A building energy consumption intelligent prediction management system and method

Abstract

The invention relates to the technical field of digital control of building energy consumption, and discloses an intelligent prediction management system and method for building energy consumption. The system comprises a building newly added energy consumption object identification module, a building electricity energy consumption prediction module, and a building electricity energy consumption prediction and dispatching management module; the daily electricity energy consumption parameters of new building electrical equipment in a target building are accurately matched based on the daily electricity energy consumption parameters of different characteristic types of building electrical equipment stored in history based on big data combined with an intelligent recognition algorithm and characteristic information of new building electrical equipment, so as to realize intelligent and accurate analysis of the daily electricity energy consumption of new electrical equipment in the target building; the electricity energy consumption of new electrical equipment and original electrical equipment in the target building in a predicted time length is scientifically and efficiently predicted based on numerical processing, so as to realize classified dynamic prediction of electricity energy consumption of electrical equipment in the target building, and improve the reliability of the building energy consumption prediction management result.

Description

Technical Field

[0001] The present invention relates to the technical field of digital control of building energy consumption, and in particular to an intelligent prediction management system and method for building energy consumption. Background Art

[0002] There are two ways to define building energy consumption. In a broad sense, building energy consumption refers to the energy consumption of the entire process from building material manufacturing, construction, to building use. In a narrow sense, building energy consumption, that is, the operating energy consumption of a building, is people's daily energy consumption, such as heating, air conditioning, lighting, cooking, laundry, etc. It is the dominant part of building energy consumption. Usually, building energy consumption management is based on automation technology to digitally transform electrical equipment in buildings to achieve the purpose of reducing building energy consumption; however, the existing building energy consumption management cannot achieve dynamic and accurate prediction of the latest building electricity energy consumption, nor can it achieve scientific prediction, scheduling and control of building electricity energy consumption.

[0003] A Chinese invention patent with announcement number CN104516324B discloses an intelligent building management system, which collects and receives data and equipment status parameters reported by hardware equipment online, and scientifically analyzes the maintenance status and energy consumption status parameters of the hardware equipment, thereby achieving accurate statistics on the energy consumption status of the hardware equipment and the overall energy consumption status of the building. At the same time, it combines a graphical user interface to receive user requirements and the maintenance status of the hardware equipment, and intelligently and scientifically adjusts the operating status of the hardware equipment, thereby achieving the purpose of reducing energy consumption; however, the above technology cannot achieve a scientific evaluation of the energy consumption of newly added hardware equipment inside the building, which reduces the scientificity and accuracy of building energy consumption management. Summary of the invention

[0004] 1. Technical issues to be resolved

[0005] In order to solve the problem that the existing building energy consumption management cannot realize dynamic and accurate prediction of the latest building electricity energy consumption, nor can it realize scientific prediction and dispatching control of building electricity energy consumption, the above purpose is to accurately collect the characteristic information of new building electrical equipment and the time length information of building energy consumption prediction, intelligently judge the objects of new building electrical equipment, scientifically analyze the daily electricity energy consumption parameters of new building electrical equipment, autonomously and efficiently count the predicted electricity energy consumption parameters of new and existing electrical equipment in the building, accurately predict the total electricity energy consumption parameters of building electrical equipment, and scientifically execute the predicted building electricity consumption dispatching and control operations.

[0006] (II) Technical solution

[0007] The present invention is implemented by the following technical solution: a method for intelligent prediction and management of building energy consumption, the method comprising the following steps:

[0008] S1. Collect the predicted time length data of building energy consumption and the feature text data of new building electrical equipment;

[0009] S2, performing object judgment processing of the newly added building electrical equipment in the target building according to the feature text data of the newly added building electrical equipment, generating judgment data of the newly added building electrical equipment object, and directly executing step S5 when the newly added building electrical equipment object does not exist;

[0010] S3. When it exists, the daily power consumption parameters of the newly added building power equipment in the target building are analyzed and processed based on the feature text data of the newly added building power equipment and the daily power consumption data of the building power equipment of different feature types in history, and the daily power consumption data of the newly added building power equipment are constructed;

[0011] S4, performing power consumption measurement processing of the newly added building electrical equipment in the target building within the predicted time length according to the building energy consumption prediction time length data and the newly added building electrical equipment daily power consumption data, and generating the newly added building electrical equipment power consumption prediction data;

[0012] S5, based on the building energy consumption forecast time length data and the historical daily power consumption data of building electrical equipment of different characteristic types, the power consumption of the original building electrical equipment in the target building is measured and processed within the forecast time length, and the power consumption forecast data of the original building electrical equipment is generated;

[0013] S6. Construct the total power consumption forecast data of building electrical equipment;

[0014] S7. Obtain the total power consumption forecast data of the building electrical equipment and the building energy consumption forecast time length data and execute the building forecast power consumption scheduling control operation.

