Energy-saving and environment-friendly green building energy-saving control method and system

Abstract

The invention provides an energy-saving and environment-friendly green building energy-saving control method and system. Intelligent energy management is realized by integrating historical energy consumption data and meteorological prediction information. The method comprises the following steps: firstly, acquiring power consumption data of a green building and generating capacity data of a self-generating module in a preset period, and calculating a corresponding average value as a benchmark reference; and the time period electricity demand and the power generation capacity of the building are estimated in combination with weather prediction data on the day, and the current-day estimated electricity consumption and generation capacity difference value is calculated. Based on the difference value and time-interval energy data, through predictive scheduling, the system identifies an energy supply and demand gap in advance, and timely starts an energy-saving measure or adjusts a power utilization strategy. The building energy utilization efficiency is effectively improved, the renewable energy potential is fully played, the daily electricity utilization cost is reduced, meanwhile, carbon emission is reduced, and organic unification of economic benefits and environmental benefits is achieved.

Description

Technical Field

[0001] The present application relates to the field of green buildings, and in particular to an energy-saving and environmentally friendly green building energy-saving control method and system. Background Art

[0002] Energy-saving and environmentally friendly green buildings are a key development direction for modern architecture. A variety of mature energy-saving control technologies have been developed. Currently, mainstream energy-saving control methods include intelligent lighting control systems, HVAC automatic control systems, and integrated renewable energy control systems.

[0003] Intelligent lighting control systems use sensors to detect ambient light and human activity, automatically adjusting lamp brightness and on / off status, significantly reducing lighting energy consumption. While these systems offer rapid response and significant energy savings, their main drawbacks are high initial investment costs, sensor susceptibility to environmental interference, and misjudgment, resulting in frequent on / off switching that impacts user experience, and system complexity that increases the difficulty and cost of ongoing maintenance.

[0004] Automatic HVAC control systems are a core technology for green building energy conservation. They utilize temperature and humidity sensors and intelligent algorithms to achieve precise environmental control. While this system effectively balances comfort and energy efficiency, it has significant technical limitations. The system's adaptability to environmental fluctuations is poor, and control accuracy decreases significantly in extreme weather conditions. Furthermore, insufficient coordination between system components often leads to localized overheating or overcooling, which in turn increases energy consumption.

[0005] Although renewable energy integrated control systems represent the future development direction, the intermittent and unstable nature of renewable energy sources such as solar and wind energy makes it difficult for the system to ensure a continuous and stable energy supply. The high cost of energy storage equipment further exacerbates this problem. Summary of the Invention

[0006] This application provides an energy-saving and environmentally friendly green building energy-saving control method, comprising the following steps: A1, obtaining recent electricity consumption data of a preset green building and recent power generation data of a self-generating module of the green building according to a preset data reference period; A2, calculates the corresponding recent average electricity consumption and recent average power generation based on the recent electricity consumption data and recent power generation data; A3, obtaining the daily weather forecast data corresponding to the green building; A4, estimating the estimated electricity consumption data and estimated power generation data for the green building for the current time period based on the recent average electricity consumption, recent average power generation, and the current day's weather forecast data; A5, calculating the corresponding daily estimated electricity consumption and daily estimated power generation based on the daily estimated electricity consumption data and the daily estimated power generation data; A6, calculates the difference between the estimated power consumption and power generation for the day based on the estimated power consumption and power generation for the day; A7, performing energy-saving adjustment on the green building according to the difference of the estimated power generation amount on the day, the estimated power consumption data for the time period on the day, and the estimated power generation data for the time period on the day.

[0007] By adopting the above technical solution, the energy-saving and environmentally friendly green building energy-saving control method can realize energy supply and demand forecasting and data-based scheduling by integrating historical electricity consumption data, historical power generation data and weather forecast information, and can perform forward-looking energy-saving adjustments based on the estimated difference between electricity consumption and power generation, thereby improving the energy utilization efficiency of the building and giving play to the role of renewable energy, achieving a win-win situation in economic and environmental benefits.

[0008] Optionally, step A4 includes the following steps: A401, determining the corresponding weather forecast data for the current time period based on the preset time period sequence and the current time period weather forecast data; A402, combining the meteorological forecast data for each time period in the meteorological forecast data for the current time period and the recent average electricity consumption to generate comprehensive electricity consumption data for each corresponding time period; A403 generates comprehensive power generation data for each corresponding time period based on the meteorological forecast data for each time period and the recent average power generation in the meteorological forecast data for the current time period; A404, inputting the pre-trained time period power consumption prediction model based on the comprehensive power consumption data of each time period to generate the estimated power consumption for each corresponding time period; A405 generates the corresponding estimated power consumption data for the current day period based on the time period sequence and all the time period estimated power consumption comparison combinations; A406, inputting the pre-trained time period power generation prediction model based on the comprehensive power generation data of each time period to generate the estimated power generation for each corresponding time period; A407, generating the corresponding estimated power generation data for the current day period based on the period sequence and all the period estimated power generation comparison combinations.

[0009] By adopting the above technical solution, the energy-saving and environmentally friendly green building energy-saving control method can divide the whole day into multiple time periods for refined prediction, organically combine meteorological data with recent electricity consumption and power generation data to form comprehensive data, and use pre-trained machine learning models to predict electricity consumption and power generation in each time period respectively. This not only improves the time resolution of the prediction, but also captures the energy supply and demand fluctuation characteristics caused by changes in meteorological conditions in different time periods, providing more accurate and reliable data support for subsequent dynamic energy-saving regulation, making building energy-saving regulation more intelligent and refined.

[0010] Optionally, step A7 includes the following steps: A701 calculates and generates the corresponding daily time period estimated power consumption and power generation difference data based on the daily time period estimated power consumption data and the daily time period estimated power generation data; A702, determining the corresponding peak period estimated power generation difference data based on the preset daily peak period estimated power generation difference data; A703, determining the corresponding off-peak period estimated power generation difference data based on the preset off-peak period of the day and the estimated power generation difference data of the day; A704, supplying power to the green building via the self-generating module and the preset power storage module during the peak period of the day according to the estimated power generation difference data during the peak period; A705, if the difference in the estimated power generation amount for the day is greater than the preset operating condition conversion threshold, charging the energy storage module via the self-generation module and a preset external power supply during the off-peak period of the day according to the difference in the estimated power generation amount for the off-peak period; A706, if the difference in the estimated power generation for the day is less than or equal to the operating condition conversion threshold, then during the off-peak period of the day, heat and cold are stored in the preset storage temperature control module through the self-generation module and the external power supply according to the data of the difference in the estimated power generation for the off-peak period.

[0011] By adopting the above technical solution, the energy-saving and environmentally friendly green building energy-saving control method can reduce dependence on the external power grid and peak electricity price costs by giving priority to self-generation and energy storage systems during peak hours; during off-peak hours, the operation mode is flexibly adjusted according to the energy surplus situation. When the energy gap is large, the energy storage system is charged first, and when energy is relatively sufficient, off-peak electricity prices are used to store heat and cool. This not only achieves energy peak shaving and valley filling and economic optimization, but also effectively balances the building's heating and cooling needs throughout the day through storage-type temperature regulation, further achieving energy saving and environmental protection.

