Intelligent building energy scheduling system and method under multi-scene weather

Abstract

The invention relates to the field of intelligent energy regulation and control, in particular to an intelligent building energy scheduling system and method under multi-scene weather, and the method comprises the steps: determining a target region through an energy analysis module, obtaining the total operation data of all equipment in the target region, dividing the total operation data into nonlinear data and linear data, and transmitting the nonlinear data and the linear data to a server; calculating the ratio of the nonlinear data to the linear data; the energy scheduling module calculates a scheduling distribution result according to the proportion of the nonlinear data and the linear data, and the energy supply module controls the solar energy, the wind energy, the geothermal energy or the electric energy to schedule and distribute energy supply of the target area according to the scheduling distribution result. It is ensured that the intelligent building energy scheduling system under the multi-scene weather can be suitable for energy scheduling strategies of building areas with linear problems and non-linear problems, the accuracy of the building energy scheduling system is improved, and the applicability of the building energy scheduling system is widened.

Description

Technical Field

[0001] The present application relates to the technical field of intelligent energy regulation, and in particular to an intelligent building energy scheduling system and method under multi-scenario weather conditions. Background Art

[0002] The building energy dispatch system is an intelligent system that integrates advanced monitoring, control, analysis and optimization technologies. It aims to achieve the optimal configuration and efficient use of energy resources through real-time monitoring and dispatching of various energy sources in the building (such as electricity, hot water, heating and cooling, etc.). The building energy dispatch system can automatically analyze energy data, identify energy waste, and make optimization suggestions to ensure efficient, safe and reliable operation of building energy.

[0003] The building energy dispatching system plays a vital role in today's buildings. It can effectively monitor and manage energy consumption in buildings. However, the building energy dispatching system in the prior art regards the building energy system as a linear programming problem, with the minimum cost as the objective function, and at the same time meets the energy balance and energy quality requirements of the system; then, by solving the linear equations, the optimal energy distribution and dispatching strategy is obtained. However, such an energy dispatching system is difficult to calculate the dispatching results for complex nonlinear problems, and the calculation is relatively difficult. In industrial parks, there may be a variety of energy conversion equipment, such as gas turbines, waste heat recovery devices, etc. The energy conversion efficiency of these equipment is affected by many factors, such as equipment status, operating environment, etc. The relationship between these factors and energy conversion efficiency is nonlinear, so the accuracy of using the energy dispatching system of the prior art to dispatch the energy of the industrial park is low, and the energy dispatching calculation process is very complicated.

[0004] To address these issues, there is an urgent need for a more intelligent smart building energy scheduling system and method under multiple weather scenarios, which can adapt to the energy scheduling strategies of building areas under various scenarios to improve the accuracy of the building energy scheduling system and broaden its applicability. Summary of the invention

[0005] In order to meet the energy scheduling strategy for building areas that can adapt to various scenarios, the present application provides an intelligent building energy scheduling system and method under multi-scenario weather conditions.

[0006] In the first aspect, the present application provides an intelligent building energy dispatching system under multi-scenario weather conditions, which adopts the following technical solutions:

[0007] An intelligent building energy dispatching system under multi-scenario weather conditions, comprising: an energy supply module, an energy dispatching module and an energy analysis module; wherein the energy dispatching module is connected to the energy analysis module, and the energy dispatching module is connected to the energy supply module;

[0008] An energy analysis module, configured to determine a target area, obtain total operation data of all devices in the target area, divide the total operation data into non-linear data and linear data, calculate a first ratio of the non-linear data to the total operation data, and calculate a second ratio of the linear data to the operation data;

[0009] An energy scheduling module, configured to, when the first ratio is greater than the second ratio, input the nonlinear data into a preset nonlinear scheduling model, and use the preset nonlinear scheduling model to analyze and calculate the nonlinear data to obtain a nonlinear scheduling allocation result; when the second ratio is greater than the first ratio, input the linear data into a preset linear scheduling model, and use the preset linear scheduling model to analyze and calculate the linear data to obtain a linear scheduling allocation result;

[0010] An energy supply module is used to control the scheduling and allocation of energy supply to the target area by using solar energy, wind energy, geothermal energy or electric energy according to the nonlinear scheduling and allocation result or the linear scheduling and allocation result.

