A big data driven green building energy management system

Abstract

The application discloses a kind of big data driven green building energy management systems, it is related to the technical field of green building energy management.Method includes: waste judgment module obtains the energy use data of user, judges whether user exists waste energy phenomenon;Important evaluation module collects the building use system of user, evaluates the importance of building use system;Threshold setting module evaluates the stability coefficient of real-time green energy, sets the importance threshold value;Energy use module is filtered from building use system and obtained energy use system;Adaptation evaluation module evaluates the degree of adaptation of real-time green energy and energy use system;Energy management module obtains energy adaptation system from energy use system according to the degree of adaptation, manages the docking of real-time green energy and energy adaptation system.The application improves the performance of saving optimization index of big data driven green building energy management.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of green building energy management, and in particular to a green building energy management system driven by big data. BACKGROUND

[0002] As a building mode aiming to reduce environmental impact and improve resource utilization efficiency, the core of green building lies in minimizing energy consumption and optimizing efficiency in the building operation process through the integration of energy-saving technologies, renewable energy systems (such as solar photovoltaic, wind power, etc.) and intelligent control means. In order to achieve this goal, the building needs a set of fine energy management system to coordinate the complex internal energy-using units (such as lighting system, heating / cooling system, fresh air system, elevator, office equipment, etc.) and the external variable environment and energy supply conditions. However, the existing green building energy management technology has significant deficiencies in the core matching link: the system often lacks sufficient data support and intelligent analysis capability, making it difficult to accurately and dynamically determine which specific energy-using unit the current and predicted renewable energy output should be allocated to. Usually, only fixed priorities are set, and allocation is made according to the priorities, which does not play a role in saving and optimizing energy, weakening the energy-saving potential and environmental protection benefits of green buildings. SUMMARY

[0003] The purpose of the present application is to provide a green building energy management system driven by big data to solve the problems raised in the background art.

[0004] The green building energy management system driven by big data provided by the present application adopts the following technical solution:

[0005] The waste judgment module obtains the user's energy use data and judges whether the user has wasted energy based on the energy use data;

[0006] The important evaluation module collects the building use system of the user and evaluates the importance of the building use system if the user has wasted energy;

[0007] The threshold setting module collects real-time green energy, evaluates the stability coefficient of the real-time green energy, and sets the importance threshold according to the stability coefficient;

[0008] The energy use module selects the energy use system from the building use system according to the importance threshold and the importance;

[0009] The adaptation evaluation module obtains the user's attention to the energy use system and evaluates the adaptation degree of the real-time green energy and the energy use system in combination with the importance of the energy use system;

[0010] The energy management module obtains the energy adaptation system from the energy use system according to the adaptation degree, and manages the docking of the real-time green energy and the energy adaptation system.

[0011] Preferably, the step of obtaining the energy use data of the user and judging whether the user has the energy waste phenomenon according to the energy use data comprises:

[0012] Obtaining the average energy use amount of all users and extracting the real-time energy use amount according to the energy use data;

[0013] Judging whether the real-time energy use amount exceeds the average energy use amount, and if so, obtaining the electric appliance operation data of the user;

[0014] Evaluating the use appropriateness of the electric appliance according to the electric appliance operation data and judging whether the user has the energy waste phenomenon according to the use appropriateness;

[0015] If the real-time energy use amount does not exceed the average energy use amount, it is judged that the user does not have the energy waste phenomenon.

[0016] Preferably, the step of evaluating the use appropriateness of the electric appliance according to the electric appliance operation data and judging whether the user has the energy waste phenomenon according to the use appropriateness comprises:

[0017] Collecting the electric appliance use data of all users and counting the use proportion of different electric appliances according to the electric appliance use data;

[0018] Setting a use proportion standard and distinguishing the regular use electric appliance and the irregular use electric appliance according to the use proportion standard;

[0019] Extracting the user operation electric appliance according to the electric appliance operation data of the user and judging whether the user operation electric appliance is the regular use electric appliance;

[0020] If the user operation electric appliance is the regular use electric appliance, it is judged that the user has the energy waste phenomenon;

[0021] If the user operation electric appliance is not the regular use electric appliance, obtaining the use condition of the user operation electric appliance and judging whether the user has the energy waste phenomenon according to the use condition.

[0022] Preferably, the step of obtaining the use condition of the user operation electric appliance and judging whether the user has the energy waste phenomenon according to the use condition comprises:

[0023] Screening the irregular operation electric appliance in the user operation electric appliance and recording it as the user abnormal electric appliance;

[0024] Collecting whether the user abnormal electric appliance has the use condition, and if so, judging whether the user satisfies the use condition;

[0025] If the user meets the use condition, it is judged that the user does not exist waste energy phenomenon;

[0026] If the user abnormal electrical appliance does not have use condition, the running scene of the user abnormal electrical appliance is obtained, and the real-time running scene of using the user abnormal electrical appliance is collected;

[0027] The environment similarity of the running scene and the real-time running scene is compared as the use suitability of the user abnormal electrical appliance, and whether the user exists waste energy phenomenon is judged according to the use suitability.

