CN112215474A - Running characteristic model for water chilling unit - Google Patents

Running characteristic model for water chilling unit Download PDF

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CN112215474A
CN112215474A CN202010988999.0A CN202010988999A CN112215474A CN 112215474 A CN112215474 A CN 112215474A CN 202010988999 A CN202010988999 A CN 202010988999A CN 112215474 A CN112215474 A CN 112215474A
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coefficient matrix
time
day
energy
water chilling
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汪雨清
卜震
张文宇
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Shanghai Building Science Research Institute Co Ltd
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Shanghai Building Science Research Institute Co Ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/067—Enterprise or organisation modelling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06312—Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/06—Electricity, gas or water supply

Abstract

The invention discloses an energy-consumption running characteristic model for a water chilling unit, which comprises the following steps of: selecting key influence factors according to the running energy consumption characteristics of the water chilling unit; determining basis energy feature vectors
Figure DDA0002690206100000011
The basic energy use feature vector
Figure DDA0002690206100000012
The characteristic vector of the energy consumption of the water chilling unit is used time by time in a typical working day; according to the basic energy utilization feature vector
Figure DDA0002690206100000013
Determining a key impact coefficient matrix, the key impact coefficient matrix comprising: hourly class impact coefficient matrix
Figure DDA0002690206100000014
Time of day class influence coefficient matrix
Figure DDA0002690206100000015
Seasonal category influence coefficient matrix
Figure DDA0002690206100000016
According to the basic energy utilization feature vector
Figure DDA0002690206100000017
The hour class impact coefficient matrix
Figure DDA0002690206100000018
The time of day class influence coefficient matrix
Figure DDA0002690206100000019
The seasonal category influence coefficient matrix
Figure DDA00026902061000000110
Obtaining the energy consumption operation characteristic model
Figure DDA00026902061000000111