[0011] Furthermore, the energy scheduling module includes: a second energy scheduling unit, the second energy scheduling unit is connected to the energy supply module;

[0012] The second energy scheduling unit is used to construct a preset nonlinear scheduling model. When the first ratio is greater than the second ratio, the nonlinear data is input into the preset nonlinear scheduling model, and the preset nonlinear scheduling model is used to analyze and calculate the nonlinear data to obtain a nonlinear scheduling allocation result.

[0013] Furthermore, the preset nonlinear scheduling model is:

[0014]

[0015] Among them, ξ1+ξ2+ξ n =1, ξ1 is the regression proportionality coefficient of the first device, ξ2 is the regression proportionality coefficient of the second device, ξ n is the regression proportional coefficient of the nth device; y1 is the energy data of the first device, y2 is the energy data of the second device, is the energy data of the nth device, and θ is the environmental regression deviation.

[0016] Further, the energy scheduling module includes: a first energy scheduling unit; the first energy scheduling unit is connected to the energy supply module;

[0017] The first energy scheduling unit is used to construct a linear scheduling model. When the second ratio is greater than the first ratio, the linear data is input into a preset linear scheduling model, and the preset linear scheduling model is used to analyze and calculate the linear data to obtain a linear scheduling allocation result.

[0018] Furthermore, the intelligent building energy dispatching system may further include: a weather sensing module, the weather sensing module being connected to the energy dispatching module;

[0019] The weather sensing module is used to obtain environmental information in the target area, wherein the environmental information is one of sunny, rainy, cloudy, or typhoon;

[0020] The energy scheduling module is further used to adjust the nonlinear scheduling allocation result according to the environmental information, or to adjust the linear scheduling allocation result according to the environmental information.

[0021] Furthermore, the intelligent building scheduling system further includes: a storage module, the storage module being connected to the energy supply module;

[0022] The storage module is used to generate an energy report of a building by integrating the energy consumption of each device, and store the energy report.

[0023] Furthermore, the intelligent building scheduling system further includes: an energy alarm module, the energy alarm module being connected to the energy analysis module;

[0024] The energy alarm module is used to calculate the energy consumption of each device based on the total operation data, determine whether the consumption of each device is within the preset consumption, and mark and display the consumption of the device when the consumption of the device is greater than the preset consumption.

[0025] Furthermore, the intelligent building energy scheduling system further includes: a display module, the display module being connected to the energy scheduling module;

[0026] The display module is used to display the nonlinear scheduling allocation result or the linear scheduling allocation result.

[0027] In a second aspect, the present application provides a smart building energy scheduling method under multi-scenario weather conditions, comprising:

[0028] Determine a target area, obtain total operating data of all devices in the target area, divide the total operating data into non-linear data and linear data, calculate a first ratio of the non-linear data to the total operating data, and calculate a second ratio of the linear data to the operating data;

[0029] When the first ratio is greater than the second ratio, the nonlinear data is input into a preset nonlinear scheduling model, and the nonlinear data is analyzed and calculated using the preset nonlinear scheduling model to obtain a nonlinear scheduling allocation result; when the second ratio is greater than the first ratio, the linear data is input into a preset linear scheduling model, and the linear data is analyzed and calculated using the preset linear scheduling model to obtain a linear scheduling allocation result;

[0030] According to the nonlinear scheduling allocation result or the linear scheduling allocation result, solar energy, wind energy, geothermal energy or electric energy is controlled to schedule and allocate the energy supply to the target area.

[0031] In a third aspect, the present disclosure provides a computer device including a memory and a processor, wherein the memory stores a computer program, and wherein the processor implements the steps of a smart building energy scheduling method under multi-scenario weather conditions in any of the above-mentioned embodiments when executing the computer program.