[0028] Preferably, the step of collecting real-time green energy and evaluating the stability coefficient of real-time green energy according to the stability coefficient to set the importance threshold value, specifically comprises:

[0029] Setting a demand time period, collecting energy fluctuation data of real-time green energy, and judging whether real-time green energy is completely stable in the demand time period according to the energy fluctuation data;

[0030] If the real-time green energy is completely stable in the demand time period, the stability coefficient is the maximum value;

[0031] If the real-time green energy is not completely stable in the demand time period, the fluctuation degree of the real-time green energy is evaluated according to the energy fluctuation data;

[0032] According to the energy fluctuation data, the average stability duration ratio of real-time green energy to the total duration of demand time period is extracted and recorded as duration ratio;

[0033] The stability coefficient of real-time green energy is obtained by combining the fluctuation degree and the duration ratio, and the importance threshold value is set according to the stability coefficient.

[0034] Preferably, the step of obtaining the importance of the user to the energy use system, combining the importance of the energy use system, and evaluating the adaptability of real-time green energy to the energy use system, specifically comprises:

[0035] Obtaining the historical use data of the user to the energy use system, and evaluating the degree of attention of the user to the energy use system according to the historical use data;

[0036] According to the historical use data and the importance of the energy use system, the life influence degree of the energy use system to the user is evaluated;

[0037] Collecting the historical failure data of the energy use system, and evaluating the initiative awareness degree of the user according to the historical failure data;

[0038] The importance of the energy use system is evaluated by comprehensively considering the degree of attention, the life influence degree and the initiative awareness degree;

[0039] The difference between the importance degree and the importance degree threshold is calculated, and the importance degree is evaluated to obtain the adaptation degree of the real-time green energy and energy use system.

[0040] Preferably, the step of obtaining the historical use data of the energy use system by the user and evaluating the importance degree of the energy use system by the user according to the historical use data is specifically:

[0041] The performance index value of the energy use system set by the user is obtained according to the historical use data, and the average index value of the performance of the energy use system is collected;

[0042] The difference between the performance index value and the average index value is calculated and recorded as a performance difference value;

[0043] The change attention of the user to the energy use system is extracted according to the historical use data;

[0044] The use standard of the energy use system is collected, the proportion of the user executing according to the use standard is counted according to the historical use data, and is recorded as a standard use proportion;

[0045] The importance degree of the energy use system by the user is comprehensively evaluated in combination with the performance difference value, the change attention and the standard use proportion.

[0046] Preferably, the step of extracting the change attention of the user to the energy use system according to the historical use data is specifically:

[0047] The frequency of the user paying attention to the energy use system is counted according to the historical use data and is recorded as an attention frequency;

[0048] The average change value of the performance of the energy use system when the user pays attention to the energy use system is counted according to the historical use data;

[0049] The change attention of the user to the energy use system is evaluated in combination with the attention frequency and the average change value.

[0050] Preferably, the step of evaluating the life influence degree of the energy use system on the user according to the historical use data and the importance degree of the energy use system is specifically:

[0051] The function index value of the energy use system is obtained, and the environment index value corresponding to the energy use system is collected;

[0052] The difference between the environment index value and the function index value is calculated and recorded as a function difference value;

[0053] The frequency of the user adjusting the energy use system is extracted according to the historical use data and is recorded as an adjustment frequency;

[0054] The influence degree of the energy use system on other systems is obtained, and the life influence degree of the energy use system on the user is comprehensively obtained in combination with the function difference value and the adjustment frequency.

[0055] Preferably, the history fault data of the energy collection system is collected, and the step of evaluating the initiative awareness degree of the user according to the history fault data is specifically:

[0056] According to the history fault data, the user's personal solved faults are screened and recorded as user solved faults;

[0057] The user's solutions corresponding to the user solved faults are collected, and the user's solutions associated with energy are screened and recorded as energy solutions;

[0058] The energy saving degree of the energy solutions to energy is counted, and the proportion of the energy solutions in the user solutions is counted and recorded as the energy solution proportion;

[0059] The initiative awareness degree of the user is evaluated in combination with the energy saving degree and the energy solution proportion.

[0060] In summary, the present application includes at least one of the following beneficial technical effects:

[0061] 1. The energy use data of the user is obtained by the waste judgment module, and whether the user has the phenomenon of wasting energy is judged according to the energy use data. If the user has the phenomenon of wasting energy, the important evaluation module collects the building use system of the user, and evaluates the importance of the building use system. The threshold setting module collects the real-time green energy, sets the demand time period, and judges whether the real-time green energy is completely stable in the demand time period according to the energy fluctuation data of the real-time green energy. If the real-time green energy is completely stable in the demand time period, the stability coefficient is the maximum value. If the real-time green energy is not completely stable in the demand time period, the fluctuation degree of the real-time green energy is evaluated according to the energy fluctuation data, and the stability coefficient of the real-time green energy is obtained in combination with the average stability time length of the real-time green energy. The ratio of the total time length of the demand time period, and the importance threshold is set according to the stability coefficient. The energy use module screens the energy use system from the building use system according to the importance threshold and the importance. The adaptation evaluation module obtains the importance of the user to the energy use system, and evaluates the adaptation degree of the real-time green energy and the energy use system in combination with the importance of the energy use system. The energy management module obtains the energy adaptation system from the energy use system according to the adaptation degree, and manages the docking of the real-time green energy and the energy adaptation system. The adaptation of energy and system is evaluated from the importance of the use system, the stability of the real-time green energy, and the importance of the use system to the user, so as to realize more optimal energy management. Through the energy management under the condition of waste, it is beneficial to guide the user to save energy, and improves the saving optimization index performance of the big data driven green building energy management.