[0015] Preferably, the operation steps of collecting the building energy consumption forecast time length data and the newly added building electrical equipment feature text data are as follows:

[0016] S11. Collect the building energy consumption prediction time length parameters of the target building online through the building energy consumption management platform, and generate the building energy consumption prediction time length data ,in The unit of is day;

[0017] The building energy consumption management platform collects product usage feature information of new electrical equipment in the target building online, and generates feature text data of new building electrical equipment The product usage characteristic information includes the product name information, product specification model information, product usage condition information, product manufacturer information and product production date information of the newly added electrical equipment.

[0018] Preferably, the newly added building electrical equipment object in the target building is judged and processed based on the newly added building electrical equipment feature text data to generate the newly added building electrical equipment object judgment data. When the newly added building electrical equipment object does not exist, the operation steps of directly executing step S5 are as follows:

[0019] S21, the newly added building electrical equipment feature text data Perform data character object search and judgment, and generate new building electrical equipment object judgment data based on the data character object search and judgment results ;

[0020] when If there are data characters in the target building, it means that there are new electrical equipment in the target building, and the new building electrical equipment object judgment data is output. for existence;

[0021] when If there is no data character in the target building, it means that there is no new electrical equipment in the target building, and the judgment data of the new building electrical equipment object is output. If it does not exist, directly execute step S5.

[0022] Preferably, when it exists, the daily power consumption parameters of the newly added building power equipment in the target building are analyzed and processed based on the feature text data of the newly added building power equipment and the daily power consumption data of the building power equipment of different feature types in history, and the operation steps of constructing the daily power consumption data of the newly added building power equipment are as follows:

[0023] S31, when the newly added building electrical equipment object determines data If it exists, establish a historical data set of daily electricity consumption of electrical equipment in buildings with different characteristics , ;in Indicates The daily power consumption data of building electrical equipment with different characteristics corresponding to the historical building electrical equipment product usage characteristic combination information type, Indicates the maximum number of building electrical equipment product usage feature combination information types; building electrical equipment product usage feature combination information indicates a combination of product name information, product specification model information, product usage condition information, product manufacturer information and product production date information of electrical equipment already existing in the target building, and the historical daily power consumption data of building electrical equipment with different feature types indicates the daily power consumption data of electrical equipment with different types of building electrical equipment product usage feature combination information. The unit is kilowatt-hour per day;

[0024] S32, adding the feature text data of the building electrical equipment A collection of daily electricity consumption data of building electrical equipment with different characteristics and history Daily electricity consumption data of electrical equipment in buildings of different characteristics and types as described in the historical data Perform keyword matching of electrical equipment product usage characteristics to search for the new building electrical equipment characteristic text data The corresponding historical daily power consumption data of building electrical equipment of different characteristic types , and construct a new data set of daily electricity consumption of building electrical equipment , execute to generate the daily power consumption data set of the newly added building electrical equipment The specific steps are as follows:

[0025] S321, initializing algorithm parameters, population size N, maximum number of iterations T;

[0026] S322, initializing the population, calculating the fitness, and determining the daily power consumption identification pathfinder and the daily power consumption identification follower;

[0027] S323, according to the position formula A collection of daily electricity consumption data of building electrical equipment of different historical characteristics Update the daily electricity consumption identification pathfinder position in the search space, where t represents the current iteration number of the algorithm; It represents the pathfinder for identifying daily electricity consumption after the tth iteration A collection of daily electricity consumption data of building electrical equipment of different historical characteristics The position in the search space, It represents the pathfinder for identifying daily electricity consumption after the t-1th iteration A collection of daily electricity consumption data of building electrical equipment of different historical characteristics The position in the search space, It represents the pathfinder for identifying daily electricity consumption after the t+1th iteration A collection of daily electricity consumption data of building electrical equipment of different historical characteristics The position in the search space, represents the step size factor of the daily power consumption identification pathfinder movement and It follows a uniform distribution in [0,1];

[0028] S324, according to the position formula Update the daily power consumption identification follower in the historical different feature types of building electrical equipment daily power consumption data set The position in the search space where Indicates the daily electricity consumption identification follower after the tth iteration A collection of daily electricity consumption data of building electrical equipment of different historical characteristics The position in the search space, Indicates the daily electricity consumption identification follower after the t+1th iteration A collection of daily electricity consumption data of building electrical equipment of different historical characteristics Position in the search space; Indicates the identification followers of other daily electricity consumption after the tth iteration A collection of daily electricity consumption data of building electrical equipment of different historical characteristics Search for positions in space, daily power consumption to identify the position of the follower Mobility is not only related to daily power consumption, but also to the pathfinder location Related, and influenced by other daily electricity consumption to identify the follower location The impact of Represents the daily power consumption identification followers in the historical different feature types of building electrical equipment daily power consumption data set The position distance parameter in the search space, Represents the daily power consumption data set of building electrical equipment of different historical feature types between the daily power consumption identification pathfinder and the daily power consumption identification follower The position distance parameter in the search space, , ; represents the interaction coefficient between followers in daily electricity consumption identification, It represents the attraction coefficient of the daily power consumption identification pathfinder to the daily power consumption identification follower, , All are uniformly distributed in [1,2]; The step size factor for the movement of the daily power consumption identification follower and other daily power consumption identification followers, The step size factor for the daily power consumption identification follower and the daily power consumption identification pathfinder, , All are random numbers in the range [0,1];