Description

Running characteristic model for water chilling unit
Technical Field
The invention relates to the field of building intelligent monitoring energy data analysis, in particular to an energy operation characteristic model for an office building water chilling unit.
Background
At present, most building intelligent monitoring platforms are in a 'building without using' state, part of the building intelligent monitoring platforms are used as preliminary support in the aspects of energy consumption macroscopic statistics and decision, energy consumption standard limit determination and the like, few buildings really apply the established intelligent monitoring platforms to the operation management of the building energy consumption system, and the main reasons are explored: the method has the advantages that firstly, most building energy management personnel have professional knowledge limitation, and secondly, an effective big data analysis method is lacked.
The heating ventilation air-conditioning system is used as a building energy consumption 'big household', is always an object of key research and focusing in the industry, and the current research aiming at the running aspect of the energy of the office building water chiller mainly comprises the following steps: the method comprises the steps of testing and analyzing the operation parameters of the water chiller under typical working conditions, predicting and monitoring the electricity utilization abnormity of the water chiller by using non-invasive load monitoring (NILM) and other big data technologies, evaluating the energy efficiency of the whole water chiller and the like, but complete research on the aspect of comprehensively reflecting the change rule of the operation characteristic of the water chiller is not available. Therefore, how to realize the real-time adjustment of the operation of the water chilling unit on the basis of the intelligent platform of the office building, optimize the operation of the air conditioning water chilling unit and reduce the energy consumption waste of the cold source of the whole air conditioner is a problem to be solved urgently at present.
Disclosure of Invention
The invention aims to solve the technical problem of how to realize real-time adjustment of the operation of a water chilling unit on the basis of an intelligent platform of an office building, optimize the operation of the air conditioning water chilling unit and reduce the energy consumption waste of a cold source of the whole air conditioner, and provides an energy consumption operation characteristic model of the water chilling unit.
The invention solves the technical problems through the following technical scheme:
the method for establishing the energy-consumption operation characteristic model for the water chilling unit comprises the following steps of:
selecting key influence factors according to the running energy consumption characteristics of the water chilling unit;
determining basis energy feature vectors
Figure BDA0002690206080000021
The basic energy use feature vector
Figure BDA0002690206080000022
The characteristic vector of the energy consumption of the water chilling unit is used time by time in a typical working day;
according to the basic energy utilization feature vector
Figure BDA0002690206080000023
Determining a key impact coefficient matrix, the key impact coefficient matrix comprising: hourly class impact coefficient matrix
Figure BDA0002690206080000024
Time of day class influence coefficient matrix
Figure BDA0002690206080000025
Seasonal category influence coefficient matrix
Figure BDA0002690206080000026
According to the basic energy utilization feature vector
Figure BDA0002690206080000027
The hour class impact coefficient matrix
Figure BDA0002690206080000028
The time of day class influence coefficient matrix
Figure BDA0002690206080000029
The seasonal category influence coefficient matrix
Figure BDA00026902060800000210
Obtaining the energy consumption operation characteristic model
Figure BDA00026902060800000211
Preferably, the key influencing factors include: time category factor, day category factor, season category factor.
Further, the time category factors include: an operating period factor and a non-operating period factor; the day category factors include: a weekday factor and a non-weekday factor; the seasonal category factors include: summer, transition season and winter factors.
Preferably, the feature vector is used according to the basis
Figure BDA00026902060800000212
Determining the hourly class impact coefficient matrix
Figure BDA00026902060800000213
The time-by-time influence coefficient, the number of rows and columns, the diagonal and other partial element values.
Preferably, the time-by-time energy consumption feature data and the basic energy consumption feature vector are based on typical non-working days
Figure BDA00026902060800000214
Determining theTime of day class influence coefficient matrix
Figure BDA00026902060800000215
Preferably, the time-by-time energy consumption feature data and the basic energy consumption feature vector are based on typical working days of seasons
Figure BDA00026902060800000216
Determining the seasonal category impact coefficient matrix
Figure BDA00026902060800000217
Preferably, the seasonal typical weekday hourly availability feature data includes: the typical working day hourly use energy characteristic data of the transition season and the typical working day hourly use energy characteristic data of the winter; the seasonal category influence coefficient matrix
Figure BDA00026902060800000218
The method comprises the following steps: transition season influence coefficient matrix
Figure BDA00026902060800000219
And winter influence coefficient matrix
Figure BDA00026902060800000220
Preferably, the typical working day is a normal working day of an office building, and the typical non-working day is a normal rest day of the office building.
On the basis of the common knowledge in the field, the above preferred conditions can be combined randomly to obtain the preferred embodiments of the invention.
The positive progress effects of the invention are as follows: the energy consumption problem existing in the operation process of the office building water chilling unit is found in time, measures such as operation strategy adjustment and the like are taken in time, further waste of the energy consumption of the water chilling unit is avoided, the whole energy consumption of the building is reduced, and intelligent development of building energy conservation is promoted.
Drawings
FIG. 1 is a flow chart of a method in an embodiment of an operational characteristic model for a chiller according to the present invention;
FIG. 2 is a characteristic diagram of typical cycle variation and typical day variation after energy consumption data is normalized in an embodiment of an operational characteristic model for a chiller according to the present invention;
FIG. 3 is a basic energy consumption feature vector of an embodiment of an energy consumption feature model for a chiller according to the present invention;