[0062] 2. If the real-time energy usage exceeds the average energy usage of the user, the user's appliance operation data is obtained. The appliance usage data of all users is collected, and the usage proportion of different appliances is calculated based on the appliance usage data. A usage proportion standard is set to distinguish between regular and irregular use of appliances. Based on the user's appliance operation data, the user's running appliances are extracted. If all the user's running appliances are regular use appliances, it is determined that the user is wasting energy. If the user's running appliances are not all regular use appliances, the irregular running appliances among the user's running appliances are screened and recorded as the user's abnormal appliances. It is collected whether the user's abnormal appliances have usage conditions. If the usage conditions are met, it is determined whether the user meets the usage conditions. If the user meets the usage conditions, it is determined that the user does not waste energy. If the user's abnormal appliances do not have usage conditions, the running scene of the user's abnormal appliances and the environmental similarity of the real-time running scene of the user's abnormal appliances are compared as the usage suitability of the user's abnormal appliances. Based on the usage suitability, it is determined whether the user wastes energy. If the real-time energy usage does not exceed the average energy usage, it is determined that the user does not waste energy. Based on the user's energy usage and appliance usage, it is evaluated whether the user wastes energy. In the case of energy waste, the user is guided to save energy through energy management. Not only is the utilization of energy optimized, but also the interference of energy management on normal use is reduced, and the intelligence of green building energy management driven by big data is improved.

[0063] 3. The difference between the performance index value of the energy usage system set by the user and the average index value of the energy usage system performance is calculated and recorded as the performance difference. The frequency of the user's attention to the energy usage system and the average change value of the energy usage system when the user pays attention to it are calculated, and the change attention of the user to the energy usage system is comprehensively obtained. The degree of attention of the user to the energy usage system is comprehensively evaluated in combination with the proportion of the user's execution according to the use standard. The difference between the environmental index value of the energy usage system and the functional index value of the energy usage system is calculated and recorded as the functional difference. The influence degree of the energy usage system on the user's life is comprehensively obtained in combination with the frequency of the user adjusting the energy usage system and the influence degree of the energy usage system on other systems. The degree of initiative of the user is evaluated by calculating the energy saving degree of the user in solving system failure and the proportion of solutions associated with energy. Finally, the importance of the energy usage system is evaluated by comprehensively evaluating the degree of attention, the influence degree of life and the degree of initiative. The difference between the importance and the importance threshold value is used to finally obtain the real-time green energy and the energy usage system. In the process of adapting the energy usage system to green energy, the user's attention caused by energy and the energy adjustment made by the user because of energy are considered. The management of green energy realizes the guidance of energy usage, greatly improves the utilization efficiency of energy, reduces the waste of energy, and improves the energy utilization efficiency of green building energy management driven by big data. BRIEF DESCRIPTION OF DRAWINGS

[0064] Figure 1 is a module connection diagram of an embodiment of a big data driven green building energy management system of the present application. DETAILED DESCRIPTION

[0065] The present application will be further described in detail below in conjunction with embodiments and Figure 1 The embodiments of the present application are not limited to this.

[0066] The present application discloses a big data driven green building energy management system, specifically comprising:

[0067] The waste judgment module acquires the user's energy use data and judges whether the user has energy waste phenomenon according to the energy use data.

[0068] The important evaluation module collects the user's building use system if the user has energy waste phenomenon and evaluates the importance of the building use system.

[0069] The user's building use system includes lighting system, heating / cooling system, elevator system, fresh air system, etc. The importance of these building use systems can be evaluated by the user himself, and the importance of the system is different in different environments. For example, in an office building, the office equipment system is particularly important because it stores a large amount of data. In a residential building, the life system will be more important.

[0070] The threshold setting module collects real-time green energy, evaluates the stability coefficient of the real-time green energy, and sets the importance threshold according to the stability coefficient.

[0071] The energy use module filters the energy use system from the building use system according to the importance threshold and the importance.

[0072] The building use system with an importance not greater than the importance threshold is used as the energy use system. Because of its own characteristics, green energy is not very stable at some moments, and the more important the equipment is, the more stable the power supply is needed. Therefore, when the importance threshold is 80, the system with an importance of 90 will have problems when using the energy because the energy can only serve the system with an importance of 80 at most.

[0073] The adaptive evaluation module acquires the user's attention to the energy use system, combines the importance of the energy use system, and evaluates the adaptability of the real-time green energy and the energy use system.

[0074] The energy management module obtains the energy adaptive system from the energy use system according to the adaptability and manages the docking of the real-time green energy and the energy adaptive system.