[0012] Optionally, the energy-saving and environmentally friendly green building energy-saving control method further includes the following steps for adjusting the room temperature: B1, determining the corresponding temperature forecast data for the current time period in the current day's weather forecast data according to a preset time period sequence; B2, calculating and determining the corresponding predicted temperature difference data for the time period of the day based on the temperature forecast data for the time period of the day and the preset comfortable temperature; B3, determining the predicted temperature difference for each corresponding time period based on the predicted temperature difference data for the current time period; B4, if the predicted temperature difference during a time period is greater than or equal to the preset cooling threshold, the corresponding time period is defined as a cooling period; B5, generating cooling period data according to all cooling period combinations, supplying cooling to the green building during corresponding periods according to the cooling period data, and recording corresponding cooling consumption data for the day; B6, if the predicted temperature difference during a time period is less than or equal to the preset heating threshold, the corresponding time period is defined as the heating time period; B7, generating heating period data according to all heating period combinations, supplying heat to the green building in corresponding periods according to the heating period data, and recording corresponding daily heat storage consumption data.

[0013] By adopting the above technical solution, the energy-saving and environmentally friendly green building energy-saving control method can realize a predictive temperature control strategy based on the temperature forecast data and comfortable temperature of the day period. By identifying the cooling and heating demand periods in advance, it can make full use of the cold and heat energy stored in the off-peak period, avoiding the start-up of high-energy-consuming air-conditioning equipment during peak electricity consumption periods. At the same time, by recording the cold storage heat consumption data, it provides data support for the continuous optimization of the system, and realizes the efficient combination of temperature regulation and energy management as a whole, which not only ensures the comfort of the indoor environment, but also minimizes the energy consumption cost and carbon emissions of the building.

[0014] Optionally, the energy-saving and environmentally friendly green building energy-saving control method further comprises the following steps: C1, obtain the current date, and determine the corresponding current seasonal time window in the preset seasonal cycle time window data based on the current date; C2, according to the current seasonal time window, obtain the corresponding historical seasonal time window daily cooling consumption data and historical seasonal time window daily heating consumption data; C3, based on the historical seasonal window daily cooling consumption data and the estimated cooling demand for the next day corresponding to the current date; C4 calculates the corresponding refrigerant storage temperature based on the preset upper limit temperature of cold storage, refrigerant storage capacity, refrigerant solid specific heat capacity, refrigerant liquid specific heat capacity, refrigerant melting latent heat, and the estimated cooling demand for the next day; C5, based on the historical seasonal window daily heat storage consumption data and the current date, the estimated heating demand for the next day; C6 calculates the corresponding heat storage temperature of the heat medium based on the preset lower limit temperature of heat storage, heat medium storage capacity, heat medium liquid specific heat capacity, and the estimated heat demand for the next day; C7, preferentially storing cold in the storage temperature adjustment module according to the cold storage temperature of the refrigerant; C8, and after the cold storage is completed, the storage temperature adjustment module is stored in heat according to the heat storage temperature of the heat medium.

[0015] By adopting the above technical solution, the energy-saving and environmentally friendly green building energy-saving control method can estimate the next day's heating and cooling demand according to the temperature control demand characteristics of different seasons, and calculate the cooling and heating storage temperature by considering the physical properties of the cold and hot media, so as to ensure that the stored cooling and heating amount can meet the next day's demand while avoiding energy waste caused by excessive energy storage, and make full use of the low-peak electricity and lower ambient temperature at night to improve the cooling efficiency, thereby realizing the overall cross-day energy balance and seasonal intelligent scheduling, and improving the energy utilization efficiency of green buildings.

[0016] Optionally, the energy-saving and environmentally friendly green building energy-saving control method further comprises the following steps: D1, obtaining the next day's weather forecast data corresponding to the green building; D2, estimating the next-day estimated electricity consumption data and next-day estimated power generation data corresponding to the green building based on the recent average electricity consumption, recent average power generation and next-day weather forecast data; D3, calculating the corresponding next day estimated electricity consumption and next day estimated power generation according to the next day estimated electricity consumption data and the next day estimated power generation data; D4, calculating the corresponding difference between the next day's estimated power consumption and the next day's estimated power generation; D5: If the difference between the next day's estimated power generation difference and the current day's estimated power generation difference is greater than a preset excess storage threshold, then the corresponding next day's estimated cooling demand update value is calculated by multiplying the preset excess cooling storage coefficient by the next day's estimated cooling demand, and the corresponding next day's estimated heating demand update value is calculated by multiplying the preset excess heating storage coefficient by the next day's estimated heating demand; D6, updating the next day's estimated cooling demand in step C4 according to the next day's estimated cooling demand update value; D7, updating the next day's estimated heating demand in step C6 according to the next day's estimated heating demand update value.

[0017] By adopting the above technical solution, the energy-saving and environmentally friendly green building energy-saving control method can identify in advance the risk of further power generation shortage on the next day by comparing the difference between the power generation on the current day and the next day; when it is predicted that the power generation gap on the next day will increase significantly, the excess energy storage strategy is triggered to ensure that the temperature control needs of the building can still be met in the event of energy shortage by increasing the demand for cold and heat storage, and reducing the cold and heat storage load on the next day to ensure that the energy storage module 20 can obtain sufficient electricity for charging.

[0018] Optionally, the energy-saving and environmentally friendly green building energy-saving control method further comprises the following steps: D8, during the preset peak period of the next day, the temperature difference between the cold storage temperature of the refrigerant and the heat storage temperature of the heat medium is consumed by the temperature difference power generation module of the storage temperature adjustment module to supply power to the green building.

[0019] By adopting the above technical solution, the energy-saving and environmentally friendly green building energy-saving control method can generate electricity by utilizing the temperature difference between the refrigerant and the heat medium in the storage temperature adjustment module 30, so that the energy storage system originally used only for temperature adjustment can be further used for temperature control and power generation. During the peak electricity consumption period of the next day, part of the temperature difference between the refrigerant and the heat medium is directly converted into electricity for building use through temperature difference power generation, which not only further reduces the dependence on the external power grid, but also realizes the cascade utilization of energy, thereby improving the energy utilization efficiency and economy of the entire green building.