FIG. 4 is an hour category impact coefficient matrix in an embodiment of an operational characteristic model for a chiller according to the present invention;
FIG. 5 is a day class impact coefficient matrix in an embodiment of an operational characteristic model for a chiller according to the present invention;
FIG. 6 is a transition season impact coefficient matrix in an embodiment of an operational characteristic model for a chiller according to the present invention;
fig. 7 is a winter influence coefficient matrix in an embodiment of an operational characteristic model for a chiller according to the present invention.
Fig. 8 is a typical winter non-working day operational characteristic model in an embodiment of the operational characteristic model for the water chiller according to the present invention.
Detailed Description
To facilitate an understanding of the present application, the present application will now be described more fully with reference to the accompanying drawings. Preferred embodiments of the present application are shown in the drawings. This application may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein in the description of the present application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
FIG. 1 is a flow chart of a method for constructing a model using operational features according to the present invention, wherein
S01: selecting key influence factors according to the running energy consumption characteristics of the water chilling unit;
in one example, as shown in fig. 2, the upper graph is a typical cycle cooler time-by-time electricity consumption graph in different seasons, and it can be seen that the electricity consumption data of the office building in different seasons are greatly different and reach a peak value in summer, while the electricity consumption data in transitional seasons are very small, the data in winter is almost 0, and in summer, the electricity consumption data of each day reaches a peak value in a certain time period and the electricity consumption data of the rest of time is almost 0; the lower graph is a time-by-time power consumption graph of the refrigerator in a typical working day in different seasons, which is a further explanation of the upper graph, and can be seen that the power consumption data of the refrigerator increases sharply from 7 am on the normal working day of an office building in summer until the peak value is reached about 9 am, then the state is continued until 6 pm, and then the power consumption data is reduced from the peak value to almost 0 quickly from 7 pm.
In one example, based on the operating energy consumption characteristics of the water chilling unit, the key influence factors of the operating energy consumption of the water chilling unit are judged, and the selection of the key influence factors comprises the following steps: time category factors including: an operating period factor and a non-operating period factor; a day category factor, the day category factor comprising: weekday factors and non-weekday factors; seasonal category factors including: summer factors, transition season factors, and winter factors. Wherein, the summer temperature range is: tsu is in the range of 25 ℃ and 40 DEG C](ii) a Transition section temperature range: t istr∈[10℃，25℃](ii) a Temperature range in winter: t iswi∈[-5℃，10℃]。
S02, determining a characteristic vector of the basic energy;
in one example, as shown in FIG. 3, a typical summer day hourly chiller energy feature vector is determined based on the summer chiller operating energy consumption standard data
Figure BDA0002690206080000041
Vector quantity
Figure BDA0002690206080000042
In the form of 1 column by 24 rows, each of the 24 rows corresponding to 24 hours of a typical working day, wherein
Figure BDA0002690206080000043
Respectively, energy consumption data for each hour of summer working days.
S03: determining a key influence coefficient matrix according to the basic energy utilization characteristic vector;
in one example, as shown in FIG. 4, an hour category impact coefficient matrix is determined
Figure BDA0002690206080000051
Energy feature vector in view of foundation
Figure BDA0002690206080000052
Is determined based on the time-by-time energy consumption data of typical working days in summer, so an hour category influence coefficient matrix is defined
Figure BDA0002690206080000053
An hour class influence coefficient matrix with a time-by-time influence coefficient of 1
Figure BDA0002690206080000054
Represented as a symmetric matrix of 24 columns by 24 rows with a value of 1 on the diagonal and all the others as 0.
In one example, as shown in FIG. 5, a time-of-day class impact coefficient matrix is determined
Figure BDA0002690206080000055
Analyzing the time-by-time energy use characteristic change of the typical non-working day and comparing the time-by-time energy use characteristic change with the time-by-time basic energy use characteristic data of the typical summer working day so as to determine a day class influence coefficient matrix
Figure BDA0002690206080000056
In one example, as shown in FIGS. 6 and 7, a matrix of seasonal category impact coefficients is determined
Figure BDA0002690206080000057
Analyzing the time-by-time energy use characteristic data of typical working days in transition seasons and winter, and comparing the time-by-time energy use characteristic data with the time-by-time basic energy use characteristic data of the typical working days in summer to determine a season category influence coefficient matrix
Figure BDA0002690206080000058
The seasonal category influence coefficient matrix is divided into a transitional seasonal influence coefficient matrix
Figure BDA0002690206080000059
As shown in fig. 6 and the winter influence coefficient matrix
Figure BDA00026902060800000510
As shown in fig. 7.
S04: acquiring the energy utilization running characteristic model;
in one example, as shown in FIG. 8, the energy use model
Figure BDA00026902060800000511
Can be expressed as follows:
Figure BDA00026902060800000512
wherein:
Figure BDA00026902060800000513
the mathematical expression symbol of the water chilling unit can operate the characteristic model;
Figure BDA00026902060800000514
basic energy consumption characteristic vectors of the water chilling unit;
Figure BDA00026902060800000515
an hour category impact coefficient matrix;
Figure BDA00026902060800000516
a day class influence coefficient matrix;
Figure BDA00026902060800000517
a seasonal category influence coefficient matrix, which is a winter influence coefficient matrix in fig. 8
Figure BDA00026902060800000518
While specific embodiments of the invention have been described above, it will be appreciated by those skilled in the art that this is by way of example only, and that the scope of the invention is defined by the appended claims. Various changes and modifications to these embodiments may be made by those skilled in the art without departing from the spirit and scope of the invention, and these changes and modifications are within the scope of the invention.