[0075] In practical application, in green building energy management, generally, priority is set, and then priority is adapted for management. This simple solution cannot improve the utilization of energy, and can meet the user demand, but many users waste resources cannot be effectively solved. Therefore, if there is no energy waste of the user, the energy management can be carried out according to the system priority, or directly according to the importance. If the user exists energy waste, the green energy management is matched according to the importance of the energy use system and the importance of the user to the energy use system, the user is guided to save energy independently, the energy waste is reduced, and the utilization of energy is improved.

[0076] The step of obtaining the energy use data of the user and judging whether the user exists energy waste phenomenon according to the energy use data is specifically:

[0077] The average energy use amount of all users is obtained, and the real-time energy use amount is extracted according to the energy use data.

[0078] The average energy use amount of the user refers to the average energy use amount of all users in a certain area, which can be set by the user. The real-time energy use amount refers to the real-time energy use amount of the target user.

[0079] It is judged whether the real-time energy use amount exceeds the average energy use amount, if the average energy use amount is exceeded, the user's electrical appliance running data is obtained.

[0080] The use appropriateness of the electrical appliance is evaluated according to the electrical appliance running data, and whether the user exists energy waste phenomenon is judged according to the use appropriateness.

[0081] If the real-time energy use amount does not exceed the average energy use amount, it is judged that the user does not exist energy waste phenomenon.

[0082] In practical application, the precondition of guiding the user to save energy is that the user exists energy waste phenomenon. If the user does not waste energy, it is unnecessary for the user to save energy, so it is necessary to judge whether the user exists energy waste. If the real-time energy use amount of the user does not exceed the average energy use amount, it is considered that the user does not exist energy waste. If the real-time energy use amount of the user exceeds the average energy use amount, it is necessary to further judge according to the electrical appliance use condition of the user. For example, the user needs to use medical equipment due to illness, and the medical equipment consumes a large amount of electric energy, which does not belong to the user waste energy, but the user demand. If the user opens the window while using the air conditioner, the air conditioner works continuously to consume energy. At this time, it belongs to the user waste energy.

[0083] The step of evaluating the use appropriateness of the electrical appliance according to the electrical appliance operation data and judging whether the user has the phenomenon of wasting energy according to the use appropriateness is specifically:

[0084] The electrical appliance use data of all users is collected, and the use proportion of different electrical appliances is counted according to the electrical appliance use data.

[0085] The use of some electrical appliances is time-sensitive, such as air conditioners which are generally used only in summer and winter, and heating appliances which are used only in winter. However, refrigerators are used for a long time without interruption throughout the year. The use proportion of different electrical appliances is counted as the proportion of the total number of users.

[0086] The use proportion standard is set, and the regular use electrical appliance and the irregular use electrical appliance are distinguished according to the use proportion standard.

[0087] By setting the use proportion standard, the use proportion higher than the standard is divided into the regular use electrical appliance, and the use proportion not higher than the standard is divided into the irregular use electrical appliance.

[0088] The user runs the electrical appliance according to the electrical appliance operation data of the user, and judges whether the user runs the electrical appliance is the regular use electrical appliance.

[0089] If the user runs the electrical appliance is the regular use electrical appliance, it is judged that the user has the phenomenon of wasting energy.

[0090] If the user uses all the regular electrical appliances, that is, everyone uses, but the user's energy consumption is still higher than the average value, it is considered that the user has improper operation and other situations in the process of using the electrical appliance, causing energy waste, because the user's energy consumption is higher than the general level.

[0091] If the user runs the electrical appliance is not the regular use electrical appliance, the use condition of the user running the electrical appliance is obtained, and whether the user has the phenomenon of wasting energy is judged according to the use condition.

[0092] In actual application, whether the user produces energy consumption needs to consider not only the real-time energy consumption of the user, but also the actual situation of the user. If the user uses some irregular electrical appliances that the user must use, then the energy consumption of the user will be higher than the normal level. At this time, because of the user's demand, it is not the user's subjective or improper operation that causes the high energy consumption, so it is necessary to further judge whether the user needs to use the irregular electrical appliance to judge whether the user has the energy consumption situation. For example, in summer, the user uses a heating appliance, and if the user accidentally turns it on, it is obvious that the user has caused energy waste, but the user does not know. But if the user needs to dry something, it is the actual demand of the user, which does not belong to energy waste.

[0093] Obtaining a use condition of the user running the electrical appliance, and judging whether the user has a waste energy phenomenon according to the use condition, specifically comprising:

[0094] Screening a non-conventional electrical appliance from the electrical appliances run by the user and recording as a user abnormal electrical appliance.

[0095] The user is normal in use by default for the conventional electrical appliance, and the non-conventional electrical appliance is screened as the user abnormal electrical appliance.

[0096] Collecting whether the user abnormal electrical appliance has a use condition, and judging whether the user satisfies the use condition if the user abnormal electrical appliance has the use condition.

[0097] Some electrical appliances have use conditions, for example, the air conditioner is generally used when the temperature is too high or too low, and the dehumidifier is generally run when the humidity is high. The use condition is obtained by feature extraction according to the use conditions of all users.

[0098] If the user satisfies the use condition, it is judged that the user does not have a waste energy phenomenon.

