Building energy consumption data supplementary calculation method

Abstract

The invention relates to the technical field of building energy conservation, in particular to a building energy consumption data supplementary calculation method. According to the building energy consumption data supplementary calculation method provided by the invention, the technical problem of large error of a building energy consumption data supplementary calculation method in the prior art is solved. A building energy consumption data supplementary calculation method comprises the following steps that the position of instrument data missing is determined, the starting point and the ending point of instrument data missing are judged, and two influence factors including air temperature and holidays and festivals which have large influences on building energy consumption are summarized through observation of generation rules and trends of a large amount of various types of building energy consumption data. And similar change rules are continuously repeated by taking day as a unit. According to the method, air temperature and holidays are taken as factors, and trend calculation is carried out on air temperature data factors and holiday data factors through an index moving average algorithm, so that the supplementary calculation precision of building energy consumption data is effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of building energy conservation, and particularly to a method for supplementing and calculating building energy consumption data. Background Art

[0002] In recent years, there have been more and more large and medium-sized buildings in each city, and the energy consumption of a single building has been increasing year by year. It has become extremely important to monitor and analyze the continuously complete, true and effective energy consumption data collected from buildings in real time. Through practical observation, it is very difficult to collect continuously complete, true and effective energy consumption data from buildings. Various factors such as damage and replacement of monitoring instruments, network failures, equipment crashes, and power outages will cause the data collected for building energy consumption to be discontinuous and incomplete, and many unexpected error data will also be generated. How to supplement and calculate the abnormally missing data in the middle has become extremely important. At present, on the market, there are mainly various methods such as the average method, the clustering method, and the ratio method to realize the derivation of data supplementation and calculation. These methods cannot well reflect the actual energy consumption usage of various types of buildings, resulting in relatively large deviations between the supplemented and calculated data and the actual generated data.

[0003] Therefore, the method for supplementing and calculating building energy consumption data in the prior art has the technical problem of large errors. Summary of the Invention

[0004] A method for supplementing and calculating building energy consumption data provided by the present invention solves the technical problem of large errors in the method for supplementing and calculating building energy consumption data in the prior art.

[0005] Some implementation schemes for solving the above technical problems include:

[0006] A method for supplementing and calculating building energy consumption data includes the following steps:

[0007] Determine the position where the instrument data is missing, judge the starting point and the ending point of the missing instrument data, and record the m missing energy consumption data between the starting point and the ending point as e j (j ∈ (1, 2, 3,..., m)), and record the starting point instrument value Es and the ending point instrument value Ee of the collected data, and judge whether the trend of the instrument value data has changed according to the following rules; j (j ∈ (1, 2, 3,..., m)), and record the starting point instrument value Es and the ending point instrument value Ee of the collected data, and judge whether the trend of the instrument value data has changed according to the following rules;

[0008] If Es = Ee, it means that no energy consumption has occurred during the data missing period, and all missing energy consumption data are assigned a value of 0, that is, e j = 0 (j ∈ (1, 2, 3,..., m));

[0009] If Es ≠ Ee, first obtain the temperature information T of the day when the data is missing i and the holiday information H i, and filter out all temperature information T that meets the same interval from the data marked as W i and holiday information H i from n sample data e i，j (i ∈ (1, 2, 3,..., n), j ∈ (1, 2, 3,..., m)); the n sample data in this set of data are energy consumption data arranged according to time sequence, and the sample data closer to the current time has a greater impact on the trend of the data. m is the m missing energy consumption data between Es and Ee;

[0010] Calculate the trend factor of e j by the trend determination method:

[0011]

[0012] Ef j is the trend factor corresponding to the jth energy consumption data in the m missing data;

[0013] α is the weight factor, α ∈ (0, 1);

[0014] n is the number of sample data;

[0015] i is a variable;

[0016] e i，j are n sample data that all meet the same interval of temperature information T i and holiday information H i ;

[0017] If the trend of the instrument value data changes, it indicates that there is an instrument replacement or the instrument has been damaged and repaired during the data missing period. Then the energy consumption data is directly assigned the trend factor, that is, e j = EF j (j ∈ (1, 2, 3,..., m));

[0018] If the trend of the instrument data value does not change, it means that the instrument fails to collect the intermediate energy consumption data due to some reason. At this time, the calculation formula of e j is as follows:

[0019]

[0020] According to the calculated value of the energy consumption data e j , the corresponding instrument data E j at the corresponding time point can be calculated:

[0021]

[0022] Fill the calculated E j value into the corresponding data missing position.