[0032] In a fourth aspect, the present disclosure provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the smart building energy scheduling system under multi-scenario weather conditions of any of the above-mentioned embodiments is run.

[0033] The above-mentioned intelligent building energy dispatching system under multi-scenario weather conditions determines the target area through the energy analysis module, obtains the total operating data of all equipment in the target area, divides the total operating data into nonlinear data and linear data, and calculates the proportion of nonlinear data and linear data; the energy dispatching module calculates the dispatching allocation result according to the proportion of nonlinear data and linear data, and the energy supply module controls solar energy, wind energy, geothermal energy or electric energy to dispatch and allocate the energy supply to the target area according to the dispatching allocation result. Ensure that the intelligent building energy dispatching system under multi-scenario weather conditions can be applied to the energy dispatching strategy of building areas with linear and nonlinear problems, improve the accuracy of the building energy dispatching system and broaden its applicability. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 The present invention is a structural diagram of an intelligent building energy dispatching system under multi-scenario weather conditions in an embodiment.

[0035] Figure 2 The present invention is a flowchart of a smart building energy scheduling method under multi-scenario weather conditions in an embodiment.

[0036] Figure 3 Schematic diagram of the internal structure of a computer device in one embodiment.

[0037] In the drawings, the same reference numerals are used for the same components, and the drawings are not drawn to scale. DETAILED DESCRIPTION

[0038] In order to enable those skilled in the art to better understand the technical solution of the present disclosure, and to fully understand and implement how the present disclosure applies technical means to solve technical problems and achieve the corresponding technical effects, the technical solution in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are only embodiments of a part of the present disclosure, not all of the embodiments. The embodiments of the present disclosure and the various features in the embodiments can be combined with each other without conflict, and the technical solutions formed are all within the scope of protection of the present disclosure. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without making creative work should fall within the scope of protection of the present disclosure.

[0039] It should be noted that the terms "first", "second", etc. in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusions, for example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products, or devices.

[0040] It should be noted that the steps shown in the flowcharts of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and that, although a logical order is shown in the flowcharts, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0041] Embodiment 1

[0042] Please refer to Figure 1 In one embodiment of the present application, a smart building energy scheduling system under multi-scenario weather conditions includes an energy supply module 101, an energy scheduling module 102, and an energy analysis module 103. The detailed description of each functional module is as follows:

[0043] In actual applications, the energy supply module 103 is used to determine the target area, obtain the total operating data of all equipment in the target area, divide the total operating data into nonlinear data and linear data, calculate the first proportion of the nonlinear data in the total operating data, and calculate the second proportion of the linear data in the operating data.

[0044] Specifically, the target area is determined first, which is the area where energy scheduling is required. In the process of determining the target area, it can be determined by a network segment or a specific group of devices. After the target area is determined, the total operation data of all devices in the target area is collected, which includes various sensor readings, operation logs, performance parameters, etc. The total operation data can be obtained in a variety of ways, such as directly reading the device interface, extracting from a data warehouse or database, or remotely collecting from the device using the Internet of Things (IoT) technology.

[0045] The total operating data is then divided into nonlinear data and linear data, where nonlinear data refers to data whose relationships are not linear or proportional. For example, the performance of some equipment may show nonlinear growth with the increase of load; linear data refers to data whose relationships can be described by a straight line. For example, the energy consumption of some equipment may increase linearly with the increase of operating time.

[0046] Calculate the proportion of the nonlinear data in the running total. This can be done by dividing the total amount of nonlinear data by the total amount of running data. This ratio helps to understand the importance of the nonlinear data in the overall data. Similarly, calculate the proportion of the linear data in the running data (which may refer to the same data set as the running total). This is also done by dividing the total amount of linear data by the total amount of running data. This ratio provides the relative importance of the linear data in the overall data.