[0099] If the user does not satisfy the use condition, it is judged that the user has a waste energy phenomenon, for example, the air humidity is very small, but the user uses the dehumidifier, which is obviously unnecessary and increases energy consumption.

[0100] If the user abnormal electrical appliance does not have the use condition, the running scene of the user abnormal electrical appliance is obtained, and the real-time running scene of using the user abnormal electrical appliance is collected.

[0101] The user abnormal electrical appliance does not have the use condition, which means that it is difficult to obtain a unique use feature according to the use conditions of all users, for example, the use of the kettle can be used at any time within 24 hours, and there is no definite and general use feature, so there is no specific use condition. At this time, the running scene of the user abnormal electrical appliance is collected, and the similarity of the scenes is compared.

[0102] The environmental similarity of the running scene and the real-time running scene is compared as the use appropriateness of the user abnormal electrical appliance, and whether the user has a waste energy phenomenon is judged according to the use appropriateness.

[0103] In actual application, a suitable threshold of use degree can be set, and when the threshold is reached, it is considered that there is no waste of energy phenomenon, otherwise it is considered that there is a waste of energy phenomenon. Some electrical appliances do not have specific use conditions, such as the use of a heater, which generally requires the air temperature to reach a certain level before use, but the use of a washing machine does not have such specific conditions. However, the use scenario of the washing machine is that the user needs to wash clothes, at which time the similarity of the scenario can be compared to determine whether the user has a demand for using the electrical appliance. For example, a user has an electrocardiogram monitoring device, and the use scenario of the electrocardiogram monitoring device is usually that the user's electrocardiogram is abnormal or may be abnormal due to illness. If the user is indeed abnormal or seriously ill at this time, it is normal to use the device. However, if the user is not abnormal at this time, long-term use of the electrocardiogram monitoring device is considered a waste of energy.

[0104] The step of collecting real-time green energy and evaluating the stability coefficient of the real-time green energy, and setting the importance threshold value according to the stability coefficient, is specifically:

[0105] Setting a demand time period, collecting energy fluctuation data of real-time green energy, and determining whether the real-time green energy is completely stable in the demand time period according to the energy fluctuation data.

[0106] Green energy includes solar energy, wind energy and other energy, which will be unstable due to its own characteristics. The demand time period for energy supply is determined, and it is determined whether the green energy can be completely stable in the time period. Different green energy will change in stability due to changes in actual conditions, for example, solar energy will provide stable energy when the sunlight is stable, so it can be completely stable in the time period.

[0107] If the real-time green energy is completely stable in the demand time period, the stability coefficient is the maximum value.

[0108] If the real-time green energy is not completely stable in the demand time period, the fluctuation degree of the real-time green energy is evaluated according to the energy fluctuation data.

[0109] The fluctuation degree refers to the difference between the maximum value and the minimum value of the energy.

[0110] According to the energy fluctuation data, the average stable time length ratio of the real-time green energy to the total time length of the demand time period is extracted and recorded as the time length ratio.

[0111] The stability coefficient of the real-time green energy is obtained by combining the fluctuation degree and the time length ratio, and the importance threshold value is set according to the stability coefficient.

[0112] In actual application, the weight ratio of the fluctuation degree and the time length ratio can be preset in the weighted average framework, the normalized weighted aggregation is performed to generate the stability coefficient. Because green energy may not be stable, the importance threshold needs to be set according to the stability in the use process, so as to screen the system equipment that can function with green energy. For example, some medical equipment is used for saving people, and slight instability of function may affect the health of the user. Therefore, unstable green energy cannot be used for these medical equipment systems to function, so as to avoid irreparable loss. When the stability coefficient is higher, the importance threshold is larger, which can be set by establishing the correlation curve of the stability coefficient and the importance threshold.

[0113] The steps of obtaining the importance of the user to the energy use system, combining the importance of the energy use system, and evaluating the adaptability of the real-time green energy to the energy use system are as follows:

[0114] The historical use data of the user to the energy use system is obtained, and the importance of the user to the energy use system is evaluated according to the historical use data.

[0115] The influence degree of the energy use system on the user's life is evaluated according to the historical use data and the importance of the energy use system.

[0116] The historical failure data of the energy use system is collected, and the initiative awareness degree of the user is evaluated according to the historical failure data.

[0117] The importance of the energy use system is evaluated by comprehensively evaluating the importance, the influence degree and the initiative awareness degree.

[0118] In the fuzzy logic framework, the importance, the influence degree and the initiative awareness degree are fuzzified, the rule base weight is dynamically adjusted according to the weight ratio, the importance is generated by fuzzy reasoning and defuzzification.

[0119] The difference between the importance and the importance threshold is calculated, and the adaptability of the real-time green energy to the energy use system is evaluated in combination with the importance.

[0120] The information entropy of the difference between the importance and the importance threshold and the original data set of the importance can be calculated by the entropy weight method, the objective weight ratio is dynamically generated, and then the weighted aggregation is performed to output the adaptability. When the difference between the importance and the importance threshold is larger, the adaptability is larger, because it means that the green energy can well supply energy to the use system. When the importance is larger, the adaptability is also larger. On the one hand, when the importance is larger, the user will solve the problem in the first time, reducing the problem caused by energy supply. On the other hand, when the importance is larger, the user is more likely to adjust the energy use, thereby reducing the energy supply burden and providing convenience for the energy supply of the whole region.