[0023] Preferably, the data acquisition method marked as W includes:

[0024] Divide the time of a day into unit time t of a certain length. The setting of the unit time t must be greater than or equal to the data acquisition frequency of the instrument to ensure that at least one instrument data acquisition is performed in each unit time t under the condition of normal transmission of instrument data;

[0025] For the time t i The instrument data E collected i , if t i+1 also collects the instrument data E i+1 , then the energy consumption data e at the time point of t i can be calculated i = E i+1 - E i , that is, the instrument data is the data displayed in real time on the instrument at a certain moment, and the energy consumption data is the difference between the instrument data at different time points;

[0026] Load historical instrument data. If multiple instrument data are collected within a unit time t, only one of them is retained;

[0027] If the instrument data collected on the same day is continuous and complete within each unit time t, mark the instrument data of that day as W, and record the daily temperature information interval T i and holiday information H i .

[0028] Preferably, the data marked as W also includes the daily temperature data and the data on whether it is a legal holiday on the same day.

[0029] Preferably, the daily temperature data is classified according to the following rules:

[0030] T1: The average daily temperature is less than 10°C;

[0031] T2: The average daily temperature is greater than or equal to 10°C and less than or equal to 22°C;

[0032] T3: The average daily temperature is greater than 22°C.

[0033] Preferably, the data on whether it is a legal holiday on the same day is classified according to the following rules:

[0034] H1: The day is a working day;

[0035] H2: The day is a holiday.

[0036] Preferably, load historical instrument data. If multiple instrument data are collected within a unit time t, only one of them is retained, and the remaining data is deleted.

[0037] Preferably, if it is determined according to a preset rule that there is no data or there is incorrect data within a unit time, the data of that unit time is cleared and marked as missing meter data for that unit time.

[0038] Compared with the prior art, the present invention has the following advantages:

[0039] Through the observation of the generation rules and trends of a large number of various types of building energy consumption data, the present invention summarizes two influencing factors that have a greater impact on building energy consumption: temperature and holidays. And it continuously repeats a similar change rule on a daily basis. The present invention uses temperature and holidays as factors, and through the exponential smoothing average algorithm, it calculates the trends of the temperature data factor and the holiday data factor, thereby effectively improving the calculation accuracy of building energy consumption data. Specific embodiments

[0040] The specific embodiments shown below are intended to be descriptions of various configurations of the subject technology of the present invention, and are not intended to represent the only configurations in which the subject technology of the present invention can be practiced. The specific embodiments include specific details intended to provide a thorough understanding of the subject technology of the present invention. However, it will be clear and obvious to those skilled in the art that the subject technology of the present invention is not limited to the specific details shown herein, and can be practiced without these specific details.

[0041] It can be understood that in this article, relational terms such as "first" and "second" are intended to distinguish one entity or operation from another entity or operation, and are not intended to expressly or implicitly imply any actual relationship or order between these entities or operations.

[0042] The term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, such that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the phrase "comprising a..." does not exclude the existence of additional identical elements in the process, method, article or device comprising the element.

[0043] A method for calculating missing building energy consumption data includes the following steps:

[0044] Determine the positions where meter data is missing, determine the start point and end point of the missing meter data, and record the m missing energy consumption data between the start point and the end point as e j (j ∈ (1, 2, 3,..., m)), and the m missing meter data is recorded as E j(j ∈ (1, 2, 3,..., m)), record the instrument value Es at the starting point and the instrument value Ee at the ending point that have been collected, and determine whether the trend of the instrument value data has changed according to the following rules;

[0045] First, if Es = Ee, it means that the instrument did not consume energy during the data missing period. Assign all missing energy consumption data as 0, that is, e j = 0 (j ∈ (1, 2, 3,..., m));

