CN117744896B - Green building total energy consumption analysis method - Google Patents
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- 238000005265 energy consumption Methods 0.000 title claims abstract description 24
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- 238000009435 building construction Methods 0.000 description 4
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Abstract
The invention relates to the technical field of data processing, and provides a green building total energy consumption analysis method, which comprises the following steps: collecting water consumption data and acquiring a water consumption data sequence; determining the length of a correction window of the water consumption data sequence, establishing a characteristic window of the water consumption data, determining the water consumption change slope, determining the relative contribution degree of the water consumption data, and determining the relative prediction weight of the water consumption data according to the relative contribution degree of all the water consumption data contained in the characteristic window of the water consumption data; according to the relative prediction weight and the characteristic window of the water consumption data, determining the prediction value of the first adjacent water consumption data behind the characteristic window of the water consumption data, determining the prediction correction ratio of the water consumption data by combining the water consumption data, determining the correction water consumption data at the prediction moment according to the prediction correction ratio of the water consumption data, and realizing the demand prediction of water resource use according to the correction water consumption data. The invention aims to solve the problem of inaccurate analysis of the total energy consumption of a green building.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to a green building total energy consumption analysis method.
Background
Green construction is a construction concept that focuses on minimizing adverse environmental impact during design, construction, and operation. The building design and operation mode aims at improving the resource utilization efficiency, reducing the energy consumption, reducing the waste generation and creating a healthier and more comfortable environment for residents and communities. The green building total energy consumption analysis method is mainly used for analyzing the energy consumption use condition of a building and aims to optimize the energy efficiency and reduce the environmental impact. The water resource use condition in the building energy consumption is very important, the water resource use condition of the building is analyzed and monitored, the water resource use requirement is further predicted, the water resource consumption condition in the building process can be known, potential waste or excessive use can be identified, and therefore important references are provided for the management and distribution of the water resource.
A weighted moving average algorithm can be used for predicting the water resource use requirement according to the water resource use condition of a building, but in the prediction process, a proper window length is required to be determined, the influence weight of data in the window on prediction data is set, the window length and the influence weight are selected as one of main factors influencing the accuracy of a final prediction result, and the prediction model is required to be determined according to the water resource data of the building so as to improve the accuracy of the prediction of the data.
Disclosure of Invention
The invention provides a green building total energy consumption analysis method, which aims to solve the problem that the influence weight in a setting window of the existing data prediction algorithm cannot be determined according to data, so that the data prediction result is inaccurate, and a large error is generated in the green building total energy consumption analysis, and adopts the following specific technical scheme:
One embodiment of the invention provides a green building total energy consumption analysis method, which comprises the following steps:
Collecting water consumption data, and acquiring a water consumption data sequence according to the time sequence of the water consumption data;
Determining the initial window length of water consumption data in a water consumption data sequence, determining the initial window of the water consumption data, determining the correction window length of the water consumption data sequence according to the initial window of the water consumption data, and establishing a characteristic window of each water consumption data according to the correction window length;
Determining a water consumption change slope of the water consumption data, determining a relative contribution degree of the water consumption data according to a characteristic window of the water consumption data, the water consumption change slope and a time interval between the water consumption change slope and a predicted value, and determining a relative predicted weight of the water consumption data according to the relative contribution degrees of all the water consumption data contained in the characteristic window of the water consumption data;
According to the relative prediction weight and the characteristic window of the water consumption data, determining the prediction value of the first adjacent water consumption data behind the characteristic window of the water consumption data, determining the prediction correction ratio of the water consumption data by combining the water consumption data, determining the correction water consumption data at the prediction moment according to the prediction correction ratio of the water consumption data, and realizing the demand prediction of water resource use according to the correction water consumption data.
Further, the specific method for acquiring the initial window of the water consumption data comprises the following steps:
and taking the water consumption data as a center, and recording a window with the length of a first preset threshold value as an initial window of the water consumption data.