[0020] This application also provides an energy-saving and environmentally friendly green building energy-saving control system, including: Self-generating module; Electric energy storage module; Storage temperature control module; Processing control module; The self-generating module, the electric energy storage module, and the storage-type temperature adjustment module are respectively electrically connected to a preset green building, the electric energy storage module and the storage-type temperature adjustment module are respectively electrically connected to a preset external power supply, and the self-generating module, the electric energy storage module, and the storage-type temperature adjustment module are respectively communicatively connected to the processing control module; The storage-type temperature adjustment module includes a cold storage module, a heat storage module, a temperature circulation pipeline, and a thermoelectric power generation module. The cold storage module and the heat storage module are thermally connected to the temperature circulation pipeline and the thermoelectric power generation module, respectively. The temperature circulation pipeline is provided in the green building, and the thermoelectric power generation module is electrically connected to the green building. The energy-saving and environmentally friendly green building energy-saving control system further includes an energy-saving strategy, including the following steps: E1, obtaining, by the processing control module, recent electricity consumption data of the green building and recent power generation data of the self-generation module of the green building according to a preset data reference period; E2, calculating the corresponding recent average power consumption and recent average power generation according to the recent power consumption data and the recent power generation data through the processing control module; E3, obtaining the daily weather forecast data corresponding to the green building through the processing control module; E4, estimating the estimated power consumption data and the estimated power generation data for the green building during the day period according to the recent average power consumption, the recent average power generation and the weather forecast data for the day through the processing control module; E5, calculating the corresponding daily estimated power consumption and daily estimated power consumption respectively according to the daily estimated power consumption data and the daily estimated power generation data by the processing control module; E6, calculating, by the processing control module, a corresponding difference in the estimated power consumption for the day based on the estimated power consumption for the day and the estimated power consumption for the day; E7, energy-saving adjustment is performed on the green building through the self-generation module, the energy storage module and the storage temperature adjustment module according to the difference of the estimated power generation amount on the day, the estimated power consumption data of the time period on the day and the estimated power generation data of the time period on the day.

[0021] By adopting the above technical solution, the energy-saving and environmentally friendly green building energy-saving control system can realize energy supply and demand forecasting and data-based scheduling by integrating historical electricity consumption data, historical power generation data and weather forecast information, and can perform forward-looking energy-saving adjustments based on the estimated difference between electricity consumption and power generation, which not only improves the energy utilization efficiency of the building, but also gives play to the role of renewable energy, achieving a win-win situation of economic and environmental benefits.

[0022] In summary, this application includes at least one of the following beneficial technical effects: 1. By integrating historical electricity consumption data, historical power generation data and weather forecast information, energy supply and demand forecasting and data-based scheduling can be achieved. Proactive energy-saving adjustments can be made based on the difference between estimated electricity consumption and power generation. This not only improves the energy efficiency of buildings, but also gives full play to the role of renewable energy, achieving a win-win situation in economic and environmental benefits.

[0023] 2. Refined forecasts can be made by dividing the entire day into multiple time periods. By organically combining meteorological data with recent electricity consumption and power generation data to form comprehensive data, and using pre-trained machine learning models to predict electricity consumption and power generation in each time period, not only the time resolution of the forecast is improved, but also the energy supply and demand fluctuation characteristics caused by changes in meteorological conditions in different time periods can be captured, providing more accurate and reliable data support for subsequent dynamic energy-saving regulation, making building energy-saving regulation more intelligent and refined.

[0024] 3. By giving priority to self-generated power and energy storage systems during peak hours, dependence on the external power grid and peak electricity price costs can be reduced. During off-peak hours, the operating mode can be flexibly adjusted according to the energy surplus. When the energy gap is large, the energy storage system is charged first. When energy is relatively sufficient, off-peak electricity prices are used to store heat and cool. This not only achieves energy peak shaving and valley filling and economic optimization, but also effectively balances the building's heating and cooling needs throughout the day through storage-type temperature regulation, further achieving energy conservation and environmental protection. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 It is a process schematic diagram of an energy-saving and environmentally friendly green building energy-saving control method of the present invention.

[0026] Figure 2 It is a schematic diagram of the principle of an energy-saving and environmentally friendly green building energy-saving control system of the present invention. DETAILED DESCRIPTION

[0027] In order to make the purpose, technical solutions and advantages of this application more clear, the following further describes this application in detail with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain this application and are not intended to limit this application.

[0028] The embodiments of the present application are described in further detail below with reference to the accompanying drawings.

[0029] refer to Figure 1 The present invention provides an energy-saving and environmentally friendly green building energy-saving control method for energy-saving control of green buildings, comprising the following steps: A1, obtaining the recent electricity consumption data of a preset green building and the recent power generation data of the self-generating module 10 of the green building according to a preset data reference period; The data reference period is a pre-set data collection period, which is used to determine the data collection scope. For example, the data reference period can be set to 1 month; The recent electricity consumption data refers to the electricity consumption data of the green building itself within the data reference period; The recent power generation data is the power generation data of the self-generating module 10 of the green building within a reference period. The self-generating module 10 can be composed of various renewable energy power generation devices, such as photovoltaic power generation, wind power generation, geothermal power generation, etc.

[0030] A2, calculates the corresponding recent average electricity consumption and recent average power generation based on the recent electricity consumption data and recent power generation data; The recent average power consumption is the average of the recent power consumption data, which can be the arithmetic mean or the weighted average based on the collection time; The recent average power generation is the average value of recent power generation data, which can be an arithmetic mean or a weighted average based on the collection time.

[0031] A3, obtaining the daily weather forecast data corresponding to the green building; The weather forecast data for the day is the forecast data of the weather for the day at the location of the green building. The data can be obtained from the corresponding meteorological department and may include various meteorological factors such as temperature data, humidity data, wind speed data, sunshine duration, rainfall, etc.

[0032] A4, estimating the estimated electricity consumption data and estimated power generation data for the green building for the current time period based on the recent average electricity consumption, recent average power generation, and the current day's weather forecast data; The estimated electricity consumption data for each period of the day is a data set of the green building's estimated possible electricity consumption for each period of the day; The estimated power generation data for each period of the day is a data set of the green building's estimated possible power generation for each period of the day; The estimated electricity consumption data and the estimated power generation data for the same period of the day can be used to generate corresponding prediction models through model training based on historical power generation data, historical electricity consumption data and historical meteorological data, or to fit corresponding prediction algorithms based on historical data.

[0033] A5, calculating the corresponding daily estimated electricity consumption and daily estimated power generation based on the daily estimated electricity consumption data and the daily estimated power generation data; The estimated electricity consumption for the day is the sum of the estimated electricity consumption for each period in the estimated electricity consumption data for that day, that is, the total electricity that the green building may need to consume throughout the day; The estimated power generation for the day is the sum of the estimated power generation for each time period in the estimated power generation data for the day, that is, the total power that may be generated by the self-generation module of the green building throughout the day.

[0034] A6, calculates the difference between the estimated power consumption and power generation for the day based on the estimated power consumption and power generation for the day; The difference between the estimated power consumption for the day is the difference between the estimated power consumption for the day and the estimated power generation for the day, that is, the power generation gap value, or the power generation surplus value. If the difference between the estimated power consumption for the day is greater than 0, there is a power generation gap; if the difference between the estimated power consumption for the day is less than 0, there is a power generation surplus.