[0046] Second, if Es ≠ Ee, first obtain the temperature information T i and holiday information H i on the day when the data is missing, and filter out all temperature information T i and holiday information H i that meet the same interval from the data marked as W, with n sample data e i，j (i ∈ (1, 2, 3,..., n), j ∈ (1, 2, 3,..., m)); The n sample data in this group are energy consumption data arranged in chronological order. The sample data closer to the current time has a greater impact on the data trend. m is the m missing energy consumption data between Es and Ee;

[0047] Calculate the trend factor of e j through the trend determination method. The calculation method of the trend factor is:

[0048]

[0049] Ef j is the trend factor corresponding to the j-th energy consumption data among the m missing data;

[0050] α is the weight factor, α ∈ (0, 1);

[0051] n is the number of sample data;

[0052] i is a variable;

[0053] e i，j are the n sample data that all meet the same interval of temperature information H i and holiday information H i ;

[0054] If the trend of the instrument value data changes, it means that there is an instrument replacement or the instrument has been repaired after being damaged during the data missing period. Then the energy consumption data is directly assigned as the trend factor, that is, e j = Ef j (j ∈ (1, 2, 3,..., m));

[0055] Thirdly, if the trend of the instrument data value remains unchanged, it indicates that the instrument fails to collect the intermediate energy consumption data due to some reason. At this time, e j The calculation formula is as follows:

[0056]

[0057] Based on the calculated value of the energy consumption data e j the instrument data E j at the corresponding time point can be calculated:

[0058]

[0059] The calculated value of E j is filled into the corresponding data missing position.

[0060] In some embodiments, the data acquisition method marked as W includes:

[0061] Divide the time of a day into unit time t of a certain length. The setting of the unit time t must be greater than or equal to the data acquisition frequency of the instrument to ensure that at least one instrument data acquisition occurs in each unit time t when the instrument data is transmitted normally;

[0062] For the instrument data E i acquired at time t i , if instrument data E i+1 is also acquired at t i+1 , the energy consumption data e i at time point t i can be calculated as e i+1 = E i - E i , that is, the instrument data is the data displayed in real time on the instrument at a certain moment, and the energy consumption data is the difference between the instrument data at different time points;

[0064] Load the historical instrument data. If multiple instrument data are acquired within a unit time t, only one of them is retained; If the instrument data collected on the same day is continuous and complete within each unit time t, mark the instrument data of that day as W, and record the temperature information interval T i and holiday information H

[0065] In some embodiments, the data marked as W also includes the daily temperature data and the data indicating whether it is a legal holiday on the same day.

[0066] In some embodiments, the daily temperature data is classified according to the following rules:

[0067] T1: The average temperature of the day is less than 10°C;

[0068] T2: The average temperature of the day is greater than or equal to 10°C and less than or equal to 22°C;

[0069] T3: The average temperature of the day is greater than 22°C.

[0070] In some embodiments, the data on whether the day is a legal holiday is classified according to the following rules:

[0071] H1: The day is a working day;

[0072] H2: The day is a holiday.

[0073] In some embodiments, when loading historical meter data, if multiple pieces of meter data are collected within a unit time t, only one piece of data is retained, and the rest of the data is deleted.

[0074] In some embodiments, if it is determined according to a preset rule that there is no data or there is incorrect data within a unit time, the data for that unit time is cleared and marked as missing meter data for that unit time.

[0075] Among them, the determination methods of the preset rules mainly include:

[0076] Extremely large data: Some of the collected electricity meter data is extremely large, ranging from hundreds of millions to trillions, and there is even electricity meter data with more than 100 digits.

[0077] Negative data: The data uploaded is negative, and there are intermittent and occasional occurrences.

[0078] Non-numeric data: The collected data has various combinations of letters and special characters, and no numbers can be extracted.

[0079] Empty data: The data is empty and no values can be extracted.

[0080] Large data mutation: The data suddenly becomes very large. For example, the data suddenly goes from tens of thousands to tens of millions, and then returns to normal in the next data packet, or returns to normal after several packets.

[0081] Small data mutation: The data suddenly becomes smaller, and the difference between the previous and the next data packets is negative. Then the next data packet returns to normal, or returns to normal after several packets.