[0047] In actual applications, the energy scheduling module 102 is used to input the nonlinear data into a preset nonlinear scheduling model when the first ratio is greater than the second ratio, and use the preset nonlinear scheduling model to analyze and calculate the nonlinear data to obtain a nonlinear scheduling allocation result; when the second ratio is greater than the first ratio, input the linear data into a preset linear scheduling model, and use the preset linear scheduling model to analyze and calculate the linear data to obtain a linear scheduling allocation result.

[0048] Specifically, after calculating the first proportion of nonlinear data to the total running data (referred to as P_nonlinear) and the second proportion of linear data to the running data (referred to as P_linear), compare the sizes of these two proportions. If P_nonlinear>P_linear, it means that nonlinear data dominates the overall data, and the preset nonlinear scheduling model should be selected at this time; if P_linear>P_nonlinear, linear data is more important in the overall data, and the preset linear scheduling model should be selected at this time. According to the decision results, the corresponding data (nonlinear data or linear data) is input into the selected scheduling model. The preset nonlinear scheduling model or linear scheduling model will analyze and calculate these data to generate scheduling allocation results.

[0049] In practical applications, the energy supply module 101 is used to control the scheduling and allocation of energy supply to the target area by using solar energy, wind energy, geothermal energy or electric energy according to the nonlinear scheduling and allocation result or the linear scheduling and allocation result.

[0050] Specifically, energy supply analysis and evaluation are first performed: First, data on the supply capacity and current status of various energy sources (solar energy, wind energy, geothermal energy, electric energy, etc.) in the target area are collected; based on the collected data, the current availability, reliability, and sustainability of various energy sources are evaluated. Then the dispatch allocation results are applied: according to the nonlinear data ratio and linear data ratio calculated previously, the nonlinear dispatch allocation results or linear dispatch allocation results are selected; the dispatch allocation results are mapped to specific energy supplies. Based on the dispatch allocation results, a detailed energy supply dispatch plan is formulated. This includes determining the energy supply schedule, priority, and possible backup energy plans; converting the dispatch plan into specific control instructions, and realizing precise dispatch and control of energy supplies such as solar energy, wind energy, geothermal energy, or electric energy through energy management systems or smart grid technologies.

[0051] In this embodiment, after energy scheduling is performed, it can be further monitored and optimized. Specifically, real-time monitoring: After scheduling is implemented, the operating status and performance of the energy supply system are continuously monitored. Data analysis and feedback: Real-time data of the system is collected and analyzed to evaluate the implementation effect of the scheduling allocation results. Optimization and adjustment: According to the analysis results, the scheduling allocation plan is optimized and adjusted as necessary to improve the efficiency and reliability of energy supply.

[0052] Through the above technical solution, an intelligent building energy dispatching system under multi-scenario weather conditions uses the energy analysis module 103 to determine the target area, obtain the total operating data of all equipment in the target area, divide the total operating data into nonlinear data and linear data, and calculate the proportion of nonlinear data and linear data; the energy dispatching module 102 calculates the dispatching allocation result according to the proportion of nonlinear data and linear data, and the energy supply module 101 controls solar energy, wind energy, geothermal energy or electric energy to dispatch and allocate the energy supply to the target area according to the dispatching allocation result. Ensure that the intelligent building energy dispatching system under multi-scenario weather conditions can be applied to the energy dispatching strategy of building areas with linear problems and nonlinear problems, improve the accuracy of the building energy dispatching system and broaden its applicability.

[0053] In one embodiment, the energy scheduling module 102 includes: a second energy scheduling unit, the second energy scheduling unit is connected to the energy supply module;

[0054] The second energy scheduling unit is used to construct a preset nonlinear scheduling model. When the first ratio is greater than the second ratio, the nonlinear data is input into the preset nonlinear scheduling model, and the preset nonlinear scheduling model is used to analyze and calculate the nonlinear data to obtain a nonlinear scheduling allocation result.