[0035] A7, adjusting energy conservation for the green building based on the difference in estimated power consumption for the day, the estimated power consumption data for the time period of the day, and the estimated power generation data for the time period of the day; Energy-saving adjustments are made to the green building based on the difference between the electricity consumption and power generation in each time period of the day and the estimated daily power consumption and power generation for the entire day. For example, by adjusting various building facilities such as cooling, heating, and lighting, electricity consumption during peak hours is reduced to balance electricity consumption and power generation during peak hours, thereby reducing peak electricity charges. Alternatively, non-essential electricity consumption during peak hours is moved to off-peak hours for consumption, thereby achieving the effect of peak shaving and valley filling.

[0036] Through the above steps, the energy-saving and environmentally friendly green building energy-saving control method can realize energy supply and demand forecasting and data-based scheduling by integrating historical electricity consumption data, historical power generation data and weather forecast information, and can perform forward-looking energy-saving adjustments based on the estimated difference between electricity consumption and power generation, thereby improving the energy utilization efficiency of the building and giving play to the role of renewable energy, achieving a win-win situation in economic and environmental benefits.

[0037] Furthermore, the step A4 includes the following steps: A401, determining the corresponding weather forecast data for the current time period based on the preset time period sequence and the current time period weather forecast data; The time period sequence is a pre-set time period segmentation sequence. For example, the whole day is divided into 24 time periods, each of which is 1 hour long, to obtain 24 time periods and combine them in sequence to generate the corresponding time period sequence; The weather forecast data for a given time period is a sequential collection of weather forecast data for each time period determined in the weather forecast data for the given day according to the time period sequence.

[0038] A402, combining the meteorological forecast data for each time period in the meteorological forecast data for the current time period and the recent average electricity consumption to generate comprehensive electricity consumption data for each corresponding time period; The time period weather forecast data is the weather forecast data corresponding to each time period in the weather forecast data for the day; The comprehensive electricity consumption data for a period is a combination of recent average electricity consumption and period weather forecast data.

[0039] A403 generates comprehensive power generation data for each corresponding time period based on the meteorological forecast data for each time period and the recent average power generation in the meteorological forecast data for the current time period; The comprehensive power generation data for a period is a combination of recent average power generation and period meteorological forecast data.

[0040] A404, inputting the pre-trained time period power consumption prediction model based on the comprehensive power consumption data of each time period to generate the estimated power consumption for each corresponding time period; The period electricity consumption prediction model is a pre-trained prediction model that is used to generate the corresponding period estimated electricity consumption based on the period meteorological forecast data and the recent average electricity consumption forecast in the comprehensive electricity consumption data of each period.

[0041] A405 generates the corresponding estimated power consumption data for the current day period based on the time period sequence and all the time period estimated power consumption comparison combinations; The estimated electricity consumption data for a given period of the day is a data collection generated by sequentially combining the estimated electricity consumption corresponding to each period in the period sequence.

[0042] A406, inputting the pre-trained time period power generation prediction model based on the comprehensive power generation data of each time period to generate the estimated power generation for each corresponding time period; The time period power generation forecast model is a pre-trained forecast model that is used to generate the corresponding time period estimated power generation based on the time period meteorological forecast data and the recent average power generation forecast in the comprehensive power generation data of each time period.

[0043] A407 generates the corresponding estimated power generation data for the current day period based on the time period sequence and all the time period estimated power generation comparison combinations; The estimated power generation data for a given day is a data set generated by sequentially combining the estimated power generation corresponding to each time period in the time period sequence.

[0044] Through the above steps, the energy-saving and environmentally friendly green building energy-saving control method can divide the whole day into multiple time periods for refined prediction, organically combine meteorological data with recent electricity consumption and power generation data to form comprehensive data, and use pre-trained machine learning models to predict electricity consumption and power generation in each time period respectively. This not only improves the time resolution of the prediction, but also captures the energy supply and demand fluctuation characteristics caused by changes in meteorological conditions in different time periods, providing more accurate and reliable data support for subsequent dynamic energy-saving regulation, making building energy-saving regulation more intelligent and refined.

[0045] Furthermore, the step A7 includes the following steps: A701 calculates and generates the corresponding daily time period estimated power consumption and power generation difference data based on the daily time period estimated power consumption data and the daily time period estimated power generation data; The estimated power consumption difference data for a given period of the day is a data set of the difference between the estimated power consumption data for a given period of the day and the estimated power generation data for a given period of the day.

[0046] A702, determining the corresponding peak period estimated power generation difference data based on the preset daily peak period estimated power generation difference data; The peak period of the day is the pre-set peak electricity consumption period of the day, which can be set according to the peak electricity price period given by the external power supply department. Usually, the power supply price of the external power supply department is higher during the peak period. The peak period estimated power generation difference data is a data set of estimated power generation difference values ​​corresponding to the peak period of the day in the estimated power generation difference data for the day period.

[0047] A703, determining the corresponding off-peak period estimated power generation difference data based on the preset off-peak period of the day and the estimated power generation difference data of the day; The off-peak period of the day is the pre-set off-peak period of electricity consumption on the day, which can be set according to the off-peak electricity price period given by the external power supply department. Usually, the power supply price of the external power supply department is lower during the off-peak period. The off-peak period estimated power generation difference data is a data set of the estimated power generation difference corresponding to the off-peak period of the day in the estimated power generation difference data of the day.

[0048] A704, supplying power to the green building during the peak period of the day according to the estimated power generation difference data during the peak period through the self-generating module 10 and the preset power storage module 20; The electric energy storage module 20 is an electric energy storage device pre-installed in the green building, used to store and release electric energy; During peak hours, the self-generating module 10 supplies power to the green building. When there is a power generation gap, the energy storage module 20 supplies power to the green building, thereby reducing the demand for electricity from the external power source, lowering peak electricity charges, and reducing the grid load of the external power source. When there is a power supply surplus, the self-generating module 10 can charge the energy storage module 20, or the electricity can be sold to the external power source to obtain revenue from the peak electricity price. Furthermore, by using the energy storage device only during peak hours and only compensating for power generation gaps, the number of charge and discharge times of the energy storage device can be greatly reduced compared to using the energy storage device throughout daily hours, thereby significantly extending the battery life.

[0049] A705, if the difference in the estimated power generation amount for the day is greater than the preset operating condition conversion threshold, charging the energy storage module 20 during the off-peak period of the day according to the difference in the estimated power generation amount during the off-peak period through the self-generating module 10 and a preset external power supply; The operating condition conversion threshold is a pre-set reference value used to determine the size of the difference in the estimated power generation on that day, and further determine whether the power generation of the green building on that day is in short supply or surplus. The operating condition conversion threshold can be set to 0 or a negative number close to 0. When the difference between the estimated power generation amount on the day is greater than the operating condition conversion threshold, it means that the power consumption on the day is significantly greater than the power generation, that is, there is a certain power generation gap. After the power generation gap is made up by the power storage module 20 during the peak period, it is necessary to charge the power storage module 20 through the self-generating module 10 or the external power supply during the off-peak period to cope with the power generation gap during the peak period of the next day. Charging the power storage module 20 through the external power supply during the off-peak period can reduce electricity costs on the one hand and balance the grid load of the external power supply on the other.