[0121] In actual application, when the user wastes resources, the allocation and management of green energy not only needs to consider the adaptation of energy and system, but also needs to consider whether the cooperation of energy can guide the user to adjust his own energy use to achieve the effect of saving energy. Therefore, according to the user's degree of attention to the energy use system, the influence of the energy use system on the user's life and the user's initiative awareness in the energy use system failure, the user's attention to the energy use system is comprehensively evaluated. The greater the user's attention to the energy use system, the more the user will actively regulate the energy use to meet his own needs and save energy. For example, the user pays more attention to the use of air conditioner, and in the case that the air conditioner effect is not very good, unnecessary high-power electrical appliances will be reduced to meet the demand of air conditioner use. Therefore, if the green energy is not very stable, it is easy to appear the case that the use effect is not good. At this time, the user will adjust the use to adapt to the supply of energy and improve the satisfaction of energy use because he pays more attention to the system. For example, when the air conditioner effect is not good, the air conditioner temperature setting is adjusted to meet the normal operation of the air conditioner.

[0122] The step of obtaining the historical use data of the user to the energy use system according to the historical use data to evaluate the degree of attention of the user to the energy use system is specifically:

[0123] According to the historical use data, the performance index value of the energy use system set by the user is obtained, and the average index value of the performance of the energy use system is collected.

[0124] The performance index value refers to the performance index that the user needs the energy use system to achieve, for example, for the lighting system, the user needs the brightness of the lighting to reach A, and A is the performance index value of the lighting system. The average index value refers to the average performance required by all other users when using the system.

[0125] The difference between the performance index value and the average index value is calculated and recorded as the performance difference value.

[0126] According to the historical use data, the change attention of the user to the energy use system is extracted.

[0127] The use standard of the energy use system is collected, and the proportion of the user executing according to the use standard is calculated according to the historical use data and recorded as the standard use proportion.

[0128] The use of the energy use system is standard, for example, how to operate in what environment, how long to use and need to pause use, etc. The greater the proportion of the user complying with the standard in the use process, the more the user pays attention to the system, and the higher the degree of attention.

[0129] The degree of attention of the user to the energy use system is comprehensively evaluated by combining the performance difference value, the change attention and the standard use proportion.

[0130] In actual application, the importance degree can be calculated by using the weighted average method. If the performance difference is greater, it means that the user has higher requirements for the system, so the importance degree will be greater. If the change attention degree is higher, it means that the user can more sensitively perceive the change of the system, so the importance degree is higher. The importance degree can reflect the user's attention to the energy use system, thereby further reflecting the user's emphasis on the energy use system.

[0131] The step of extracting the change attention degree of the user to the energy use system according to the historical use data is specifically:

[0132] The frequency of the user's attention to the energy use system is counted according to the historical use data and recorded as the attention frequency.

[0133] The attention frequency can be obtained by collecting the data frequency of the user's view of the energy use system.

[0134] The average change value of the performance of the energy use system when the user pays attention to the energy use system is counted according to the historical use data.

[0135] The performance change of the energy use system in a short time when the user pays attention to the energy use system each time is counted. For example, if the user views the air conditioner related data when the average change of the air conditioner temperature is 5 degrees Celsius, the average change value is 5 degrees Celsius.

[0136] The change attention degree of the user to the energy use system is evaluated by comprehensively considering the attention frequency and the average change value.

[0137] In actual application, the change attention degree can be calculated by using the weighted average method. For the user, if the user can pay attention to the change of the energy use system in time, the user can make a response in the first time, adjust the use of energy to meet the system operation demand. The user can solve the problem in the first time, reduce the trouble caused by unstable energy supply, at the same time, the user optimizes the use of energy, reduces the burden of the user's energy use on the energy use of the whole region, and optimizes the energy management of the whole region.

[0138] The step of evaluating the life influence degree of the energy use system on the user according to the historical use data and the importance degree of the energy use system is specifically:

[0139] The function index value of the energy use system is obtained, and the corresponding environment index value of the energy use system is collected.

[0140] The environmental index value refers to the environmental index value corresponding to the functional index value. For example, in summer, the air conditioner is used to reduce the temperature, and the temperature setting of the air conditioner is the functional index value, such as setting 26 degrees Celsius, and the corresponding environmental index value refers to the real-time temperature at the moment. The lighting system refers to the brightness of the system and the brightness of the external environment. If there is no corresponding environmental index value, the environmental index value is 0 by default.

[0141] The difference between the environmental index value and the functional index value is calculated and recorded as the functional difference value.

[0142] According to the historical use data, the frequency of the user adjusting the energy use system is extracted and recorded as the adjustment frequency.

[0143] The influence degree of the energy use system on other systems is obtained, and the life influence degree of the energy use system on the user is comprehensively obtained by combining the functional difference value and the adjustment frequency.