[0082] Data jump: The data fluctuates greatly, with no discernible pattern.

[0083] Unchanged data: The same data packet is continuously transmitted, and the date and time do not change.

[0084] The technical solution of the present invention will be further introduced below:

[0085] The present invention is carried out according to the following steps:

[0086] Obtain the historical data of air temperature information and holiday information through the system interface, and divide the air temperature information into three categories:

[0087] T1: The average air temperature on the day of historical data is less than 10°C;

[0088] T2: The average air temperature on the day of historical data is greater than or equal to 10°C and less than or equal to 22°C;

[0089] T3: The average air temperature on the day of historical data is greater than 22°C.

[0090] Divide the holiday information into two categories:

[0091] H1: The day of historical data is a working day;

[0092] H2: The day of historical data is a holiday.

[0093] Divide the time of a day into unit time t of a certain length. The setting of unit time t must be greater than or equal to the data acquisition frequency of the instrument. This ensures that at least one instrument data acquisition is performed in each unit time when the instrument data is transmitted normally.

[0094] For the instrument data E collected at time t i if t i also collects the instrument data E i+1 then the energy consumption data e at time point t i+1 can be calculated as e i = E i - E i+1 - E i That is, the instrument data is the data displayed in real time on the instrument at a certain moment, and the energy consumption data is the difference between the instrument data at different time points.

[0095] Load the historical instrument data. If multiple instrument data are collected within a unit time, only one of the data needs to be retained, and the rest of the data are deleted.

[0096] If the instrument data collected on the day is continuous and complete within each unit time t (that is, at least one instrument data is collected within each unit time), then mark the instrument data of that day as W, and record the air temperature information interval T i and holiday information H i .

[0097] According to the preset rules, if there is no data or there is incorrect data within a unit time, then clear the data of that unit time and mark it as missing instrument data for that unit time.

[0098] The system traverses the positions of missing instrument data to determine the starting point and ending point of the missing instrument data, and records the m missing energy consumption data between the starting point and the ending point as e j (j ∈ (1, 2, 3,..., m)), and the m missing instrument data are recorded as E j (j ∈ (1, 2, 3,..., m)), and record the instrument value Es at the starting point and the instrument value Ee at the ending point that have been collected. And determine whether the trend of the instrument value data has changed.

[0099] If Es = Ee, it means that the instrument did not generate energy consumption during the data missing period, then assign all missing energy consumption data to 0, that is, e j = 0 (j ∈ (1, 2, 3,..., m)).

[0100] If Es ≠ Ee, first obtain the temperature information T i and holiday information H i on the day of data missing through the interface, and filter out all temperature information T i and holiday information H i that meet the same interval from the data marked as W, and obtain n sample data e i，j (i ∈ (1, 2, 3,..., n), j ∈ (1, 2, 3,..., m)). The n sample data in this group are energy consumption data arranged in chronological order. In this way, the sample data closer to the current time has a greater impact on the data trend. m is the m missing energy consumption data between Es and Ee.

[0101] Calculate the trend factor of e j by the trend determination method:

[0102]

[0103] Ef j is the trend factor corresponding to the jth energy consumption data among the m missing data. α is the weight factor, α ∈ (0, 1)

[0104] If the trend of the instrument value data changes, it means that there is an instrument replacement or the instrument has been repaired after being damaged during the data missing period. Then the energy consumption data is directly assigned to the trend factor, that is, e j = Ef j (j ∈ (1, 2, 3,..., m)).

[0105] If the trend of the instrument data value does not change, it means that the instrument only fails to collect the intermediate energy consumption data due to some reason. At this time, the calculation formula of e j is as follows:

[0106]

[0107] According to the calculated value of the energy consumption data e j the meter data E at the corresponding time point can be calculated j value:

[0108]

[0109] And fill the calculated value into the corresponding data missing position

[0110] The above introduces the technical solution of the present invention's theme and corresponding details. It can be understood that the above introduction is only some implementation schemes of the technical solution of the present invention's theme, and some details can also be omitted during its specific implementation

[0111] In addition, in some implementation schemes of the above invention, it is possible to combine multiple implementation schemes. Due to space limitations, various combination schemes are not listed one by one. Those skilled in the art can freely combine and implement the above implementation schemes according to needs during specific implementation to obtain a better application experience