[0029] S325, calculating the historical daily power consumption data set of building electrical equipment of different characteristic types All the historical daily power consumption data of building electrical equipment of different feature types in the search space New building electrical equipment feature text data The fitness value of the daily power consumption data set of building electrical equipment of different characteristic types in the history Update the search space to search for the text data related to the newly added building electrical equipment characteristics The most matching historical daily power consumption data of building electrical equipment of different characteristic types Global optimum;

[0030] S326: When the maximum number of iterations is met, the output step S325 searches for the feature text data of the newly added building electrical equipment. The most matching historical daily electricity consumption data of building electrical equipment of different characteristic types After data identification processing, a new data set of daily electricity consumption of building electrical equipment is constructed , ,in Indicates New daily electricity consumption data of building electrical equipment, Indicates the maximum number of newly added building electrical equipment; The unit is kilowatt-hour per day.

[0031] Preferably, the steps of measuring the power consumption of the newly added building electrical equipment in the target building within the predicted time length are performed according to the predicted time length data of the building energy consumption and the daily power consumption data of the newly added building electrical equipment to generate the predicted power consumption data of the newly added building electrical equipment are as follows:

[0032] S41, the building energy consumption prediction time length data The daily electricity consumption data set of the newly added building electrical equipment Daily electricity consumption data of new building electrical equipment mentioned in According to the number of newly added building electrical equipment, the power consumption of newly added building electrical equipment within the predicted time length is measured and processed in order to generate the power consumption forecast data of newly added building electrical equipment ,in , The unit is kilowatt-hour.

[0033] Preferably, the steps of measuring and processing the power consumption of the original building power equipment in the target building within the predicted time length are performed based on the predicted building energy consumption time length data and the historical daily power consumption data of building power equipment of different characteristic types, and generating the predicted power consumption data of the original building power equipment are as follows:

[0034] S51, the building energy consumption prediction time length data A collection of daily electricity consumption data of building electrical equipment with different characteristics and history Daily electricity consumption data of electrical equipment in buildings of different characteristics and types as described in the historical data According to the product usage characteristics of building electrical equipment, the energy consumption of existing building electrical equipment in the target building is measured and processed in order within the predicted time length, and the original building electrical equipment energy consumption forecast data is generated. ,in , The unit is kilowatt-hour.

[0035] Preferably, the steps for constructing the forecast data of total power consumption of building electrical equipment are as follows:

[0036] S61, based on the generated power consumption forecast data of the newly added building electrical equipment and the generated power consumption forecast data of the original building electrical equipment Carry out the power consumption prediction parameters and numerical statistical processing of the new building power equipment and the original building power equipment to construct the total power consumption prediction data of the building power equipment ,in , The unit is kilowatt-hour.

[0037] Preferably, the steps of obtaining the total power consumption forecast data of the building electrical equipment and the building energy consumption forecast time length data and executing the building forecast power consumption scheduling control operation are as follows:

[0038] S71. Obtaining the total power consumption forecast data of the building's electrical equipment and the predicted time length data of the building energy consumption ;

[0039] S72: using the building energy consumption management platform to predict the total power consumption of the building's electrical equipment Corresponding electricity consumption forecast data and the building energy consumption forecast time length data The corresponding prediction time length parameter executes the building prediction power consumption scheduling control operation for the target building.

[0040] A building energy consumption intelligent prediction and management system, used to implement the building energy consumption intelligent prediction and management method, the system includes a building new energy consumption object identification module, a building electricity energy consumption prediction module, and a building electricity energy consumption prediction and scheduling management module;

[0041] The new building energy consumption object identification module includes a building energy consumption prediction time length acquisition unit, a new building electrical equipment feature text information acquisition unit, and a new building electrical equipment object judgment unit;

[0042] The building energy consumption prediction time length acquisition unit collects building energy consumption prediction time length data through the building energy consumption management platform; the new building electrical equipment feature text information acquisition unit collects new building electrical equipment feature text data through the building energy consumption management platform; the new building electrical equipment object judgment unit performs new building electrical equipment object judgment processing in the target building based on the new building electrical equipment feature text data to generate new building electrical equipment object judgment data;

[0043] The building power consumption prediction module includes a storage unit for daily power consumption parameters of building power equipment of different historical characteristics, a daily power consumption parameter analysis unit for newly added building power equipment, a prediction and measurement unit for power consumption of newly added building power equipment, a prediction and measurement unit for power consumption of existing building power equipment, and a prediction and measurement unit for total power consumption of building power equipment;