[0050] A706, if the difference in the estimated power generation amount for the day is less than or equal to the operating condition conversion threshold, heat and cold are stored in the preset storage temperature adjustment module 30 via the self-generating module 10 and the external power supply during the off-peak period of the day according to the difference in the estimated power generation amount for the off-peak period; When the difference between the estimated power consumption on the day is less than or equal to the operating condition conversion threshold, it means that the power generation on the day is significantly greater than the power consumption, that is, there is a certain power generation surplus. In this case, the power in the power storage module 20 is not used or is rarely used. The excess power generation can be sold back to the power grid of the external power source during peak hours to obtain the benefits of peak electricity prices. On the other hand, the excess power can be stored as cold and heat during off-peak hours for adjusting the indoor temperature of the green building the next day, or for daily use. For example, water can be used as a medium for cold and heat storage. While it can be used for room temperature regulation, it can also be used for daily use, such as using heat storage to heat washing water and using cold storage to refrigerate food.

[0051] Through the above steps, the energy-saving and environmentally friendly green building energy-saving control method can reduce dependence on the external power grid and peak electricity price costs by giving priority to self-generation and energy storage systems during peak hours; during off-peak hours, the operation mode is flexibly adjusted according to the energy surplus situation. When the energy gap is large, the energy storage system is charged first, and when energy is relatively sufficient, off-peak electricity prices are used to store heat and cool. This not only achieves energy peak shaving and valley filling and economic optimization, but also effectively balances the building's heating and cooling needs throughout the day through storage-type temperature regulation, further achieving energy saving and environmental protection.

[0052] Furthermore, the energy-saving and environmentally friendly green building energy-saving control method further includes the following steps for room temperature adjustment: B1, determining the corresponding temperature forecast data for the current time period in the current day's weather forecast data according to a preset time period sequence; The temperature forecast data for each time period of the day is a sequential collection of temperature forecast data for each time period determined in the weather forecast data for the day according to the time period sequence.

[0053] B2, calculating and determining the corresponding predicted temperature difference data for the time period of the day based on the temperature forecast data for the time period of the day and the preset comfortable temperature; The comfort temperature is a pre-set reference temperature that can be set by yourself or according to the season. For example, in winter, the comfort temperature can be set to 18°C, or in summer, the comfort temperature can be set to 25°C. The daily time period predicted temperature difference data is a data set of the difference between the predicted temperature and the comfortable temperature for each time period in the daily time period temperature prediction data.

[0054] B3, determining the predicted temperature difference for each corresponding time period based on the temperature forecast data for that time period; The time period predicted temperature difference is the difference between the predicted temperature and the comfortable temperature corresponding to each time period in the time period predicted temperature difference data of the day.

[0055] B4, if the predicted temperature difference during a time period is greater than or equal to the preset cooling threshold, the corresponding time period is defined as a cooling period; The cooling threshold is a pre-set reference value used to determine whether the green building needs to be cooled to lower the indoor temperature; The cooling period is the period when cooling is determined to be necessary.

[0056] B5, generating cooling period data according to all cooling period combinations, supplying cooling to the green building during corresponding periods according to the cooling period data, and recording corresponding cooling consumption data for the day; The cooling period data is the collection of data for each cooling period; The daily cooling capacity consumption data is the cooling capacity consumed for cooling the green building throughout the day, which can be estimated based on the temperature of the refrigerant at the beginning and end of the day, combined with its thermodynamic properties and the subsequent electrical energy consumed for cooling.

[0057] B6, if the predicted temperature difference during a time period is less than or equal to the preset heating threshold, the corresponding time period is defined as the heating time period; The heating threshold is a preset reference value used to determine whether the green building needs to be heated to increase the indoor temperature; The heating period is the period when heating is determined to be necessary.

[0058] B7, generating heating period data according to all heating period combinations, supplying heat to the green building during corresponding periods according to the heating period data, and recording corresponding daily heat storage consumption data; The heating period data is a collection of data from each heating period; The daily heat storage consumption data is the heat consumption for heating the green building throughout the day, which can be estimated based on the temperature of the heat medium at the beginning and end of the day, combined with its thermodynamic properties and the subsequent electrical energy consumed for heat storage.

[0059] Through the above steps, the energy-saving and environmentally friendly green building energy-saving control method can realize a predictive temperature control strategy based on the temperature forecast data and comfortable temperature of the day period. By identifying the cooling and heating demand periods in advance, it can make full use of the cold and heat energy stored in the off-peak period, avoiding the start-up of high-energy-consuming air-conditioning equipment during peak electricity consumption periods. At the same time, by recording the cold storage heat consumption data, it provides data support for the continuous optimization of the system, and realizes the efficient combination of temperature regulation and energy management as a whole, which not only ensures the comfort of the indoor environment, but also minimizes the energy consumption cost and carbon emissions of the building.

[0060] Furthermore, the energy-saving and environmentally friendly green building energy-saving control method further comprises the following steps: C1, obtain the current date, and determine the corresponding current seasonal time window in the preset seasonal cycle time window data based on the current date; The current date is today's date; Seasonal cycle time window data is a pre-set time window based on seasonal divisions; The current season time window is the time window of the season corresponding to the current date.

[0061] C2, according to the current seasonal time window, obtain the corresponding historical seasonal time window daily cooling consumption data and historical seasonal time window daily heating consumption data; The historical seasonal time window daily cooling consumption data is the corresponding portion of the historical daily cooling consumption data collected in the past according to the current seasonal time window; The historical seasonal time window daily heat storage consumption data is a corresponding portion of data determined from the historical daily heat storage consumption data collected in the past according to the current seasonal time window.

[0062] C3, based on the historical seasonal window daily cooling consumption data and the estimated cooling demand for the next day corresponding to the current date; The estimated cooling demand for the next day is the amount of stored cooling that may need to be consumed the next day; The next day's date is determined based on the current date, and combined with the historical seasonal window daily cooling consumption data, the required cooling demand can be roughly inferred. The cooling demand can also be further estimated in combination with the next day's meteorological data.

[0063] C4 calculates the corresponding refrigerant storage temperature based on the preset upper limit temperature of cold storage, refrigerant storage capacity, refrigerant solid specific heat capacity, refrigerant liquid specific heat capacity, refrigerant melting latent heat, and the estimated cooling demand for the next day; The upper limit temperature of cold storage is the maximum temperature of the cold storage refrigerant set in advance. When the cold storage refrigerant reaches the upper limit temperature, it can be considered that the cold storage refrigerant can no longer provide cooling, or the cooling efficiency is greatly reduced; Refrigerant reserves are the total mass of refrigerant used for cold storage; The solid-state specific heat capacity of the refrigerant is the specific heat capacity of the refrigerant in the solid state; The specific heat capacity of the refrigerant liquid is the specific heat capacity of the refrigerant in liquid state; The latent heat of fusion of refrigerant is the amount of heat that needs to be absorbed or released per kilogram of mass during the phase change between liquid and solid state of refrigerant; The refrigerant storage temperature is the target temperature to be reached by the refrigerant storage; For example, when using water as a refrigerant, the upper limit temperature of cold storage can be set to 10°C. When the cold storage temperature of the refrigerant is below 0°C, its higher latent heat of fusion can be fully utilized to increase the cold storage capacity during cold storage.