Claims (8)

1. The method for establishing the energy-consumption operation characteristic model for the water chilling unit is characterized by comprising the following steps of:
selecting key influence factors according to the running energy consumption characteristics of the water chilling unit;
determining basis energy feature vectors
Figure FDA0002690206070000011
The basic energy use feature vector
Figure FDA0002690206070000012
The characteristic vector of the energy consumption of the water chilling unit is used time by time in a typical working day;
according to the basic energy utilization feature vector
Figure FDA0002690206070000013
Determining a key impact coefficient matrix, the key impact coefficient matrix comprising: hourly class impact coefficient matrix
Figure FDA0002690206070000014
Time of day class influence coefficient matrix
Figure FDA0002690206070000015
Seasonal category influence coefficient matrix
Figure FDA0002690206070000016
According to the basic energy utilization feature vector
Figure FDA0002690206070000017
The hour class impact coefficient matrix
Figure FDA0002690206070000018
The time of day class influence coefficient matrix
Figure FDA0002690206070000019
The seasonal category influence coefficient matrix
Figure FDA00026902060700000110
Obtaining the energy consumption operation characteristic model
Figure FDA00026902060700000111
2. The operational characteristic model for the chiller according to claim 1, wherein the key influencing factors comprise: time category factor, day category factor, season category factor.
3. The operational characteristic model for the water chilling unit according to claim 2, wherein the time category factors include: an operating period factor and a non-operating period factor; the day category factors include: a weekday factor and a non-weekday factor; the seasonal category factors include: summer, transition season and winter factors.
4. The operational characteristic model for the water chilling unit according to claim 1, wherein the model is based on the basic energy usage characteristicEigenvector
Figure FDA00026902060700000112
Determining the hourly class impact coefficient matrix
Figure FDA00026902060700000113
The time-by-time influence coefficient, the number of rows and columns, the diagonal and other partial element values.
5. The operational characteristic model for the chiller according to claim 1, wherein the time-by-time energy consumption characteristic data and the basic energy consumption characteristic vector are based on typical non-working days
Figure FDA00026902060700000117
Determining the time-of-day class impact coefficient matrix
Figure FDA00026902060700000114
6. The operational characteristic model for the water chilling unit according to claim 1, wherein the time-by-time energy consumption characteristic data and the basic energy consumption characteristic vector are based on seasonal typical working days
Figure FDA00026902060700000115
Determining the seasonal category impact coefficient matrix
Figure FDA00026902060700000116
7. The operational characteristic model for the water chilling unit according to claim 1, wherein the seasonal typical weekday hourly consumption characteristic data includes: the typical working day hourly use energy characteristic data of the transition season and the typical working day hourly use energy characteristic data of the winter; the seasonal category influence coefficient matrix
Figure FDA0002690206070000021
The method comprises the following steps: transition season influence coefficient matrix
Figure FDA0002690206070000022
And winter influence coefficient matrix
Figure FDA0002690206070000023
8. The operational characteristic model for the water chilling unit according to any one of claims 1 to 7, wherein the typical working day is a normal working day of an office building, and the typical non-working day is a normal rest day of the office building.
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