[0044] The storage unit for daily power consumption parameters of building electrical equipment with different historical characteristics is used to store the daily power consumption data of building electrical equipment with different historical characteristics; the analysis unit for daily power consumption parameters of newly added building electrical equipment is used to analyze and process the daily power consumption parameters of newly added building electrical equipment in the target building based on the feature text data of the newly added building electrical equipment and the daily power consumption data of building electrical equipment with different historical characteristics, and construct the daily power consumption data of the newly added building electrical equipment; the power consumption prediction and metering unit for newly added building electrical equipment is used to calculate the power consumption prediction time length data of the newly added building electrical equipment according to the power consumption prediction time length data of the newly added building electrical equipment. The daily electricity consumption data is used to measure the electricity consumption of newly added building electrical equipment in the target building within the predicted time length, and the electricity consumption forecast data of the newly added building electrical equipment is generated; the electricity consumption forecast metering unit of the original building electrical equipment is used to measure the electricity consumption of the original building electrical equipment in the target building within the predicted time length according to the building energy consumption forecast time length data and the daily electricity consumption data of the building electrical equipment of different characteristic types in the history, and the electricity consumption forecast data of the original building electrical equipment is generated; the total electricity consumption forecast metering unit of the building electrical equipment is used to construct the total electricity consumption forecast data of the building electrical equipment;

[0045] The building power consumption forecasting and scheduling management module includes a building power equipment total power consumption forecasting result acquisition unit and a building forecasting power consumption scheduling and control operation execution unit;

[0046] The building electrical equipment total electricity energy consumption forecast result acquisition unit is used to obtain the total electricity energy consumption forecast data of the building electrical equipment and the building energy consumption forecast time length data; the building forecast electricity consumption scheduling and control operation execution unit executes the building forecast electricity consumption scheduling and control operation based on the total electricity energy consumption forecast data of the building electrical equipment and the building energy consumption forecast time length data in combination with the building energy consumption management platform.

[0047] (III) Beneficial effects

[0048] The present invention provides a building energy consumption intelligent prediction management system and method. It has the following beneficial effects:

[0049] 1. Accurately collect online the building energy consumption prediction time parameters and the characteristic information of new building electrical equipment through the building energy consumption management platform, and provide real data support for efficient and accurate prediction of building electrical energy consumption; intelligently judge the new building electrical equipment objects in the target building based on the characteristic information of the new building electrical equipment, realize the autonomous and accurate judgment and identification of the new electrical equipment objects in the target building, and improve the accuracy of building energy consumption prediction management.

[0050] 2. Accurately match the daily power consumption parameters of building electrical equipment of different characteristic types based on the big data storage history, combined with intelligent recognition algorithms and characteristic information of new building electrical equipment, to achieve intelligent and accurate analysis of the daily power consumption of new electrical equipment in the target building, and improve the intelligence and accuracy of building energy consumption forecasting management; scientifically and efficiently predict the power consumption of new and existing electrical equipment in the target building over the predicted time period based on numerical processing, realize dynamic prediction of power consumption of electrical equipment in the target building, and improve the reliability of building energy consumption forecasting management results; scientifically calculate the total power consumption forecast parameters of building electrical equipment, realize accurate statistical summary of the total power consumption of electrical equipment in the target building, and improve the intuitiveness of building energy consumption forecasting management results.

[0051] 3. By efficiently and accurately obtaining the forecast results of total electricity consumption of building electrical equipment and the forecast time parameters of building energy consumption, reliable data support is provided for the efficient and accurate execution of building electricity consumption forecast and scheduling operations; based on the forecast results of total electricity consumption of building electrical equipment and the forecast time parameters of building energy consumption, combined with the building energy consumption management platform, the building forecast electricity consumption scheduling and control operations are intelligently and accurately executed to ensure the efficiency and quality of building energy consumption forecast management. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 A schematic diagram of a module of a building energy consumption intelligent prediction and management system provided by the present invention;

[0053] Figure 2The present invention provides a flow chart of a method for intelligent prediction and management of building energy consumption. DETAILED DESCRIPTION

[0054] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0055] The embodiments of the intelligent prediction management system and method for building energy consumption are as follows:

[0056] Embodiment 1:

[0057] See also Figure 1 - Figure 2 , a method for intelligent prediction and management of building energy consumption, the method comprising the following steps:

[0058] S1. Collect the predicted time length data of building energy consumption and the feature text data of new building electrical equipment;

[0059] S2, judging and processing the newly added building electrical equipment object in the target building according to the new building electrical equipment feature text data, generating the new building electrical equipment object judgment data, and directly executing step S5 when the new building electrical equipment object does not exist;

[0060] S3. When it exists, the daily power consumption parameters of the newly added building power equipment in the target building are analyzed and processed based on the feature text data of the newly added building power equipment and the daily power consumption data of the building power equipment of different feature types in history, and the daily power consumption data of the newly added building power equipment are constructed;

[0061] S4. Measuring and processing the power consumption of the newly added building electrical equipment in the target building within the predicted time length according to the building energy consumption prediction time length data and the daily power consumption data of the newly added building electrical equipment, and generating the power consumption prediction data of the newly added building electrical equipment;

[0062] S5. Based on the building energy consumption forecast time length data and the historical daily power consumption data of building power equipment of different characteristic types, the power consumption of the original building power equipment in the target building is measured and processed within the forecast time length to generate the power consumption forecast data of the original building power equipment;

[0063] S6. Construct the total power consumption forecast data of building electrical equipment;

[0064] S7. Obtain the total power consumption forecast data of the building's electrical equipment and the building's energy consumption forecast time length data and execute the building's forecast power consumption scheduling and control operation.

