For details, please refer to [link to details]. Figure 4 Step S404 of the illustrated embodiment will not be described again here.

[0149] Step S505: Calculate the maximum electricity required for the target household's water heater to heat water. Based on the maximum electricity required for heating water and the predicted maximum electricity, determine whether to execute the energy plan.

[0150] Specifically, step S505 includes:

[0151] Step S5051: Determine the safety valve based on the maximum power required for boiling water.

[0152] Step S5052: Determine whether the predicted maximum power consumption is greater than the maximum power consumption required for boiling water, and determine whether the predicted maximum power consumption is greater than the safety valve value.

[0153] Step S5053: When the predicted maximum power consumption is greater than the maximum power consumption required for boiling water and the predicted maximum power consumption is greater than the safety valve value, determine to execute the energy plan.

[0154] Step S5054: If the predicted maximum power consumption is not greater than the maximum power consumption required for boiling water or the predicted maximum power consumption is not greater than the safety valve value, determine that the energy plan will not be executed.

[0155] In one example, the predicted power consumption for heating water is obtained from the electric water heater. The electric water heater device provides an attribute query to obtain the power consumption required for heating water (this data is automatically generated by the actual smart water heater device). The maximum power consumption required for heating water (PowerHeat) is calculated: the estimated power consumption value for heating water is obtained from the HP of the current household's electric water heater devices. If there are multiple water heaters, the sum of their power consumption values ​​(EPC CB) is calculated to obtain the maximum power consumption required for heating water (PowerHeat).

[0156] If the following two conditions are met, the participant will be deemed to participate in the energy plan. If the conditions are not met, the participant will not participate. The conditions are: PowerPeak > PowerHeat and PowerPeak > PowerTh (1500). PowerTh is a safety valve set up to prevent the participant from having to buy electricity due to participating in the energy plan. It is an initial fixed value and can be adjusted later based on more data and experience.

[0157] Step S406: When determining to execute the energy plan, control the water heater to heat water during the period of predicted maximum electricity consumption. See details below. Figure 4 Step S406 of the illustrated embodiment will not be described again here.

[0158] The water heater control method based on household energy provided in this embodiment requires a judgment before using the remaining power to control the water heater to heat water, since the power consumption for heating water is relatively large. This judgment is based on whether the predicted maximum power consumption can support the water heater to heat water and whether participating in the energy plan will lead to the purchase of electricity. If the predicted maximum power consumption can support the water heater to heat water and will not lead to the purchase of electricity, the water heater is controlled to heat water during the period of the predicted maximum power consumption. This reduces the purchase of electricity and allows the user to obtain subsidies from participating in the energy plan, further reducing the cost of use.

[0159] This embodiment also provides a water heater control device based on household energy, which is used to implement the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0160] This embodiment provides a water heater control device based on household energy, such as... Figure 6 As shown, it includes:

[0161] Data acquisition module 601 is used to acquire historical electricity consumption data, weather data, and next day's weather forecast data for the target household in the first time period. For details, please refer to [link / reference needed]. Figure 1 Step S101 of the illustrated embodiment will not be described again here.

[0162] The hourly remaining power calculation module 602 is used to calculate the hourly remaining power for each hour of the past first time period based on historical power data; for details, please refer to [link to relevant documentation]. Figure 1 Step S102 of the illustrated embodiment will not be described again here.

[0163] The hourly remaining power prediction module 603 is used to calculate the predicted hourly remaining power for the next day based on weather data from the previous first time period, the next day's weather forecast data, and the hourly remaining power. For details, please refer to [link to relevant documentation]. Figure 1 Step S103 of the illustrated embodiment will not be described again here.

[0164] The power prediction module 604 is used to predict the remaining power based on the hourly forecast for the next day, calculate the maximum predicted power value, and determine the time period corresponding to the maximum predicted power value; for details, please refer to [link to relevant documentation]. Figure 1 Step S104 of the illustrated embodiment will not be described again here.

[0165] The energy plan judgment module 605 calculates the maximum electricity required for the target household's water heater to heat water, and determines whether to execute the energy plan based on the maximum electricity required for heating water and the predicted maximum electricity. For details, please refer to [link to relevant documentation]. Figure 1 Step S105 of the illustrated embodiment will not be described again here.