[0144] In actual application, the influence degree of the energy use system on other systems can be obtained by user evaluation or by the function completion percentage of other systems. The original data set of the influence degree, the functional difference value and the adjustment frequency can be calculated by entropy weight method, and the objective weight proportion is dynamically generated, and then the weighted aggregation is executed to output the life influence degree. The greater the influence degree, the greater the influence of the system on the operation of other systems, and therefore the greater the life influence degree of the system. The greater the functional difference value, the higher the demand of the user for it, for example, when it is dark outside, the lighting system has a greater influence on life. At the same time, the higher the adjustment frequency, the higher the requirement of the user for the function, and therefore the greater the life influence degree. For example, the user has high requirements for the brightness of the lamp, and the user continuously adjusts the brightness of the lamp with the change of the external brightness, so if the lamp cannot be used or cannot achieve the corresponding effect, the life influence degree on the user is high.

[0145] The historical failure data of the energy use system is collected, and the user's initiative awareness degree is evaluated according to the historical failure data, which is specifically:

[0146] According to the historical failure data, the user's personal solution to the failure is screened and recorded as the user's solution to the failure.

[0147] The user's solution corresponding to the user's solution to the failure is collected, and the user's solution associated with energy is screened and recorded as the energy solution.

[0148] The user's solution associated with energy refers to the solution achieved by the user through the management and optimization of energy, for example, the air conditioner cannot run at a low temperature, and the user solves it by increasing the temperature of the air conditioner. In essence, it is to reduce the use of energy of the air conditioner to maintain the operation of the air conditioner, which is the energy solution. Or, the user stops other high-power electrical appliances to maintain the operation of the air conditioner, which also belongs to the energy solution.

[0149] The energy saving degree of the statistical energy solution is counted, and the proportion of the statistical energy solution in the user solution is recorded as the energy solution proportion.

[0150] That is, the energy consumption before and after the user solution is compared, so as to obtain the saving degree. In the user solution, not all problems are solved by energy, some users may solve the problem by means of complaint, finding people, replacing equipment, etc.

[0151] The initiative consciousness degree of the user is evaluated in combination with the saving degree and the energy solution proportion.

[0152] In actual application, the weights of the saving degree and the energy solution proportion are respectively set, the saving degree and the energy solution proportion are multiplied by the corresponding weight ratio, and then the initiative consciousness degree is obtained by superposition. When the energy solution proportion is larger and the saving degree is larger, the initiative consciousness degree is larger. It is explained that the user can be guided to save energy and manage the use of energy by using energy. Therefore, when the green energy is adapted to the energy supply system, the user can be guided to save energy to a greater extent, the environmental protection of energy management is optimized, and the energy use efficiency is improved.

[0153] The above are preferred embodiments of the present application, and are not intended to limit the protection scope of the present application, so: any equivalent changes made on the basis of the structure, shape, principle of the present application should be covered within the protection scope of the present application.

Claims

1. A big data driven green building energy management system, characterized in that, The application relates to a green energy management method and device. The method comprises the following steps: a waste judgment module acquires energy use data of a user and judges whether the user has a waste energy phenomenon according to the energy use data; an importance evaluation module collects a building use system of the user and evaluates the importance degree of the building use system if the user has the waste energy phenomenon; a threshold setting module collects real-time green energy, evaluates a stability coefficient of the real-time green energy, and sets an importance degree threshold according to the stability coefficient; an energy use module screens an energy use system from the building use system according to the importance degree threshold and the importance degree; an adaptation evaluation module acquires the attention degree of the user to the energy use system, combines the importance degree of the energy use system, and evaluates the adaptation degree of the real-time green energy and the energy use system; an energy management module acquires an energy adaptation system from the energy use system according to the adaptation degree and manages the docking of the real-time green energy and the energy adaptation system. The steps of collecting the real-time green energy, evaluating the stability coefficient of the real-time green energy, and setting the importance degree threshold according to the stability coefficient are specifically as follows: a demand time period is set, energy fluctuation data of the real-time green energy is collected, and it is judged whether the real-time green energy is completely stable in the demand time period according to the energy fluctuation data; if the real-time green energy is completely stable in the demand time period, the stability coefficient is the maximum value; if the real-time green energy is not completely stable in the demand time period, the fluctuation degree of the real-time green energy is evaluated according to the energy fluctuation data; an average stability duration ratio of the real-time green energy is extracted from the energy fluctuation data and recorded as a duration ratio, wherein the average stability duration ratio is a ratio of the average stability duration of the real-time green energy to the total duration of the demand time period; the stability coefficient of the real-time green energy is obtained by combining the fluctuation degree and the duration ratio, and the importance degree threshold is set according to the stability coefficient. The steps of acquiring the attention degree of the user to the energy use system, combining the importance degree of the energy use system, and evaluating the adaptation degree of the real-time green energy and the energy use system are specifically as follows: historical use data of the user to the energy use system is acquired, and the attention degree of the user to the energy use system is evaluated according to the historical use data; the life influence degree of the energy use system on the user is evaluated according to the historical use data and the importance degree of the energy use system; historical fault data of the energy use system is collected, and the initiative awareness degree of the user is evaluated according to the historical fault data; the attention degree of the energy use system is evaluated by comprehensively considering the attention degree, the life influence degree and the initiative awareness degree; 2. A big data driven green building energy management system as claimed in claim 1, wherein, the difference between the importance degree and the importance degree threshold is calculated, and the adaptation degree of the real-time green energy and the energy use system is evaluated by combining the attention degree. The steps of acquiring the energy use data of the user and judging whether the user has a waste energy phenomenon according to the energy use data are specifically as follows: the average energy use amount of all users is acquired, and the real-time energy use amount is extracted from the energy use data; it is judged whether the real-time energy use amount exceeds the average energy use amount, and the electric appliance running data of the user is acquired if the real-time energy use amount exceeds the average energy use amount; the use appropriateness degree of the electric appliance is evaluated according to the electric appliance running data, and it is judged whether the user has a waste energy phenomenon according to the use appropriateness degree; if the real-time energy use amount does not exceed the average energy use amount, it is judged that the user does not have a waste energy phenomenon.