[0065] For further information, see Figure 1 - Figure 2 The steps for collecting building energy consumption forecast time length data and new building electrical equipment feature text data are as follows:

[0066] S11. Collect the building energy consumption prediction time length parameters of the target building online through the building energy consumption management platform, and generate the building energy consumption prediction time length data ,in The unit of is day;

[0067] The building energy consumption management platform collects product usage feature information of new electrical equipment in the target building online, and generates feature text data of new building electrical equipment The product usage characteristic information includes the product name information, product specification model information, product usage condition information, product manufacturer information and product production date information of the newly added electrical equipment.

[0068] According to the feature text data of the newly added building electrical equipment, the newly added building electrical equipment object in the target building is judged and processed to generate the newly added building electrical equipment object judgment data. When the newly added building electrical equipment object does not exist, the operation steps of directly executing step S5 are as follows:

[0069] S21. Add new building electrical equipment feature text data Perform data character object search and judgment, and generate new building electrical equipment object judgment data based on the data character object search and judgment results ;

[0070] when If there are data characters in the target building, it means that there are new electrical equipment in the target building, and the new building electrical equipment object judgment data is output. for existence;

[0071] when If there is no data character in the target building, it means that there is no new electrical equipment in the target building, and the judgment data of the new building electrical equipment object is output. If it does not exist, directly execute step S5.

[0072] Through the cooperation of the building energy consumption prediction time length collection unit and the new building electrical equipment characteristic text information collection unit, the building energy consumption management platform is used to accurately collect the building energy consumption prediction time length parameters and the characteristic information of the new building electrical equipment online, providing real data support for the efficient and accurate prediction of building electrical energy consumption; a new building electrical equipment object judgment unit is added to perform intelligent judgment on the new building electrical equipment objects in the target building based on the characteristic information of the new building electrical equipment, so as to realize the autonomous and accurate judgment and identification of the new electrical equipment objects in the target building, and improve the accuracy of building energy consumption prediction management.

[0073] For further information, see Figure 1 - Figure 2 , when it exists, the daily power consumption parameters of the new building power equipment in the target building are analyzed and processed based on the feature text data of the new building power equipment and the daily power consumption data of the building power equipment of different feature types in history, and the operation steps for constructing the daily power consumption data of the new building power equipment are as follows:

[0074] S31, when adding building electrical equipment object judgment data If it exists, establish a historical data set of daily electricity consumption of electrical equipment in buildings with different characteristics , ;in Indicates The daily power consumption data of building electrical equipment with different characteristics corresponding to the historical building electrical equipment product usage characteristic combination information type, Indicates the maximum number of building electrical equipment product usage feature combination information types; building electrical equipment product usage feature combination information indicates the combination of product name information, product specification model information, product usage condition information, product manufacturer information and product production date information of the electrical equipment already existing in the target building. The historical daily power consumption data of building electrical equipment with different feature types indicates the daily power consumption data of electrical equipment with different types of building electrical equipment product usage feature combination information. The unit is kilowatt-hour per day;

[0075] S32. Add new building electrical equipment feature text data A collection of daily electricity consumption data of building electrical equipment with different characteristics and historical characteristics Daily electricity consumption data of electrical equipment in buildings with different characteristics and types in history Perform keyword matching of electrical equipment product usage characteristics to search for new building electrical equipment feature text data Corresponding historical daily electricity consumption data of electrical equipment in buildings with different characteristics , and construct a new data set of daily electricity consumption of building electrical equipment , execute to generate new building electrical equipment daily power consumption data set The specific steps are as follows:

[0076] S321, initializing algorithm parameters, population size N, maximum number of iterations T;

[0077] S322, initializing the population, calculating the fitness, and determining the daily power consumption identification pathfinder and the daily power consumption identification follower;

[0078] S323, according to the position formula A collection of daily electricity consumption data of building electrical equipment with different historical characteristics Update the daily electricity consumption identification pathfinder position in the search space, where t represents the current iteration number of the algorithm; It represents the pathfinder for identifying daily electricity consumption after the tth iteration A collection of daily electricity consumption data of building electrical equipment with different historical characteristics The position in the search space, It represents the pathfinder for identifying daily electricity consumption after the t-1th iteration A collection of daily electricity consumption data of building electrical equipment with different historical characteristics The position in the search space, It represents the pathfinder for identifying daily electricity consumption after the t+1th iteration A collection of daily electricity consumption data of building electrical equipment with different historical characteristics The position in the search space, represents the step size factor of the daily power consumption identification pathfinder movement and It follows a uniform distribution in [0,1];