Further, the specific method for acquiring the correction window length comprises the following steps:
In the method, in the process of the invention, A correction window length representing a water usage data sequence; /(I)Data representing water consumption/>Is extremely poor for all water usage data contained in the initial window of (a); /(I)Data representing water consumption/>The average value of all water consumption data contained in the initial window of (a); /(I)Data representing water consumption/>Variance of all water usage data contained in the initial window of (a); /(I)The number of all water usage data contained in the water usage data sequence is represented; /(I)Representing window parameters; /(I)Representing a linear normalization function; /(I)As a function of the value.
Further, the specific method for acquiring the characteristic window of the water consumption data comprises the following steps:
and taking the water consumption data as a center, and marking the window with the length of the correction window as a characteristic window of the water consumption data.
Further, the specific method for acquiring the water consumption data by using the water consumption change slope comprises the following steps:
and recording the ratio of the difference value between the adjacent water consumption data and the water consumption data before the water consumption data and the acquisition time interval as the water consumption change slope of the water consumption data.
Further, the determining the relative contribution degree of the water consumption data according to the characteristic window of the water consumption data, the water consumption change slope and the time interval between the water consumption change slope and the predicted value comprises the following specific methods:
In the method, in the process of the invention, Represents the/>First/>, contained within a characteristic window of personal water usage dataThe relative contribution of the individual water usage data; /(I)Represents the/>The average value of all water consumption data contained in the characteristic window of the individual water consumption data; /(I)Represents the/>First/>, contained within a characteristic window of personal water usage dataA time interval between the individual water usage data and the predicted value; /(I)Represents the/>First/>, contained within a characteristic window of personal water usage dataIndividual water usage data; /(I)Represents the/>First/>, contained within a characteristic window of personal water usage dataWater use change slope of individual water use data; /(I)Represents the/>Dividing the characteristic window of the individual water consumption data into a first/>The average value of the water change slope of all water consumption data except the individual water consumption data; /(I)An exponential function based on a natural constant is represented.
Further, the specific method for acquiring the relative prediction weight of the water consumption data comprises the following steps:
And (3) recording the ratio of the relative contribution degree of the water consumption data to the sum of the relative contribution degrees of all the water consumption data contained in the characteristic window of the water consumption data as the relative prediction weight of the water consumption data.
Further, the specific method for obtaining the prediction correction ratio of the water consumption data comprises the following steps:
and (3) recording the ratio of the predicted value of the water consumption data to the water consumption data as a predicted correction ratio of the water consumption data.
Further, the specific method for acquiring the correction water consumption data at the predicted time is as follows:
And (3) recording the product of the predicted correction ratio of the water consumption data at the predicted time and the water consumption data before the predicted time as the corrected water consumption data at the predicted time.
Further, the method for realizing the demand prediction of water resource use according to the corrected water consumption data comprises the following specific steps:
And replacing the water consumption data with the corrected water consumption data at the predicted time to realize the demand prediction of water resource use.
The beneficial effects of the invention are as follows:
Starting from the demand of predicting the demand of water resource use according to the water resource use condition of a building, aiming at the problem that the selection of window length and influence weight seriously influences the accuracy of a final prediction result, firstly, building a building according with the objective rule of water resource use in a long time, wherein the water consumption change trend and change rule at different moments have certain regularity and periodicity as the basis, and determining a characteristic window of water consumption data according to the local fluctuation degree of the water consumption data; then, when the water consumption data are predicted, the closer the predicted time is, the closer the work and rest and life laws of a user are to the existing habits of workers in building, the higher the weight is given to the water consumption data which are closer to the predicted time, the reference value of the water consumption data which are closer to the predicted time is improved, the relative predicted weight of the water consumption data is determined, and then the predicted value of the water consumption data is obtained according to a weighted moving average algorithm; finally, according to the characteristic that the change trend of the data has certain viscosity in a shorter time, namely, the change trend of the data keeps high similarity and consistency, the water consumption data at the prediction time is corrected according to the difference between the predicted value of the water consumption data and the water consumption data which are displayed at the previous time of the prediction time, the corrected water consumption data is obtained, the accuracy of data prediction is improved, the demand prediction for water resource use is realized according to the corrected water consumption data, and the problem that the influence weight in the setting window of the existing data prediction algorithm cannot be determined according to the data per se, so that the data prediction result is inaccurate, and a large error is generated in the total energy consumption analysis of the green building is solved.