[0055] Specifically, nonlinear data related to energy supply in the target area are collected, including real-time supply of various energy sources, demand forecasts, price changes, environmental factors (such as weather conditions), etc. According to the nature of the problem, a suitable nonlinear model is selected, such as a nonlinear programming model, a neural network model, a support vector machine model, etc. These models can handle complex nonlinear relationships and provide an optimized solution. In this embodiment, a nonlinear programming model is selected to construct a preset nonlinear scheduling model.

[0056] Since there are many different factors between devices, the collected nonlinear data may be quite different. If the square root of the nonlinear data is not calculated, the accuracy of the final fitted model will be poor. Therefore, in this application, the preset nonlinear scheduling model is set as:

[0057]

[0058] Among them, ξ1+ξ2+ξ n =1, ξ1 is the regression proportionality coefficient of the first device, ξ2 is the regression proportionality coefficient of the second device, ξ n is the regression proportional coefficient of the nth device; y1 is the energy data of the first device, y2 is the energy data of the second device, is the energy data of the nth device, and θ is the environmental regression deviation.

[0059] In practical applications, the regression proportionality coefficient of the equipment is determined according to the energy consumption of the equipment. The greater the proportion of the energy consumption of the equipment to the total energy consumption, the greater the regression proportionality coefficient of the equipment; the smaller the proportion of the energy consumption of the equipment to the total energy consumption, the smaller the regression proportionality coefficient of the equipment.

[0060] In one embodiment, after calculating a first ratio of the nonlinear data to the total operation data and calculating a second ratio of the linear data to the operation data, if the difference between the first ratio and the second ratio is not greater than a preset difference, the linear data and the nonlinear data may be used simultaneously to schedule energy, specifically:

[0061] The nonlinear data is input into a preset nonlinear model, and the preset nonlinear scheduling model is used to analyze and calculate the nonlinear data to obtain a nonlinear scheduling allocation result; the linear data is input into a preset linear scheduling model, and the preset linear scheduling model is used to analyze and calculate the linear data to obtain a linear scheduling allocation result; finally, the linear scheduling allocation result is used to adjust the nonlinear scheduling allocation result. In this embodiment, the preset nonlinear model is:

[0062]

[0063] Among them, Y is the nonlinear scheduling allocation result, ξ1+ξ2+ξ n =1, ξ1 is the regression proportionality coefficient of the first device, ξ2 is the regression proportionality coefficient of the second device, ξ n is the regression proportional coefficient of the nth device; y1 is the energy data of the first device, y2 is the energy data of the second device, is the energy data of the nth device, θ is the environmental regression deviation, and Z is the linear scheduling allocation result.

[0064] In one embodiment, the energy scheduling module includes: a first energy scheduling unit; the first energy scheduling unit is connected to the energy supply module;

[0065] The first energy scheduling unit is used to construct a linear scheduling model. When the second ratio is greater than the first ratio, the linear data is input into a preset linear scheduling model, and the preset linear scheduling model is used to analyze and calculate the linear data to obtain a linear scheduling allocation result.

[0066] Specifically, when the condition is met (i.e., the second ratio is greater than the first ratio), the linear data is input into the preset linear scheduling model to ensure that the data format matches the model input requirements. The input linear data is analyzed and calculated using the preset linear scheduling model, including preprocessing of the linear data, feature extraction and other data processing processes. After the linear scheduling model analyzes and calculates, the linear scheduling allocation result is output. This result includes the allocation ratio of resources, the priority ranking of tasks, or other outputs related to linear scheduling.

[0067] In this embodiment, it is ensured that the preset linear scheduling model is applicable to the current scenario, so that the data input into the model should be accurate and reliable to avoid the model from generating misleading results.

[0068] In one embodiment, the smart building energy scheduling system may further include: a weather sensing module, the weather sensing module being connected to the energy scheduling module;

[0069] The weather sensing module is used to obtain environmental information in the target area, wherein the environmental information is one of sunny, rainy, cloudy, or typhoon;

[0070] The energy scheduling module is further used to adjust the nonlinear scheduling allocation result according to the environmental information, or to adjust the linear scheduling allocation result according to the environmental information.