[0079] S324, according to the position formula Update daily power consumption identification followers in the historical different characteristics of the type of building electrical equipment daily power consumption data set The position in the search space where Indicates the daily electricity consumption identification follower after the tth iteration A collection of daily electricity consumption data of building electrical equipment with different historical characteristics The position in the search space, Indicates the daily electricity consumption identification follower after the t+1th iteration A collection of daily electricity consumption data of building electrical equipment with different historical characteristics Position in the search space; Indicates the identification followers of other daily electricity consumption after the tth iteration A collection of daily electricity consumption data of building electrical equipment with different historical characteristics Search for positions in space, daily power consumption to identify the position of the follower Mobility is not only related to daily power consumption, but also to the pathfinder location Related, and influenced by other daily electricity consumption to identify the follower location The impact of Represents the daily power consumption identification followers in the historical different characteristics of the building electrical equipment daily power consumption data set The position distance parameter in the search space, Represents the daily power consumption data set of building electrical equipment with different historical characteristics between the daily power consumption identification pathfinder and the daily power consumption identification follower The position distance parameter in the search space, , ; represents the interaction coefficient between followers in daily electricity consumption identification, It represents the attraction coefficient of the daily power consumption identification pathfinder to the daily power consumption identification follower, , All are uniformly distributed in [1,2]; The step size factor for the movement of the daily power consumption identification follower and other daily power consumption identification followers, The step size factor for the daily power consumption identification follower and the daily power consumption identification pathfinder, , All are random numbers in the range [0,1];

[0080] S325. Calculate the historical daily power consumption data set of building electrical equipment of different characteristic types All historical daily electricity consumption data of building electrical equipment with different feature types in the search space New building electrical equipment feature text data The fitness value is calculated based on the daily power consumption data of building electrical equipment of different historical characteristics. Update the search space to find and add feature text data of building electrical equipment The most matching historical daily electricity consumption data of building electrical equipment of different characteristic types Global optimum;

[0081] S326: When the maximum number of iterations is met, the output step S325 searches for the feature text data of the newly added building electrical equipment. The most matching historical daily electricity consumption data of building electrical equipment of different characteristic types After data identification processing, a new data set of daily electricity consumption of building electrical equipment is constructed , ,in Indicates New daily electricity consumption data of building electrical equipment, Indicates the maximum number of newly added building electrical equipment; The unit is kilowatt-hour per day.

[0082] According to the building energy consumption forecast time length data and the daily power consumption data of the newly added building electrical equipment, the power consumption of the newly added building electrical equipment in the target building is measured within the forecast time length, and the operation steps for generating the power consumption forecast data of the newly added building electrical equipment are as follows:

[0083] S41. The building energy consumption prediction time length data A collection of daily electricity consumption data of newly added building electrical equipment New data on daily electricity consumption of building electrical equipment According to the number of newly added building electrical equipment, the power consumption of newly added building electrical equipment within the predicted time length is measured and processed in order to generate the power consumption forecast data of newly added building electrical equipment ,in , The unit is kilowatt-hour.

[0084] Based on the building energy consumption forecast time length data and the historical daily power consumption data of building electrical equipment of different characteristic types, the power consumption of the original building electrical equipment in the target building is measured and processed within the forecast time length. The operation steps for generating the power consumption forecast data of the original building electrical equipment are as follows:

[0085] S51. The building energy consumption prediction time length data A collection of daily electricity consumption data of building electrical equipment with different characteristics and historical characteristics Daily electricity consumption data of electrical equipment in buildings with different characteristics and types in history According to the product usage characteristics of building electrical equipment, the energy consumption of existing building electrical equipment in the target building is measured and processed in order within the predicted time length, and the original building electrical equipment energy consumption forecast data is generated. ,in , The unit is kilowatt-hour.

[0086] The steps to construct the total power consumption forecast data of building electrical equipment are as follows:

[0087] S61, based on the generated new building electrical equipment power consumption forecast data And the generated original building electrical equipment power consumption forecast data Carry out the power consumption prediction parameters and numerical statistical processing of the new building power equipment and the original building power equipment to construct the total power consumption prediction data of the building power equipment ,in , The unit is kilowatt-hour.

[0088] Through the cooperation of the storage unit of daily electricity consumption parameters of building electrical equipment with different historical characteristics and the analysis unit of daily electricity consumption parameters of new building electrical equipment, the daily electricity consumption parameters of new building electrical equipment in the target building are accurately matched based on the storage of daily electricity consumption parameters of building electrical equipment with different historical characteristics of big data combined with intelligent recognition algorithm and characteristic information of new building electrical equipment, so as to realize intelligent and accurate analysis of daily electricity consumption of new electrical equipment in the target building and improve the intelligence and accuracy of building energy consumption forecasting management; the new building electrical equipment electricity consumption forecasting metering unit and the original building electrical equipment electricity consumption forecasting metering unit, based on numerical processing, scientifically and efficiently predict the electricity consumption of new and original electrical equipment in the target building during the forecast time, realize dynamic prediction of electricity consumption of electrical equipment in the target building, and improve the reliability of building energy consumption forecasting management results; the total electricity consumption forecasting metering unit of building electrical equipment scientifically calculates the total electricity consumption forecasting parameters of building electrical equipment, realizes accurate statistical summary of the total electricity consumption of electrical equipment in the target building, and improves the intuitiveness of building energy consumption forecasting management results.