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In order to more clearly illustrate the embodiments of the invention or the technical solutions of the prior art, the drawings which are used in the description of the embodiments or the prior art will be briefly described, it being obvious that the drawings in the description below are only some embodiments of the invention, and that other drawings can be obtained according to these drawings without inventive faculty for a person skilled in the art.
FIG. 1 is a schematic flow chart of a method for analyzing total energy consumption of a green building according to an embodiment of the present invention;
FIG. 2 is a flow chart for obtaining the corrected water consumption.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, a flowchart of a method for analyzing total energy consumption of a green building according to an embodiment of the invention is shown, the method includes the following steps:
And S001, collecting water consumption data, and acquiring a water consumption data sequence according to the time sequence of the water consumption data.
In order to obtain the water resource consumption in the energy consumption of the green building, a sensor for monitoring the water consumption of a water meter and the like is deployed in a water pipe network, and data acquired by the sensor are transmitted to an analysis system through an internet of things transmission technology. In this embodiment, the interval between the sensor and the water collection is 10 minutes, and one week is taken as one sampling period.
The data acquired by the sensor is the water consumption of the water pipe network in the interval time, and the data acquired by the sensor is recorded as the water consumption data.
And arranging all water consumption data acquired in one sampling period according to the acquired time sequence to acquire a water consumption data sequence.
So far, the water consumption data sequence is obtained.
Step S002, determining the initial window length of the water consumption data in the water consumption data sequence, determining the initial window of the water consumption data, determining the correction window length of the water consumption data sequence according to the initial window of the water consumption data, and establishing the characteristic window of each water consumption data according to the correction window length.
In a short time, the water resource is used intermittently in the building process of the green building, but not all times, so that the water consumption change trend and change rule at different moments are different in a short time, but have certain regularity and periodicity in a long time due to the objective rule of building construction. Due to the presence of regularity and periodicity, the water usage data can be predicted using a weighted moving average algorithm.
When the weighted moving average algorithm is used for predicting the water consumption data, a proper window is needed to be selected, and then different weights are given to different water consumption data according to the water consumption data in the window, so that the data in the window are weighted and averaged to obtain a more accurate predicted value.
The water demand for building construction is different in different time periods, and the water demand is related to life and work and rest laws of workers in building construction, so that it is necessary to determine the window length according to the trend of the water consumption data. When the change of the water consumption data is large, a smaller window is needed to be selected, the accuracy of the change trend of the captured recent water consumption data is improved, and when the change of the water consumption data is small, a larger window is needed to be selected, so that noise data in the water consumption data can be filtered.
In this embodiment, 11 is selected as the initial window length of the water consumption data in the water consumption data sequence, and the implementer can set the window length according to the requirement. And marking the initial window length value of the water consumption data as a first preset threshold value, and determining the initial window of the water consumption data in the water consumption data sequence according to the initial window length.
The initial window for acquiring water usage data contains the range, variance, and mean of all water usage data.
And determining the correction window length of the water consumption data sequence according to the initial window of the water consumption data.
In the method, in the process of the invention,A correction window length representing a water usage data sequence; /(I)Data representing water consumption/>Is extremely poor for all water usage data contained in the initial window of (a); /(I)Data representing water consumption/>The average value of all water consumption data contained in the initial window of (a); /(I)Data representing water consumption/>Variance of all water usage data contained in the initial window of (a); /(I)The number of all water usage data contained in the water usage data sequence is represented; /(I)Representing window parameters, with an empirical value of 15; /(I)Representing a linear normalization function acting as a linear normalization value taking the value in brackets; /(I)As a function of the value, a rounded value is used to take the value in parentheses.