[0064] C5, based on the historical seasonal window daily heat storage consumption data and the current date, the estimated heating demand for the next day; The estimated heating demand for the next day is the amount of stored heat that may need to be consumed the next day.

[0065] C6 calculates the corresponding heat storage temperature of the heat medium based on the preset lower limit temperature of heat storage, heat medium storage capacity, heat medium liquid specific heat capacity, and the estimated heat demand for the next day; The lower limit temperature of heat storage is the preset minimum temperature of the heat storage medium. When the heat storage medium reaches the lower limit temperature, it can be considered that the heat storage medium can no longer provide heat, or the heating efficiency is greatly reduced. For example, if water is used as the heat medium, the lower limit temperature of heat storage can be set to 30°C. The heat medium reserve is the total mass of the heat medium used for heat storage; The liquid specific heat capacity of the heat medium is the specific heat capacity of the heat medium in the liquid state; The heat medium storage temperature is the target temperature to be reached by the heat medium.

[0066] C7, preferentially storing cold in the storage temperature adjustment module 30 according to the cold storage temperature of the refrigerant; During off-peak hours, cold storage can be prioritized because a certain amount of heat will be transferred during cold storage. Part of the transferred heat can be further used to preheat the heat medium used for heat storage, thereby reducing the energy consumption of heat medium heat storage. Giving priority to cold storage can avoid a shortage of cold storage during the peak hours of the next day, because the demand for cooling is usually greater than the demand for heating, especially in the hot summer climate.

[0067] C8, and after the cold storage is completed, the storage temperature adjustment module 30 is stored according to the heat storage temperature of the heat medium; When the refrigerant is storing cold, the heat medium can be preheated by partially transferring heat to reduce the energy consumption of heat storage.

[0068] Through the above steps, the energy-saving and environmentally friendly green building energy-saving control method can estimate the next day's heating and cooling demand according to the temperature control demand characteristics of different seasons, calculate the cooling and heating storage temperature by considering the physical properties of the cooling and heating media, ensure that the stored cooling and heating amount can meet the next day's demand while avoiding energy waste caused by excessive energy storage, and make full use of the low-peak electricity and lower ambient temperature at night to improve the cooling efficiency, thereby realizing the overall cross-day energy balance and seasonal intelligent scheduling, and improving the energy utilization efficiency of green buildings.

[0069] Furthermore, the energy-saving and environmentally friendly green building energy-saving control method further comprises the following steps: D1, obtaining the next day's weather forecast data corresponding to the green building; The next day's weather forecast data is the weather forecast data for the next day, and the data can be obtained from the weather forecast department.

[0070] D2, estimating the next-day estimated electricity consumption data and next-day estimated power generation data corresponding to the green building based on the recent average electricity consumption, recent average power generation and next-day weather forecast data; The estimated electricity consumption data for the next day is the possible electricity consumption of the green building in each period of the next day; The estimated power generation data for the next day is the possible power generation of the green building in each period on the next day.

[0071] D3, calculating the corresponding next day estimated electricity consumption and next day estimated power generation according to the next day estimated electricity consumption data and the next day estimated power generation data; The estimated electricity consumption for the next day is the sum of the estimated electricity consumption for each period in the estimated electricity consumption data for the next day, that is, the total electricity that the green building may need to consume throughout the next day; The estimated power generation for the next day is the sum of the estimated power generation for each time period in the estimated power generation data for the next day, that is, the total power that may be generated by the self-generation module of the green building throughout the next day.

[0072] D4, calculating the corresponding difference between the next day's estimated power consumption and the next day's estimated power generation; The difference between the estimated power consumption for the next day and the estimated power generation for the next day is the difference between the estimated power consumption for the next day and the estimated power generation for the next day.

[0073] D5: If the difference between the next day's estimated power generation difference and the current day's estimated power generation difference is greater than a preset excess storage threshold, then the corresponding next day's estimated cooling demand update value is calculated by multiplying the preset excess cooling storage coefficient by the next day's estimated cooling demand, and the corresponding next day's estimated heating demand update value is calculated by multiplying the preset excess heating storage coefficient by the next day's estimated heating demand; The over-storage threshold is a pre-set reference value used to determine whether the difference between the next day's estimated power generation and the current day's estimated power generation is too large; The excess cooling coefficient is a pre-set adjustment coefficient that can be set according to demand or actual working conditions. For example, the excess cooling coefficient can be set to 1.2; The excess heat storage coefficient is a pre-set adjustment coefficient that can be set according to demand or actual working conditions. For example, the excess heat storage coefficient can be set to 1.2; The updated value of the next day's estimated cooling demand is the new next day's estimated cooling demand; The updated value of the next day's estimated heating demand is the new next day's estimated heating demand; When the difference between the estimated power generation amount for the next day and the estimated power generation amount for the current day is greater than the over-storage threshold, that is, the power generation gap for the next day exceeds the power generation gap for the current day by a value greater than the over-storage threshold, it indicates that the electric energy storage module 20 may need to bear a higher power supply burden on the next day than on the current day. In this case, additional heat storage and cold storage are performed on the current day to reduce the energy consumption of heat storage and cold storage on the next day, so that the electric energy storage module 20 can be charged more efficiently from an external power source during the off-peak period to ensure that the electric energy storage module 20 can maintain sufficient power to cope with the peak period on the third day.

[0074] D6, updating the next day's estimated cooling demand in step C4 according to the next day's estimated cooling demand update value; The estimated cooling demand for the next day in step C4 is updated to calculate a new refrigerant cold storage temperature.

[0075] D7, updating the next day's estimated heating demand in step C6 according to the next day's estimated heating demand update value; The estimated heating demand for the next day in step C6 is updated to calculate a new heat storage temperature of the heat medium.

[0076] Through the above steps, the energy-saving and environmentally friendly green building energy-saving control method can identify in advance the risk of further power generation shortage on the next day by comparing the difference between the power generation on the current day and the next day; when it is predicted that the power generation gap on the next day will increase significantly, the excess energy storage strategy is triggered to ensure that the temperature control needs of the building can still be met in the event of energy shortage by increasing the demand for cold and heat storage, and reducing the cold and heat storage load on the next day to ensure that the energy storage module 20 can obtain sufficient electricity for charging.

[0077] Furthermore, the energy-saving and environmentally friendly green building energy-saving control method further comprises the following steps: D8, during the preset peak period of the next day, the temperature difference between the refrigerant storage temperature and the heat storage temperature of the heat medium is consumed by the thermoelectric power generation module 34 of the storage temperature adjustment module 30 to supply power to the green building; The peak period of the next day is the corresponding peak period of the next day; Thermoelectric power generation module 34 is a module that generates electricity through temperature difference; Due to the storage of cold and heat, the temperature difference between the refrigerant and the heat medium reaches a certain level. After the excess cold and heat storage of the previous day, while ensuring the cooling and heating needs of the next day, the excess temperature difference can be consumed to generate electricity to make up for part of the electricity demand during the peak period, thereby reducing the power supply burden of the energy storage module 20.