[0089] For further information, see Figure 1 - Figure 2 The steps for obtaining the total power consumption forecast data of building electrical equipment and the building energy consumption forecast time length data and executing the building forecast power consumption scheduling control operation are as follows:

[0090] S71. Obtain forecast data on total power consumption of building electrical equipment and building energy consumption forecast time length data ;

[0091] S72. Based on the total power consumption forecast data of building electrical equipment through the building energy consumption management platform Corresponding electricity consumption forecast data and building energy consumption forecast time length data The corresponding prediction time length parameter executes the building prediction power consumption scheduling control operation for the target building.

[0092] Through the cooperation between the building electrical equipment total electricity energy consumption forecast result acquisition unit and the building forecast electricity consumption scheduling and control operation execution unit, the total electricity consumption forecast result information of building electrical equipment and the building energy consumption forecast time length parameters are acquired efficiently and accurately, providing reliable data support for the efficient and accurate execution of building electricity consumption forecast scheduling operations; based on the total electricity consumption forecast result information of building electrical equipment and the building energy consumption forecast time length parameters combined with the building energy consumption management platform, the building forecast electricity consumption scheduling and control operations are intelligently and accurately executed to ensure the efficiency and quality of building energy consumption forecast management.

[0093] Embodiment 2:

[0094] See also Figure 1 - Figure 2 , a building energy consumption intelligent prediction management system, used to implement a building energy consumption intelligent prediction management method, the system includes a building new energy consumption object identification module, a building electricity energy consumption prediction module, and a building electricity energy consumption prediction scheduling management module;

[0095] The new building energy consumption object identification module includes a building energy consumption prediction time length acquisition unit, a new building electrical equipment feature text information acquisition unit, and a new building electrical equipment object judgment unit;

[0096] A building energy consumption prediction time length acquisition unit is used to collect building energy consumption prediction time length data through the building energy consumption management platform; a new building electrical equipment feature text information acquisition unit is added to collect new building electrical equipment feature text data through the building energy consumption management platform; a new building electrical equipment object judgment unit is added to perform new building electrical equipment object judgment processing in the target building based on the new building electrical equipment feature text data, and generate new building electrical equipment object judgment data;

[0097] The building power consumption prediction module includes a storage unit for daily power consumption parameters of building power equipment of different historical characteristics, a new daily power consumption parameter analysis unit for building power equipment, a new power consumption prediction and measurement unit for building power equipment, a power consumption prediction and measurement unit for existing building power equipment, and a total power consumption prediction and measurement unit for building power equipment;

[0098] A storage unit for daily electricity consumption parameters of building electrical equipment with different historical characteristics is used to store daily electricity consumption data of building electrical equipment with different historical characteristics; a new analysis unit for daily electricity consumption parameters of building electrical equipment is added to analyze and process the daily electricity consumption parameters of the new building electrical equipment in the target building based on the feature text data of the new building electrical equipment and the daily electricity consumption data of the building electrical equipment with different historical characteristics, and construct the daily electricity consumption data of the new building electrical equipment; a new building electrical equipment electricity consumption prediction and metering unit is added to calculate the daily electricity consumption parameters of the new building electrical equipment according to the building energy consumption prediction time length data and the daily electricity consumption of the new building electrical equipment. The electric energy consumption data is used to measure the electric energy consumption of the newly added building electric equipment in the target building within the predicted time length, and the electric energy consumption forecast data of the newly added building electric equipment is generated; the original building electric equipment electric energy consumption forecast metering unit is used to measure the electric energy consumption of the original building electric equipment in the target building within the predicted time length according to the building energy consumption forecast time length data and the historical daily electric energy consumption data of building electric equipment of different characteristic types, and the electric energy consumption forecast data of the original building electric equipment is generated; the building electric equipment total electric energy consumption forecast metering unit is used to construct the total electric energy consumption forecast data of the building electric equipment;

[0099] The building power consumption forecasting and scheduling management module includes a building power equipment total power consumption forecasting result acquisition unit and a building forecasting power consumption scheduling and control operation execution unit;

[0100] The unit for obtaining the forecast result of the total electricity energy consumption of building electrical equipment is used to obtain the forecast data of the total electricity energy consumption of building electrical equipment and the forecast time length data of building energy consumption; the unit for executing the building forecast electricity consumption scheduling and control operation executes the building forecast electricity consumption scheduling and control operation based on the forecast data of the total electricity energy consumption of building electrical equipment and the forecast time length data of building energy consumption in combination with the building energy consumption management platform.