[0112] When implementing the technical solution of the present invention's theme, those skilled in the art can obtain other detailed configurations according to the technical solution of the present invention's theme. Obviously, these details still fall within the scope covered by the technical solution of the present invention's theme without departing from the technical solution of the present invention's theme

Claims

1. A method for calculating building energy consumption data, characterized in that: The steps include: Determine the location where the instrument data is missing, determine the starting point and end point of the instrument data missing, and record the m missing energy consumption data between the starting point and the end point as e j (j∈(1,2,3,...,m)), the missing m instrument data are recorded as E j (j∈(1,2,3,...,m)), and record the collected starting point instrument value Es and end point instrument value Ee, and determine whether the instrument value data trend has changed according to the following rules; If Es = Ee, the meter did not consume any energy during the period when the data was missing, and all missing energy consumption data were assigned a value of 0, that is, e j =0(j∈(1,2,3,...,m)); If Es≠Ee, first obtain the temperature information T on the day when the data is missing i and holiday information i , and filter out all the temperature information T that meets the same interval in the data marked as W i and holiday information i n sample data e i，j (i∈(1,2,3,...,n),j∈(1,2,3,...,m)); the n sample data in this group of data are energy consumption data arranged in chronological order. The closer the sample data is to the current time, the more it can affect the trend of the data. m is the missing m energy consumption data between Es and Ee. Calculate e by trend determination method j The trend factor is calculated as follows: E j is the trend factor corresponding to the jth energy consumption data among the m missing data; α is the weight factor, α∈(0,1); n is the number of sample data; i is a variable; e i，j is the temperature information T that satisfies the same interval i and holiday information i n sample data of; If the trend of the instrument value data changes, it means that the instrument was replaced during the period of missing data or the instrument was damaged and repaired. In this case, the energy consumption data is directly assigned to the trend factor, that is, e j =Ef j (j∈(1,2,3,...,m)); If the trend of the instrument data value does not change, it means that the instrument has not collected the intermediate energy consumption data for some reason. j The calculation formula is as follows: According to the energy consumption data j The calculated value can be used to calculate the instrument data E at the corresponding time point j Values: The calculated E j Fill in the corresponding missing data positions.

2. The building energy consumption data compensation method according to claim 1 is characterized in that: The data acquisition method marked as W includes: Divide a day into a certain length of unit time t. The unit time t must be set to be greater than or equal to the instrument data collection frequency to ensure that the instrument data is collected at least once in each unit time t when the instrument data is transmitted normally. For time t i The instrument data collected i , if t i+1 Also collected instrument data E i+1 , then we can calculate t i Energy consumption data at time point i =E i+1 -E i , that is, the instrument data is the data displayed in real time on the instrument at a certain moment, and the energy consumption data is the difference between the instrument data at different time points; Load historical instrument data. If multiple instrument data are collected within a unit time t, one of the data is retained; If the instrument data collected on that day are continuous and complete within each unit time t, the instrument data of that day is marked as W, and the temperature information interval T of that day is recorded. i and holiday information i .

3. The building energy consumption data compensation method according to claim 2 is characterized in that: The data marked as W also includes temperature data of the day and data on whether the day is a statutory holiday.

4. The building energy consumption data compensation method according to claim 3 is characterized in that: The temperature data for the day are classified according to the following rules: Tx: The average temperature of the day is less than 10℃; T2: The average temperature of the day is greater than or equal to 10℃ and less than or equal to 22℃; T3: The average temperature of the day is greater than 22℃.

5. The building energy consumption data compensation method according to claim 3 is characterized in that: Whether the day is a statutory holiday or not is classified according to the following rules: H1: Today is a working day; H2: The day is a holiday.

6. The building energy consumption data supplementation method according to claim 2 is characterized in that: Load historical instrument data. If multiple instrument data are collected within a unit time t, one of the data is retained and the rest is deleted.

7. The building energy consumption data compensation method according to claim 2 is characterized in that: According to the preset rules, if there is no data or erroneous data in a unit time, the data of the unit time will be cleared and marked as missing instrument data for the unit time.

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