3. A big data driven green building energy management system as claimed in claim 2, wherein, The step of evaluating the use suitability of the electrical appliance according to the electrical appliance operation data and judging whether the user has the phenomenon of wasting energy according to the use suitability is specifically: Collecting electrical appliance use data of all users, and counting use proportions of different electrical appliances according to the electrical appliance use data; Setting a use proportion standard, and distinguishing regular use electrical appliances from irregular use electrical appliances according to the use proportion standard; Extracting user-operated electrical appliances according to the electrical appliance operation data of the user, and judging whether the user-operated electrical appliances are all regular use electrical appliances; If the user-operated electrical appliances are all regular use electrical appliances, it is judged that the user has the phenomenon of wasting energy; If the user-operated electrical appliances are not all regular use electrical appliances, use conditions of the user-operated electrical appliances are obtained, and it is judged whether the user has the phenomenon of wasting energy according to the use conditions.

4. A big data driven green building energy management system as claimed in claim 3, wherein, The step of obtaining the use conditions of the user-operated electrical appliances and judging whether the user has the phenomenon of wasting energy according to the use conditions is specifically: Screening irregular use electrical appliances in the user-operated electrical appliances and recording as user abnormal electrical appliances; Collecting whether the user abnormal electrical appliances have use conditions, and judging whether the user meets the use conditions if the user abnormal electrical appliances have the use conditions; If the user meets the use conditions, it is judged that the user does not have the phenomenon of wasting energy; If the user abnormal electrical appliances do not have the use conditions, running scenes of the user abnormal electrical appliances are obtained, and real-time running scenes of using the user abnormal electrical appliances are collected; Comparing environment similarity of the running scenes and the real-time running scenes as use suitability of the user abnormal electrical appliances, and judging whether the user has the phenomenon of wasting energy according to the use suitability.

5. A big data driven green building energy management system as claimed in claim 1, wherein, The step of obtaining historical use data of the user to the energy use system and evaluating the degree of attention of the user to the energy use system according to the historical use data is specifically: Obtaining performance index values of the energy use system set by the user according to the historical use data, and collecting average index values of performance of the energy use system; Calculating a difference value between the performance index values and the average index values and recording as a performance difference value; Extracting a change attention degree of the user to the energy use system according to the historical use data; Collecting use standards of the energy use system, counting a proportion of the user executing according to the use standards according to the historical use data and recording as a standard use proportion; Combining the performance difference value, the change attention degree and the standard use proportion to comprehensively evaluate the degree of attention of the user to the energy use system.

6. A big data driven green building energy management system as claimed in claim 5, wherein, The step of extracting the change attention degree of the user to the energy use system according to the historical use data is specifically: Counting a frequency of the user paying attention to the energy use system according to the historical use data and recording as an attention frequency; Counting an average change value of performance of the energy use system when the user pays attention to the energy use system according to the historical use data; Comprehensively evaluating the change attention degree of the user to the energy use system according to the attention frequency and the average change value.

7. A big data driven green building energy management system as claimed in claim 5, wherein, The step of evaluating the life influence degree of the energy use system on the user according to the historical use data and the importance degree of the energy use system is specifically: Obtaining function index values of the energy use system, and collecting environment index values corresponding to the energy use system; Calculating a difference value between the environment index values and the function index values and recording as a function difference value; Extracting a frequency of the user adjusting the energy use system according to the historical use data and recording as an adjustment frequency; The influence degree of the energy use system on other systems is obtained, and the influence degree of the energy use system on the user's life is obtained by combining the function difference and the adjustment frequency.

8. A big data driven green building energy management system as claimed in claim 1, wherein, The historical fault data of the energy use system is collected, and the initiative awareness degree of the user is evaluated according to the historical fault data, specifically as follows: The faults solved by the user are screened according to the historical fault data and recorded as user-solved faults; The user solutions corresponding to the user-solved faults are collected, and the user solutions associated with energy are screened and recorded as energy solutions; The energy saving degree of the energy solutions is counted, and the proportion of the energy solutions in the user solutions is counted and recorded as the energy solution proportion; The initiative awareness degree of the user is evaluated by combining the energy saving degree and the energy solution proportion.

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