[0101] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for intelligent prediction and management of building energy consumption, characterized in that: The method comprises the following steps: S1. Collect data on the predicted duration of building energy consumption And new building electrical equipment feature text data ; S2. Based on the newly added building electrical equipment feature text data Perform judgment processing on newly added building electrical equipment objects in the target building and generate judgment data on newly added building electrical equipment objects , when it does not exist, directly execute step S5; S3, when it exists, based on the newly added building electrical equipment feature text data Analyze and process the daily power consumption parameters of new building power equipment in the target building based on the historical daily power consumption data of building power equipment with different characteristics, and construct the daily power consumption data of new building power equipment; S4. Based on the predicted time length data of the building energy consumption The power consumption of the newly added building electrical equipment in the target building is measured and processed with the daily power consumption data of the newly added building electrical equipment within the predicted time length to generate the power consumption prediction data of the newly added building electrical equipment ; S5. Based on the predicted time length data of the building energy consumption The daily power consumption data of building power equipment with different characteristics of the historical type are used to measure the power consumption of the original building power equipment in the target building within the predicted time length to generate the power consumption prediction data of the original building power equipment ; S6. Construct the total power consumption forecast data of building electrical equipment ; S7. Obtaining the total power consumption forecast data of the building's electrical equipment and the predicted time length data of the building energy consumption And perform building forecast electricity energy consumption scheduling and control operations.

2. According to claim 1, a method for intelligent prediction and management of building energy consumption is characterized in that: The S1 comprises the following steps: S11. Collect the building energy consumption prediction time length parameters of the target building online through the building energy consumption management platform, and generate the building energy consumption prediction time length data ,in The unit of is day; The building energy consumption management platform collects product usage feature information of new electrical equipment in the target building online, and generates feature text data of new building electrical equipment .

3. The intelligent prediction and management method for building energy consumption according to claim 2 is characterized in that: The S2 comprises the following steps: S21, the newly added building electrical equipment feature text data Perform data character object search and judgment, and generate new building electrical equipment object judgment data based on the data character object search and judgment results ; when If there are data characters in the output, the new building electrical equipment object judgment data is output for existence; when If there is no data character in the output, the new building electrical equipment object judgment data is output If it does not exist, directly execute step S5.

4. The intelligent prediction and management method for building energy consumption according to claim 3 is characterized in that: The S3 comprises the following steps: S31, when the newly added building electrical equipment object determines data If it exists, establish a historical data set of daily electricity consumption of electrical equipment in buildings with different characteristics , ;in Indicates The daily power consumption data of building electrical equipment with different characteristics corresponding to the historical building electrical equipment product usage characteristic combination information type, Indicates the maximum number of building electrical equipment product usage feature combination information types. The unit is kilowatt-hour per day; S32, the With the As stated in Perform keyword matching of electrical equipment product features to search for the The corresponding , and construct a new data set of daily electricity consumption of building electrical equipment , execute to generate the daily power consumption data set of the newly added building electrical equipment The specific steps are as follows: S321, initializing algorithm parameters, population size N, maximum number of iterations T; S322, initializing the population, calculating the fitness, and determining the daily power consumption identification pathfinder and the daily power consumption identification follower; S323, according to the position formula in the Update the daily electricity consumption to identify the pathfinder position in the search space; S324, updating the daily power consumption according to the position formula to identify the follower Position in the search space; S325, calculating the Search space for all the With the The fitness value of The search space is updated to search for The best match for Global optimum; S326, when the maximum number of iterations is met, the output S325 step searches for the The best match for After data identification processing, a new data set of daily electricity consumption of building electrical equipment is constructed , ,in Indicates New daily electricity consumption data of building electrical equipment, Indicates the maximum number of newly added building electrical equipment; The unit is kilowatt-hour per day.

5. The method for intelligent prediction and management of building energy consumption according to claim 4 is characterized in that: The S4 comprises the following steps: S41, the With the As stated in According to the number of newly added building electrical equipment, the power consumption of newly added building electrical equipment within the predicted time length is measured and processed in order to generate the power consumption forecast data of newly added building electrical equipment ,in , The unit is kilowatt-hour.

6. The method for intelligent prediction and management of building energy consumption according to claim 5 is characterized in that: The S5 comprises the following steps: S51, the With the As stated in According to the product usage characteristics of building electrical equipment, the energy consumption of existing building electrical equipment in the target building is measured and processed in order within the predicted time length, and the original building electrical equipment energy consumption forecast data is generated. ,in , The unit is kilowatt-hour.

7. A method for intelligent prediction and management of building energy consumption according to claim 6, characterized in that: The S6 comprises the following steps: S61, based on the generated and the generated Carry out the power consumption prediction parameters and numerical statistical processing of the new building power equipment and the original building power equipment to construct the total power consumption prediction data of the building power equipment ,in , The unit is kilowatt-hour.

8. The intelligent prediction and management method for building energy consumption according to claim 7 is characterized in that: The S7 comprises the following steps: S71, obtaining the and stated ; S72, through the building energy consumption management platform based on The corresponding power consumption forecast data and the The corresponding prediction time length parameter executes the building prediction power consumption scheduling control operation for the target building.

9. A building energy consumption intelligent prediction and management system, used to implement a building energy consumption intelligent prediction and management method according to any one of claims 1 to 8, characterized in that: The system comprises a building newly added energy consumption object identification module, a building electricity energy consumption prediction module, and a building electricity energy consumption prediction scheduling management module.

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