[0078] Through the above steps, the energy-saving and environmentally friendly green building energy-saving control method can generate electricity by utilizing the temperature difference between the refrigerant and the heat medium in the storage temperature adjustment module 30, so that the energy storage system originally used only for temperature adjustment can be further used for temperature control and power generation. During the peak electricity consumption period of the next day, part of the temperature difference between the refrigerant and the heat medium is directly converted into electricity for building use through temperature difference power generation, which not only further reduces the dependence on the external power grid, but also realizes the cascade utilization of energy, thereby improving the energy utilization efficiency and economy of the entire green building.

[0079] refer to Figure 2 The present invention also provides an energy-saving and environmentally friendly green building energy-saving control system, comprising: Self-generating module 10; Power storage module 20; Storage temperature control module 30; Processing control module 40; The self-generating module 10, the electric energy storage module 20, and the storage-type temperature adjustment module 30 are respectively electrically connected to a preset green building, the electric energy storage module 20 and the storage-type temperature adjustment module 30 are respectively electrically connected to a preset external power source, and the self-generating module 10, the electric energy storage module 20, and the storage-type temperature adjustment module 30 are respectively communicatively connected to the processing control module 40; The storage-type temperature adjustment module 30 includes a cold storage module 31, a heat storage module 32, a temperature circulation pipeline 33, and a thermoelectric power generation module 34. The cold storage module 31 and the heat storage module 32 are thermally connected to the temperature circulation pipeline 33 and the thermoelectric power generation module 34, respectively. The temperature circulation pipeline 33 is provided in the green building, and the thermoelectric power generation module 34 is electrically connected to the green building. The self-generating module 10 is mainly used to generate electricity through renewable energy power generation equipment, such as photovoltaic power generation equipment, wind power generation equipment, and geothermal power generation equipment.

[0080] The electric energy storage module 20 is mainly used to store electric energy.

[0081] The storage temperature adjustment module 30 is mainly used for storing cold and heat so as to be ready for cooling and heating when needed.

[0082] The cold storage module 31 is mainly used for storing cold by consuming electrical energy.

[0083] The heat storage module 32 is mainly used for storing heat by consuming electrical energy.

[0084] The temperature circulation pipeline 33 is mainly used to be distributed in the green building. The cold storage module 31 and the heat storage module 32 can be connected to the temperature circulation pipeline 33 to supply cooling or heating to the green building according to demand.

[0085] The temperature difference power generation module 34 is mainly used to generate electricity according to the temperature difference between the refrigerant in the cold storage module 31 and the heat medium in the heat storage module 32 .

[0086] The processing control module 40 is mainly used for data processing and controlling each module.

[0087] The energy-saving and environmentally friendly green building energy-saving control system further includes an energy-saving strategy, including the following steps: E1, obtaining, by the processing control module 40, the recent electricity consumption data of the green building and the recent power generation data of the self-generation module 10 of the green building according to a preset data reference period; E2, calculating the corresponding recent average power consumption and recent average power generation according to the recent power consumption data and the recent power generation data through the processing control module 40; E3, obtaining the daily weather forecast data corresponding to the green building through the processing control module 40; E4, estimating the estimated power consumption data and the estimated power generation data for the green building during the day period according to the recent average power consumption, the recent average power generation and the weather forecast data for the day through the processing control module 40; E5, calculating the corresponding daily estimated power consumption and daily estimated power generation according to the daily estimated power consumption data and the daily estimated power generation data by the processing control module 40; E6, calculating the corresponding difference between the estimated power consumption and power generation on the day according to the estimated power consumption and power generation on the day by the processing control module 40; E7, energy-saving adjustment is performed on the green building through the self-generation module 10, the energy storage module 20 and the storage temperature adjustment module 30 according to the difference of the estimated power generation amount on the day, the estimated power consumption data of the day time period and the estimated power generation data of the day time period.

[0088] Through the above technical solutions, the energy-saving and environmentally friendly green building energy-saving control system can realize energy supply and demand forecasting and data-based scheduling by integrating historical electricity consumption data, historical power generation data and weather forecast information, and can perform forward-looking energy-saving adjustments based on the estimated difference between electricity consumption and power generation, which not only improves the energy utilization efficiency of the building, but also gives play to the role of renewable energy, achieving a win-win situation of economic and environmental benefits.

[0089] The above are all preferred embodiments of the present application and are not intended to limit the scope of protection of this application. Unless otherwise specified, any feature disclosed in this specification (including the abstract and drawings) may be replaced by other equivalent or similar features. In other words, unless otherwise specified, each feature is merely an example of a series of equivalent or similar features.

Claims

1. An energy-saving and environmentally friendly green building energy-saving control method, characterized in that: The following steps are involved: A1, obtaining recent electricity consumption data of a preset green building and recent power generation data of a self-generating module of the green building according to a preset data reference period; A2, calculates the corresponding recent average electricity consumption and recent average power generation based on the recent electricity consumption data and recent power generation data; A3, obtaining the daily weather forecast data corresponding to the green building; A4, estimating the estimated electricity consumption data and estimated power generation data for the green building for the current time period based on the recent average electricity consumption, recent average power generation, and the current day's weather forecast data; A5, calculating the corresponding daily estimated electricity consumption and daily estimated power generation based on the daily estimated electricity consumption data and the daily estimated power generation data; A6, calculates the difference between the estimated power consumption and power generation for the day based on the estimated power consumption and power generation for the day; A7, performing energy-saving adjustment on the green building according to the difference of the estimated power generation amount on the day, the estimated power consumption data for the time period on the day, and the estimated power generation data for the time period on the day.

2. The energy-saving and environmentally friendly green building energy-saving control method according to claim 1 is characterized in that: Step A4 includes the following steps: A401, determining the corresponding weather forecast data for the current day period based on the preset time period sequence and the current day weather forecast data; A402, combining the meteorological forecast data for each time period in the meteorological forecast data for the current time period and the recent average electricity consumption to generate comprehensive electricity consumption data for each corresponding time period; A403 generates comprehensive power generation data for each corresponding time period based on the meteorological forecast data for each time period and the recent average power generation in the meteorological forecast data for the current time period; A404, inputting the pre-trained time period power consumption prediction model based on the comprehensive power consumption data of each time period to generate the estimated power consumption for each corresponding time period; A405, generating the corresponding daily time period estimated power consumption data based on the time period sequence and all time period estimated power consumption comparison combinations; A406, inputting the pre-trained time period power generation prediction model based on the comprehensive power generation data of each time period to generate the estimated power generation for each corresponding time period; A407, generating the corresponding estimated power generation data for the current day period based on the period sequence and all the period estimated power generation comparison combinations.