When the initial window of the water usage data contains the larger the difference and variance of all the water usage data, the larger the local fluctuation degree of the water usage data contained in the water usage data sequence is, the smaller the initial window should be given, and at this time, the smaller the correction window length of the water usage data sequence is.
And the correction window length of the water consumption data sequence is the correction window length of all the water consumption data contained in the water consumption data sequence, and the characteristic window of each water consumption data is established by taking the correction window length as the window length of the water consumption data.
So far, the characteristic window of the water consumption data is obtained.
And S003, determining a water consumption change slope of the water consumption data, determining a relative contribution degree of the water consumption data according to a characteristic window of the water consumption data, the water consumption change slope and a time interval between the water consumption change slope and a predicted value, and determining a relative predicted weight of the water consumption data according to the relative contribution degree of all the water consumption data contained in the characteristic window of the water consumption data.
And determining the weight of the water consumption data contained in the window of each water consumption data according to the correction window length of the water consumption data sequence.
When predicting the water consumption data, the closer to the prediction time, the closer to the existing habits of workers who construct the building are the user's work and life laws, so the water consumption data closer to the prediction time should be given higher weight, and the reference value of the water consumption data closer to the prediction time should be improved.
The slope determined by the water consumption data and the water consumption data adjacent to the previous one is recorded as the water consumption change slope of the water consumption data.
When the water use change slope of the water use data is closer to the water use change slope of other water use data in the window, the water use data can represent the average level of the water use data in the window, so the water use data should be given a greater weight.
And determining the relative contribution degree of the water consumption data according to the characteristic window of the water consumption data, the water consumption change slope and the time interval between the water consumption change slope and the predicted value.
In the method, in the process of the invention,Represents the/>First/>, contained within a characteristic window of personal water usage dataThe relative contribution of the individual water usage data; /(I)Represents the/>The average value of all water consumption data contained in the characteristic window of the individual water consumption data; /(I)Represents the/>First/>, contained within a characteristic window of personal water usage dataA time interval between the individual water usage data and the predicted value; /(I)Represents the/>First/>, contained within a characteristic window of personal water usage dataIndividual water usage data; /(I)Represents the/>First/>, contained within a characteristic window of personal water usage dataWater use change slope of individual water use data; /(I)Represents the/>Dividing the characteristic window of the individual water consumption data into a first/>The average value of the water change slope of all water consumption data except the individual water consumption data; /(I)An exponential function based on a natural constant is represented.
When the time interval between the water consumption data and the predicted value is shorter, the water consumption data has a water consumption change slope which is closer to the water consumption change slope of other water consumption data in the window, and the relative contribution degree of the water consumption data is larger, at this time, the water consumption data should be given a higher weight, and the reference value of the water consumption data is improved.
And determining the relative prediction weight of the water consumption data according to the relative contribution degree of all the water consumption data contained in the characteristic window of the water consumption data.
In the method, in the process of the invention,Represents the/>First/>, contained within a characteristic window of personal water usage dataRelative predictive weights of the individual water usage data; /(I)Represents the/>First/>, contained within a characteristic window of personal water usage dataThe relative contribution of the individual water usage data; /(I)Represents the/>The amount of water usage data contained within the characteristic window of the individual water usage data.
In the same characteristic window, when the relative contribution degree of the water consumption data is larger, the relative prediction weight of the water consumption data is larger, and at the moment, the water consumption data is given higher weight, so that the reference value of the water consumption data is improved.
So far, the relative prediction weight of the water consumption data is obtained.