[0071] In the process of energy dispatch and allocation, it is crucial to consider the environmental information in the target area, because different weather conditions will directly affect the energy production efficiency and demand pattern. Specifically, the environmental information in the target area is collected in real time or regularly using data sources such as meteorological monitoring equipment, weather forecast services or satellite images. In this embodiment, it is necessary to ensure that the collected environmental information is accurate and reliable, and it can be cross-validated through multiple data sources.

[0072] Environmental information is classified into categories such as sunny, rainy, cloudy, and typhoon; the classification results of environmental information are updated in real time according to weather changes. In this embodiment, the allocation ratio of solar energy may be increased on sunny days because solar panels are more efficient in clear weather. The allocation of solar energy may be reduced on rainy days while increasing the allocation of wind energy or hydropower. Cloudy days may require balancing the allocation of multiple energy sources to cope with insufficient light. Typhoons may require reducing the allocation of all outdoor energy sources (such as solar energy and wind energy) to ensure safety. Finally, changes in energy demand are predicted based on weather changes, and the nonlinear scheduling allocation results are adjusted accordingly.

[0073] Furthermore, in nonlinear scheduling models, certain parameters or coefficients (i.e., environmental regression deviations) may need to be adjusted according to weather conditions to reflect linear changes in energy production and demand; under extreme weather conditions such as typhoons, it may be necessary to adjust the priority of energy allocation to ensure energy supply to key facilities or services.

[0074] In one embodiment, the intelligent building scheduling system further includes: a storage module, the storage module being connected to the energy supply module;

[0075] The storage module is used to generate an energy report of a building by integrating the energy consumption of each device, and store the energy report.

[0076] In this embodiment, sensors and metering equipment are installed at key energy consumption points in buildings (such as power systems, heating / cooling systems, lighting systems, etc.) to monitor and record energy consumption data in real time. Establish a data collection system: Use Internet of Things (IoT) technology or data collection software to collect energy consumption data from various devices. Ensure that the data collection system can obtain data in real time and accurately, and has data verification and anomaly detection functions.

[0077] The data is then processed, including: cleaning the collected raw data to remove duplicate, erroneous or invalid data; converting the cleaned data into a unified format and standard to facilitate subsequent analysis and report generation; storing the processed data in a database or data warehouse for long-term preservation and subsequent query.

[0078] Then analyze the data and conduct an in-depth analysis of the energy consumption patterns of buildings. Identify peak hours of energy consumption, inefficient areas, and potential energy-saving opportunities; use machine learning or statistical models based on historical data to predict future energy consumption trends. Finally, generate reports based on reporting requirements and store the reports.

[0079] In one embodiment, the intelligent building energy scheduling system further includes: a display module, the display module being connected to the energy scheduling module;

[0080] The display module is used to display the nonlinear scheduling allocation result or the linear scheduling allocation result.

[0081] In one embodiment, the intelligent building scheduling system further includes: an energy alarm module, the energy alarm module being connected to the energy analysis module;

[0082] The energy alarm module is used to calculate the energy consumption of each device based on the total operation data, determine whether the consumption of each device is within the preset consumption, and mark and display the consumption of the device when the consumption of the device is greater than the preset consumption.

[0083] Specifically, set reasonable energy consumption thresholds (preset consumption) for each device. These thresholds can be determined based on the energy efficiency standards of the device, historical data, industry standards, or specific needs of the building; compare the calculated consumption of each device with its preset consumption. When the consumption of a device is greater than its preset consumption, mark it as a device that "exceeds the preset consumption." Generate detailed reports for these devices that exceed the preset consumption, including information such as consumption, excess ratio, possible reasons, etc. At the same time, the alarm system can be triggered to notify relevant personnel to pay attention and take action.

[0084] In this embodiment, the energy alarm module can be connected to the energy display module, and then the energy display module can display the devices that exceed the preset consumption and their related information in an intuitive manner (such as tables, charts, color coding, etc.).