[0166] The energy planning execution module 606 is used to control the water heater to heat water during the period of highest predicted electricity consumption when determining to execute an energy plan. For details, please refer to [link to details]. Figure 1 Step S106 of the illustrated embodiment will not be described again here.

[0167] Further functional descriptions of the above modules and units are the same as those in the corresponding embodiments described above, and will not be repeated here.

[0168] In this embodiment, the water heater control device based on household energy is presented in the form of a functional unit. Here, a unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0169] This invention also provides a computer device having the above-described features. Figure 7 The image shows a water heater control device based on home energy.

[0170] Please see Figure 7 , Figure 7 This is a schematic diagram of the structure of a computer device provided in an optional embodiment of the present invention, such as... Figure 7As shown, the computer device includes one or more processors 10, memory 20, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components communicate with each other via different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on external input / output devices (such as display devices coupled to the interfaces). In some alternative implementations, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 7 Take a processor 10 as an example.

[0171] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GDA), or any combination thereof.

[0172] The memory 20 stores instructions executable by at least one processor 10 to cause the at least one processor 10 to perform the method shown in the above embodiments.

[0173] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the computer device. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 20 may optionally include memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0174] The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0175] The computer device also includes a communication interface 30 for communicating with other devices or communication networks.

[0176] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code, which, when accessed and executed by the computer, processor, or hardware, implements the methods shown in the above embodiments.

[0177] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A water heater control method based on household energy, characterized in that, The method includes: Obtain historical electricity consumption data, weather data, and next day's weather forecast data for the target household in the first time period. Based on the historical electricity consumption data of the past first time period, the hourly remaining electricity consumption for each hour of the past first time period is calculated; the historical electricity consumption data of the target household in the past first time period includes: the hourly electricity consumption of the water heater and the hourly electricity sold by the target household in the past first time period; The calculation of the hourly remaining electricity for each hour of the past first time period based on the historical electricity data of the past first time period includes: Based on the historical electricity data of the past first time period, determine whether the electricity sold in the current hour of the past first time period is greater than zero; If the electricity sold in the current hour of the past first time period is not greater than zero, the remaining electricity for the current hour of the past first time period is determined to be zero. Based on the weather data of the past first time period, the weather forecast data for the next day, and the hourly remaining power, the hourly predicted remaining power for the next day is calculated; the weather data of the past first time period and the weather forecast data for the next day include: the weather information code corresponding to the weather of each hour of the past first time period and the weather information code corresponding to the predicted weather of each hour of the next day. The calculation of the predicted hourly remaining power for the next day based on the weather data of the past first time period, the weather forecast data for the next day, and the hourly remaining power includes: Based on the weather information code corresponding to the predicted weather for each hour of the next day, filter historical moments within the first time period that have the same weather information code corresponding to the predicted weather for each hour of the next day, and obtain the hourly remaining battery power for the filtered historical moments. The minimum hourly remaining power at each of the aforementioned historical moments is determined as the predicted hourly remaining power for the next day. Based on the predicted remaining power for the next day, calculate the maximum predicted power and determine the time period corresponding to the maximum predicted power. The step of calculating the maximum predicted power consumption based on the hourly predicted power consumption for the next day, and determining the time period corresponding to the maximum predicted power consumption, includes: Based on the predicted remaining electricity for the next day, the sum of the predicted remaining electricity for the next day from the current time to the next time is calculated until the sum of the predicted electricity for each time period is obtained. The interval between the current time and the next time is the time required for the water heater to heat water. The maximum value of the sum of predicted electricity consumption for each time period is selected and recorded as the maximum predicted electricity consumption. The time period corresponding to the maximum predicted electricity consumption is then determined. Calculate the maximum electricity required for the target household's water heater to heat water, and determine whether to execute the energy plan based on the maximum electricity required for heating water and the predicted maximum electricity. When determining to execute an energy plan, the water heater is controlled to heat water during the period of maximum predicted electricity consumption.

2. The method according to claim 1, characterized in that, Also includes: When the electricity sold in the current hour of the past first time period is greater than zero, calculate the difference between the electricity sold in the current hour of the past first time period and the electricity sold in the previous hour of the past first time period, and calculate the difference between the electricity consumed by the water heater in the current hour of the past first time period and the electricity consumed by the water heater in the previous hour of the past first time period. Add the difference in electricity sold and the difference in electricity consumed by the water heater to obtain the remaining electricity for the current hour of the past first time period.