Step S004, according to the relative prediction weight and the characteristic window of the water consumption data, determining the prediction value of the first adjacent water consumption data behind the characteristic window of the water consumption data, determining the prediction correction ratio of the water consumption data in combination with the water consumption data, determining the correction water consumption data at the prediction moment according to the prediction correction ratio of the water consumption data, and realizing the demand prediction of water resource use according to the correction water consumption data.
And determining a predicted value of the first adjacent water consumption data after the characteristic window of the water consumption data according to the water consumption data in the characteristic window of the water consumption data by using a weighted moving average algorithm by taking the corrected window length as the window length of the water consumption data and taking the relative predicted weight of the water consumption data as the influence weight of the water consumption data in the window.
Because the change trend of the data has certain viscosity in a shorter time, namely the change trend of the data keeps high similarity and consistency, the water consumption data at the prediction time can be corrected according to the difference between the predicted value of the water consumption data and the water consumption data, which are shown at the previous time of the prediction time, so that the accuracy of data prediction is improved.
And (3) recording the ratio of the predicted value of the water consumption data to the water consumption data as a predicted correction ratio of the water consumption data.
When the prediction correction ratio of the water usage data is larger, the larger the difference between the predicted value of the water usage data obtained by using the weighted moving average algorithm and the water usage data is, the more the water usage data at the predicted time is required to be adjusted.
And determining the corrected water consumption data at the predicted time according to the predicted correction ratio of the water consumption data.
In the method, in the process of the invention,Correction water consumption data indicating a predicted time; /(I)Water consumption data indicating a predicted time; /(I)The predictive correction ratio of the water consumption data immediately before the predicted time is indicated.
When the prediction correction ratio of the water consumption data immediately before the prediction time is larger, the degree of adjustment of the water consumption data at the prediction time is larger, and the difference between the corrected water consumption data at the prediction time and the water consumption data at the prediction time is larger.
The flow chart of the corrected water consumption amount acquisition is shown in fig. 2.
The water consumption data at the prediction moment is replaced by the correction water consumption data at the prediction moment, so that the demand prediction of water resource use is realized, the analysis of the water consumption information of building construction can be facilitated, and reasonable and effective adjustment measures and management schemes can be adopted for the water resource further.
The foregoing description of the preferred embodiments of the present invention is not intended to be limiting, but rather, any modifications, equivalents, improvements, etc. that fall within the principles of the present invention are intended to be included within the scope of the present invention.
Claims (8)
1. The method for analyzing the total energy consumption of the green building is characterized by comprising the following steps of:
Collecting water consumption data, and acquiring a water consumption data sequence according to the time sequence of the water consumption data;
Determining the initial window length of water consumption data in a water consumption data sequence, determining the initial window of the water consumption data, determining the correction window length of the water consumption data sequence according to the initial window of the water consumption data, and establishing a characteristic window of each water consumption data according to the correction window length;
Determining a water consumption change slope of the water consumption data, determining a relative contribution degree of the water consumption data according to a characteristic window of the water consumption data, the water consumption change slope and a time interval between the water consumption change slope and a predicted value, and determining a relative predicted weight of the water consumption data according to the relative contribution degrees of all the water consumption data contained in the characteristic window of the water consumption data;
According to the relative prediction weight and the characteristic window of the water consumption data, determining the prediction value of the adjacent first water consumption data behind the characteristic window of the water consumption data, determining the prediction correction ratio of the water consumption data by combining the water consumption data, determining the correction water consumption data at the prediction moment according to the prediction correction ratio of the water consumption data, and realizing the demand prediction of water resource use according to the correction water consumption data;
The specific method for acquiring the correction window length comprises the following steps:
In the method, in the process of the invention, A correction window length representing a water usage data sequence; /(I)Data representing water consumption/>Is extremely poor for all water usage data contained in the initial window of (a); /(I)Data representing water consumption/>The average value of all water consumption data contained in the initial window of (a); Data representing water consumption/> Variance of all water usage data contained in the initial window of (a); /(I)The number of all water usage data contained in the water usage data sequence is represented; /(I)Representing window parameters; /(I)Representing a linear normalization function; /(I)Is a valued function;
the method for determining the relative contribution degree of the water consumption data according to the characteristic window of the water consumption data, the water consumption change slope and the time interval between the water consumption change slope and the predicted value comprises the following specific steps:
In the method, in the process of the invention, Represents the/>First/>, contained within a characteristic window of personal water usage dataThe relative contribution of the individual water usage data; Represents the/> The average value of all water consumption data contained in the characteristic window of the individual water consumption data; /(I)Represents the/>First/>, contained within a characteristic window of personal water usage dataA time interval between the individual water usage data and the predicted value; /(I)Represents the/>First/>, contained within a characteristic window of personal water usage dataIndividual water usage data; /(I)Represents the/>First/>, contained within a characteristic window of personal water usage dataWater use change slope of individual water use data; /(I)Represents the/>Dividing the characteristic window of the individual water consumption data into a first/>The average value of the water change slope of all water consumption data except the individual water consumption data; /(I)An exponential function based on a natural constant is represented.