[0085] In this embodiment, the above modules may be embedded in or independent of the processor in the device in the form of hardware, or may be stored in the memory in the device in the form of software, so that the processor can call and execute the operations corresponding to the above modules.

[0086] Embodiment 2

[0087] In this embodiment, a method for scheduling energy in a smart building under multiple weather scenarios is provided. The method for scheduling energy in a smart building under multiple weather scenarios is applicable to a smart building energy scheduling system for multiple weather scenarios in the above embodiment. Figure 2 As shown, including:

[0088] Step 201: determine a target area, obtain total operating data of all equipment in the target area, divide the total operating data into nonlinear data and linear data, calculate a first proportion of the nonlinear data to the total operating data, and calculate a second proportion of the linear data to the operating data.

[0089] Step 202: When the first ratio is greater than the second ratio, the nonlinear data is input into a preset nonlinear scheduling model, and the preset nonlinear scheduling model is used to analyze and calculate the nonlinear data to obtain a nonlinear scheduling allocation result; when the second ratio is greater than the first ratio, the linear data is input into a preset linear scheduling model, and the preset linear scheduling model is used to analyze and calculate the linear data to obtain a linear scheduling allocation result.

[0090] Step 203: Control solar energy, wind energy, geothermal energy or electric energy to dispatch and dispatch energy supply to the target area according to the nonlinear dispatch and allocation result or the linear dispatch and allocation result.

[0091] For the specific definition of a smart building energy scheduling method under multi-scenario weather conditions, please refer to the description of a smart building energy scheduling system under multi-scenario weather conditions above, which will not be repeated here. Each step in the above-mentioned smart building energy scheduling method under multi-scenario weather conditions can be implemented in whole or in part by software, hardware, or a combination thereof.

[0092] Embodiment 3

[0093] In this embodiment, a computer device is provided. The computer device includes a processor, a memory, a network interface, a display screen and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program, and the non-volatile storage medium is deployed with a database, which is used to store all data involved in a smart building energy scheduling method under multi-scenario weather. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The network interface of the computer device is used to communicate with other computer devices that deploy application software. When the computer program is executed by the processor, a smart building energy scheduling method under multi-scenario weather is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covered on the display screen, or a key, trackball or touchpad set on the computer device housing, or an external keyboard, touchpad or mouse.

[0094] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and when the processor executes the computer program, the steps of a smart building energy scheduling method under multi-scenario weather conditions described in any of the above embodiments are implemented.

[0095] Embodiment 4

[0096] In this embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, an intelligent building energy scheduling system under multi-scenario weather conditions described in any of the above embodiments is run.

[0097] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0098] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0099] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. An intelligent building energy dispatching system under multi-scenario weather conditions, characterized in that: include: Energy supply module, energy scheduling module and energy analysis module; Wherein the energy scheduling module is connected to the energy analysis module, and the energy scheduling module is connected to the energy supply module; An energy analysis module, configured to determine a target area, obtain total operation data of all devices in the target area, divide the total operation data into non-linear data and linear data, calculate a first ratio of the non-linear data to the total operation data, and calculate a second ratio of the linear data to the operation data; An energy scheduling module, configured to input the nonlinear data into a preset nonlinear scheduling model when the first ratio is greater than the second ratio, and use the preset nonlinear scheduling model to analyze and calculate the nonlinear data to obtain a nonlinear scheduling allocation result; When the second ratio is greater than the first ratio, the linear data is input into a preset linear scheduling model, and the linear data is analyzed and calculated using the preset linear scheduling model to obtain a linear scheduling allocation result; An energy supply module is used to control the scheduling and allocation of energy supply to the target area by using solar energy, wind energy, geothermal energy or electric energy according to the nonlinear scheduling and allocation result or the linear scheduling and allocation result.