3. The energy-saving and environmentally friendly green building energy-saving control method according to claim 1 is characterized in that: Step A7 includes the following steps: A701 calculates and generates the corresponding daily time period estimated power consumption and power generation difference data based on the daily time period estimated power consumption data and the daily time period estimated power generation data; A702, determining the corresponding peak period estimated power generation difference data based on the preset daily peak period estimated power generation difference data; A703, determining the corresponding off-peak period estimated power generation difference data based on the preset off-peak period of the day and the estimated power generation difference data of the day; A704, supplying power to the green building via the self-generating module and the preset power storage module during the peak period of the day according to the estimated power generation difference data during the peak period; A705, if the difference in the estimated power generation amount for the day is greater than the preset operating condition conversion threshold, charging the energy storage module via the self-generation module and a preset external power supply during the off-peak period of the day according to the difference in the estimated power generation amount for the off-peak period; A706, if the difference in the estimated power generation for the day is less than or equal to the operating condition conversion threshold, then during the off-peak period of the day, heat and cold are stored in the preset storage temperature control module through the self-generation module and the external power supply according to the data of the difference in the estimated power generation for the off-peak period.

4. The energy-saving and environmentally friendly green building energy-saving control method according to claim 3 is characterized in that: The method further comprises the following steps for room temperature regulation: B1, determining the corresponding temperature forecast data for the current time period in the current day's weather forecast data according to a preset time period sequence; B2, calculating and determining the corresponding predicted temperature difference data for the time period of the day based on the temperature forecast data for the time period of the day and the preset comfortable temperature; B3, determining the predicted temperature difference for each corresponding time period based on the predicted temperature difference data for the current time period; B4, if the predicted temperature difference during a time period is greater than or equal to the preset cooling threshold, the corresponding time period is defined as a cooling period; B5, generating cooling period data according to all cooling period combinations, supplying cooling to the green building during corresponding periods according to the cooling period data, and recording corresponding cooling capacity consumption data for the day; B6, if the predicted temperature difference during a time period is less than or equal to the preset heating threshold, the corresponding time period is defined as the heating time period; B7, generating heating period data according to all heating period combinations, supplying heat to the green building in corresponding periods according to the heating period data, and recording corresponding daily heat storage consumption data.

5. The energy-saving and environmentally friendly green building energy-saving control method according to claim 4 is characterized in that: Further comprising the steps of: C1, obtain the current date, and determine the corresponding current seasonal time window in the preset seasonal cycle time window data based on the current date; C2, according to the current seasonal time window, obtain the corresponding historical seasonal time window daily cooling consumption data and historical seasonal time window daily heating consumption data; C3, based on the historical seasonal window daily cooling consumption data and the estimated cooling demand for the next day corresponding to the current date; C4 calculates the corresponding refrigerant storage temperature based on the preset upper limit temperature of cold storage, refrigerant storage capacity, refrigerant solid specific heat capacity, refrigerant liquid specific heat capacity, refrigerant melting latent heat, and the estimated cooling demand for the next day; C5, based on the historical seasonal window daily heat storage consumption data and the current date estimate of the next day's corresponding heating demand; C6 calculates the corresponding heat storage temperature of the heat medium based on the preset lower limit temperature of heat storage, heat medium storage capacity, heat medium liquid specific heat capacity, and the estimated heat demand for the next day; C7, preferentially storing cold in the storage temperature adjustment module according to the cold storage temperature of the refrigerant; C8, and after the cold storage is completed, the storage temperature adjustment module is heated according to the heat storage temperature of the heat medium.

6. The energy-saving and environmentally friendly green building energy-saving control method according to claim 5 is characterized in that: Further comprising the steps of: D1, obtaining the next day's weather forecast data corresponding to the green building; D2, estimating the next-day estimated electricity consumption data and next-day estimated power generation data corresponding to the green building based on the recent average electricity consumption, recent average power generation and next-day weather forecast data; D3, calculating the corresponding next day estimated electricity consumption and next day estimated power generation according to the next day estimated electricity consumption data and the next day estimated power generation data; D4, calculating the corresponding difference between the next day's estimated power consumption and the next day's estimated power generation; D5: If the difference between the next day's estimated power generation difference and the current day's estimated power generation difference is greater than a preset excess storage threshold, then the corresponding next day's estimated cooling demand update value is calculated by multiplying the preset excess cooling storage coefficient by the next day's estimated cooling demand, and the corresponding next day's estimated heating demand update value is calculated by multiplying the preset excess heating storage coefficient by the next day's estimated heating demand; D6, updating the next day's estimated cooling demand in step C4 according to the next day's estimated cooling demand update value; D7, updating the next day's estimated heating demand in step C6 according to the next day's estimated heating demand update value.

7. The energy-saving and environmentally friendly green building energy-saving control method according to claim 6 is characterized in that: Further comprising the steps of: D8, during the preset peak period of the next day, the temperature difference between the cold storage temperature of the refrigerant and the heat storage temperature of the heat medium is consumed by the temperature difference power generation module of the storage temperature adjustment module to supply power to the green building.

8. An energy-saving and environmentally friendly green building energy-saving control system, characterized in that: include: Self-generating module; Electric energy storage module; Storage temperature control module; Processing control module; The self-generating module, the electric energy storage module, and the storage-type temperature adjustment module are respectively electrically connected to a preset green building, the electric energy storage module and the storage-type temperature adjustment module are respectively electrically connected to a preset external power supply, and the self-generating module, the electric energy storage module, and the storage-type temperature adjustment module are respectively communicatively connected to the processing control module; The storage-type temperature adjustment module includes a cold storage module, a heat storage module, a temperature circulation pipeline, and a thermoelectric power generation module. The cold storage module and the heat storage module are thermally connected to the temperature circulation pipeline and the thermoelectric power generation module, respectively. The temperature circulation pipeline is provided in the green building, and the thermoelectric power generation module is electrically connected to the green building. The energy-saving and environmentally friendly green building energy-saving control system further includes an energy-saving strategy, including the following steps: E1, obtaining, by the processing control module, recent electricity consumption data of the green building and recent power generation data of the self-generation module of the green building according to a preset data reference period; E2, calculating the corresponding recent average power consumption and recent average power generation according to the recent power consumption data and the recent power generation data through the processing control module; E3, obtaining the daily weather forecast data corresponding to the green building through the processing control module; E4, estimating the estimated power consumption data and the estimated power generation data for the green building during the day period according to the recent average power consumption, the recent average power generation and the weather forecast data for the day through the processing control module; E5, calculating the corresponding daily estimated power consumption and daily estimated power consumption respectively according to the daily estimated power consumption data and the daily estimated power generation data by the processing control module; E6, calculating, by the processing control module, a corresponding difference in the estimated power consumption for the day based on the estimated power consumption for the day and the estimated power consumption for the day; E7, energy-saving adjustment is performed on the green building through the self-generation module, the energy storage module and the storage temperature adjustment module according to the difference of the estimated power generation amount on the day, the estimated power consumption data of the time period on the day and the estimated power generation data of the time period on the day.

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