2. The method for analyzing the total energy consumption of the green building according to claim 1, wherein the specific method for acquiring the initial window of the water consumption data is as follows:
and taking the water consumption data as a center, and recording a window with the length of a first preset threshold value as an initial window of the water consumption data.
3. The method for analyzing the total energy consumption of the green building according to claim 1, wherein the specific method for acquiring the characteristic window of the water consumption data is as follows:
and taking the water consumption data as a center, and marking the window with the length of the correction window as a characteristic window of the water consumption data.
4. The method for analyzing the total energy consumption of the green building according to claim 1, wherein the specific method for acquiring the water consumption data by using the water consumption change slope is as follows:
and recording the ratio of the difference value between the adjacent water consumption data and the water consumption data before the water consumption data and the acquisition time interval as the water consumption change slope of the water consumption data.
5. The method for analyzing the total energy consumption of the green building according to claim 1, wherein the specific method for acquiring the relative prediction weight of the water consumption data is as follows:
And (3) recording the ratio of the relative contribution degree of the water consumption data to the sum of the relative contribution degrees of all the water consumption data contained in the characteristic window of the water consumption data as the relative prediction weight of the water consumption data.
6. The method for analyzing the total energy consumption of the green building according to claim 1, wherein the specific method for obtaining the prediction correction ratio of the water consumption data is as follows:
and (3) recording the ratio of the predicted value of the water consumption data to the water consumption data as a predicted correction ratio of the water consumption data.
7. The method for analyzing the total energy consumption of the green building according to claim 1, wherein the specific method for acquiring the corrected water consumption data at the predicted time is as follows:
And (3) recording the product of the predicted correction ratio of the water consumption data at the predicted time and the water consumption data before the predicted time as the corrected water consumption data at the predicted time.
8. The method for analyzing the total energy consumption of the green building according to claim 1, wherein the method for realizing the demand prediction for the use of water resources according to the corrected water consumption data comprises the following specific steps:
And replacing the water consumption data with the corrected water consumption data at the predicted time to realize the demand prediction of water resource use.
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| KR101139161B1 (en) * | 2010-12-01 | 2012-04-26 | 한국수자원공사 | Simulator for forecasting short-term demand of water |
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| CN117294019A (en) * | 2023-10-11 | 2023-12-26 | 中铁十四局集团建筑工程有限公司 | Environment-friendly building energy consumption monitoring method and system based on Internet of things |
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Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR101139161B1 (en) * | 2010-12-01 | 2012-04-26 | 한국수자원공사 | Simulator for forecasting short-term demand of water |
| CN116739383A (en) * | 2023-06-30 | 2023-09-12 | 浙江东鸿电子股份有限公司 | Charging pile power load prediction evaluation method based on big data |
| CN116579506A (en) * | 2023-07-13 | 2023-08-11 | 陕西通信规划设计研究院有限公司 | Building energy consumption data intelligent management method and system based on big data |
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