2. According to the multi-scenario weather intelligent building energy dispatching system of claim 1, it is characterized in that: The energy scheduling module includes: a second energy scheduling unit, the second energy scheduling unit is connected to the energy supply module; The second energy scheduling unit is used to construct a preset nonlinear scheduling model. When the first ratio is greater than the second ratio, the nonlinear data is input into the preset nonlinear scheduling model, and the preset nonlinear scheduling model is used to analyze and calculate the nonlinear data to obtain a nonlinear scheduling allocation result.

3. According to the multi-scenario weather intelligent building energy dispatching system of claim 2, it is characterized in that: The preset nonlinear scheduling model is: Y=ξ1√y1+ξ2√y2+...+ξ n √y n +θ Among them, ξ1+ξ2+ξ n =1, ξ1 is the regression proportionality coefficient of the first device, ξ2 is the regression proportionality coefficient of the second device, ξ n is the regression proportional coefficient of the nth device; y1 is the energy data of the first device, y2 is the energy data of the second device, √y n is the energy data of the nth device, and θ is the environmental regression deviation.

4. According to the multi-scenario weather intelligent building energy dispatching system of claim 1, it is characterized in that: The energy scheduling module includes: a first energy scheduling unit; the first energy scheduling unit is connected to the energy supply module; The first energy scheduling unit is used to construct a linear scheduling model. When the second ratio is greater than the first ratio, the linear data is input into a preset linear scheduling model, and the preset linear scheduling model is used to analyze and calculate the linear data to obtain a linear scheduling allocation result.

5. According to the multi-scenario weather intelligent building energy dispatching system of claim 1, it is characterized in that: The intelligent building energy dispatching system may further include: a weather sensing module, the weather sensing module being connected to the energy dispatching module; The weather sensing module is used to obtain environmental information in the target area, wherein the environmental information is one of sunny, rainy, cloudy, or typhoon; The energy scheduling module is further used to adjust the nonlinear scheduling allocation result according to the environmental information, or to adjust the linear scheduling allocation result according to the environmental information.

6. The intelligent building energy dispatching system under multi-scenario weather conditions according to claim 1 is characterized in that: The intelligent building scheduling system further includes: a storage module, the storage module being connected to the energy supply module; The storage module is used to generate an energy report of a building by integrating the energy consumption of each device, and store the energy report.

7. The intelligent building energy dispatching system under multi-scenario weather conditions according to claim 1 is characterized in that: The intelligent building scheduling system further includes: an energy alarm module, the energy alarm module being connected to the energy analysis module; The energy alarm module is used to calculate the energy consumption of each device based on the total operation data, determine whether the consumption of each device is within the preset consumption, and mark and display the consumption of the device when the consumption of the device is greater than the preset consumption.

8. The intelligent building energy dispatching system under multi-scenario weather conditions according to any one of claims 1 to 7, characterized in that: The intelligent building energy scheduling system further includes: a display module, the display module being connected to the energy scheduling module; The display module is used to display the nonlinear scheduling allocation result or the linear scheduling allocation result.

9. A smart building energy scheduling method under multi-scenario weather conditions, characterized in that: include: Determine a target area, obtain total operating data of all devices in the target area, divide the total operating data into non-linear data and linear data, calculate a first ratio of the non-linear data to the total operating data, and calculate a second ratio of the linear data to the operating data; When the first ratio is greater than the second ratio, the nonlinear data is input into a preset nonlinear scheduling model, and the nonlinear data is analyzed and calculated using the preset nonlinear scheduling model to obtain a nonlinear scheduling allocation result; When the second ratio is greater than the first ratio, the linear data is input into a preset linear scheduling model, and the linear data is analyzed and calculated using the preset linear scheduling model to obtain a linear scheduling allocation result; According to the nonlinear scheduling allocation result or the linear scheduling allocation result, solar energy, wind energy, geothermal energy or electric energy is controlled to schedule and allocate the energy supply to the target area.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the intelligent building energy scheduling system under multi-scenario weather conditions described in any one of claims 1 to 8 is run.

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