3. The method according to claim 1, characterized in that, The step of determining whether to execute the energy plan based on the maximum electricity required for boiling water and the predicted maximum electricity consumption includes: The safety valve is determined based on the maximum power required for boiling water; Determine whether the predicted maximum power consumption is greater than the maximum power consumption required for boiling water, and determine whether the predicted maximum power consumption is greater than the safety valve value; When the predicted maximum power consumption is greater than the maximum power consumption required for boiling water, and the predicted maximum power consumption is greater than the safety valve value, the energy plan is determined to be executed. If the predicted maximum power consumption is not greater than the maximum power consumption required for boiling water or the predicted maximum power consumption is not greater than the safety valve value, it is determined that the energy plan will not be executed.

4. The method according to claim 3, characterized in that, The weather information code includes standard weather information codes and non-standard weather information codes. The standard weather information codes include weather information codes corresponding to sunny, cloudy, rainy, and snowy weather. The method further includes: when the weather information code corresponding to the forecast weather for each hour of the next day is a non-standard weather information code, performing fuzzy matching on the non-standard weather information code of the forecast weather for each hour of the next day, and converting the non-standard weather information code of the forecast weather for each hour of the next day into the corresponding standard weather information code.

5. A water heater control device based on household energy, characterized in that, The device includes: The data acquisition module is used to acquire the target household's historical electricity consumption data, weather data, and weather forecast data for the next day in the first time period. The hourly remaining power calculation module is used to calculate the hourly remaining power for each hour of the past first time period based on the historical power data of the past first time period; the historical power data of the target household in the past first time period includes: the hourly power consumption of the water heater and the hourly power sold by the target household in the past first time period; The calculation of the hourly remaining electricity for each hour of the past first time period based on the historical electricity data of the past first time period includes: Based on the historical electricity data of the past first time period, determine whether the electricity sold in the current hour of the past first time period is greater than zero; If the electricity sold in the current hour of the past first time period is not greater than zero, the remaining electricity for the current hour of the past first time period is determined to be zero. The hourly remaining power prediction module is used to calculate the predicted hourly remaining power for the next day based on the weather data of the past first time period, the weather forecast data for the next day, and the hourly remaining power. The weather data of the past first time period and the weather forecast data for the next day include: the weather information code corresponding to the weather of each hour of the past first time period and the weather information code corresponding to the predicted weather of each hour of the next day. The calculation of the predicted hourly remaining power for the next day based on the weather data of the past first time period, the weather forecast data for the next day, and the hourly remaining power includes: Based on the weather information code corresponding to the predicted weather for each hour of the next day, filter historical moments within the first time period that have the same weather information code corresponding to the predicted weather for each hour of the next day, and obtain the hourly remaining battery power for the filtered historical moments. The minimum hourly remaining power at each of the aforementioned historical moments is determined as the predicted hourly remaining power for the next day. The power prediction module is used to calculate the maximum predicted power based on the hourly predicted power for the next day, and determine the time period corresponding to the maximum predicted power. The step of calculating the maximum predicted power based on the hourly predicted power for the next day and determining the time period corresponding to the maximum predicted power includes: Based on the predicted remaining electricity for the next day, the sum of the predicted remaining electricity for the next day from the current time to the next time is calculated until the sum of the predicted electricity for each time period is obtained. The interval between the current time and the next time is the time required for the water heater to heat water. The maximum value of the sum of predicted electricity consumption for each time period is selected and recorded as the maximum predicted electricity consumption. The time period corresponding to the maximum predicted electricity consumption is then determined. The energy plan judgment module is used to calculate the maximum amount of electricity required for the target household's water heater to heat water, and to determine whether to execute the energy plan based on the maximum amount of electricity required to heat water and the predicted maximum amount of electricity. An energy plan execution module is used to control the water heater to heat water during the period of maximum predicted electricity consumption when it is determined to execute an energy plan.

6. A computer device, characterized in that, include: A memory and a processor are communicatively connected, the memory stores computer instructions, and the processor executes the computer instructions to perform the water heater control method based on any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to perform any one of the home energy-based water heater control methods according to claims 1 to 4.

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