CN108427840B - Energy saving amount calculation method of data center air conditioning system based on reference energy efficiency prediction - Google Patents

Energy saving amount calculation method of data center air conditioning system based on reference energy efficiency prediction Download PDF

Info

Publication number
CN108427840B
CN108427840B CN201810194018.8A CN201810194018A CN108427840B CN 108427840 B CN108427840 B CN 108427840B CN 201810194018 A CN201810194018 A CN 201810194018A CN 108427840 B CN108427840 B CN 108427840B
Authority
CN
China
Prior art keywords
energy
air conditioning
conditioning system
sampling interval
energy consumption
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
CN201810194018.8A
Other languages
Chinese (zh)
Other versions
CN108427840A (en
Inventor
冉义兵
肖峰
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Jietong Wisdom Technology Co ltd
Original Assignee
Jietong Wisdom Technology Co ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Jietong Wisdom Technology Co ltd filed Critical Jietong Wisdom Technology Co ltd
Priority to CN201810194018.8A priority Critical patent/CN108427840B/en
Publication of CN108427840A publication Critical patent/CN108427840A/en
Application granted granted Critical
Publication of CN108427840B publication Critical patent/CN108427840B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F30/00—Computer-aided design [CAD]
    • G06F30/20—Design optimisation, verification or simulation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • General Physics & Mathematics (AREA)
  • Evolutionary Computation (AREA)
  • General Engineering & Computer Science (AREA)
  • Artificial Intelligence (AREA)
  • Computational Linguistics (AREA)
  • Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Computer Hardware Design (AREA)
  • Biomedical Technology (AREA)
  • Biophysics (AREA)
  • Geometry (AREA)
  • Data Mining & Analysis (AREA)
  • General Health & Medical Sciences (AREA)
  • Molecular Biology (AREA)
  • Computing Systems (AREA)
  • Mathematical Physics (AREA)
  • Software Systems (AREA)
  • Air Conditioning Control Device (AREA)

Abstract

The invention discloses a data center air conditioning system energy saving amount calculation method based on reference energy efficiency prediction, which comprises the following steps of: step 1, sampling the energy consumption of IT equipment sampled at the kth time and the energy consumption of an air conditioning system sampled at the kth time; step 2, calculating the energy consumption of IT equipment at the k-th sampling interval and the energy consumption of an air conditioning system at the k-th sampling interval; step 3, calculating the energy saving amount of the air conditioning system at the kth sampling interval; step 4, calculating the energy saving rate of the air conditioning system at the kth sampling interval; step 5, calculating the total energy saving amount of the air conditioning system in the energy-saving optimization period; and 6, calculating the average energy saving rate of the air conditioning system in the energy-saving optimization period. The invention objectively reflects the energy saving and the energy saving rate after the optimization measures are taken, has simple calculation and can calculate the energy saving and the energy saving rate of the sampling interval in real time.

Description

Energy saving amount calculation method of data center air conditioning system based on reference energy efficiency prediction
Technical Field
The invention relates to the technical field of energy saving amount identification, in particular to a data center air conditioning system energy saving amount calculation method based on benchmark energy efficiency prediction.
Background
The data center industry uses PUE (Power Usage efficiency, which is referred to as energy efficiency for short) to evaluate the infrastructure energy efficiency of the data center. PUE equals total facility energy consumption divided by IT equipment energy consumption.
Figure BSA0000160365920000011
Wherein EITRepresenting IT equipment energy consumption, ENonITRepresenting non-IT equipment energy consumption, PUE represents data center energy efficiency.
The data center is developed rapidly, and has huge energy consumption and high speed increasing speed. The average energy efficiency of the data center in China is 2.0-2.5. The energy consumption of the data center mainly comprises IT equipment energy consumption, air conditioning system energy consumption, power supply and distribution system energy consumption and other parts, wherein the air conditioning system energy consumption accounts for the main part of the non-IT equipment energy consumption and reaches more than 70%.
The energy saving of the data center is imperative and is commonly known in the industry, however, the energy efficiency optimization service of the data center is rare, and one of the main reasons is the lack of an objective and effective energy saving calculation method. The technical specification of public building energy-saving transformation JGJ176-2009 lists a measuring method, a bill analysis method and a calibration simulation method. The measuring method and the bill analysis method take the energy consumption of the system or equipment 1 year before transformation as a reference, and cannot be directly applied to projects which are operated for less than 1 year; generally, in the initial stage of operation, the load rate of a data center project is changed greatly, and the result is unreliable by directly comparing with the pre-base period of energy-saving transformation.
Disclosure of Invention
In view of the above defects in the prior art, the technical problem to be solved by the present invention is to provide a data center air conditioning system energy saving amount calculation method based on benchmark energy efficiency prediction, which objectively reflects the energy saving amount and the energy saving rate after the optimization measures are taken, has simple calculation, and can calculate the sampling interval energy saving amount and the energy saving rate in real time.
In order to achieve the aim, the invention provides a data center air conditioning system energy saving amount calculation method based on reference energy efficiency prediction, which comprises the following steps of:
step 1, collecting the energy consumption of a kth sampling IT device and the energy consumption of a kth sampling air-conditioning system;
step 2, calculating the energy consumption of IT equipment at the k-th sampling interval and the energy consumption of an air conditioning system at the k-th sampling interval;
step 3, calculating the energy saving amount of the air conditioning system at the kth sampling interval;
step 4, calculating the energy saving rate of the air conditioning system at the kth sampling interval;
step 5, calculating the total energy saving amount of the air conditioning system in the energy-saving optimization period;
and 6, calculating the average energy saving rate of the air conditioning system in the energy-saving optimization period.
Further, the calculating of the energy consumption of the IT equipment at the k-th sampling interval and the energy consumption of the air conditioning system at the k-th sampling interval in the step 2 is specifically as follows:
ΔEIT，k＝EIT，k-EIT，k-1
ΔEIT，kIT equipment energy consumption of the kth sampling interval, unit: kW.h;
EIT，kfor the kth miningSample IT equipment energy consumption measurement, unit: kW.h;
EIT，k-1the energy consumption measured value of the IT equipment is sampled at the k-1 time, and the unit is as follows: kW.h;
ΔEAC，k＝EAC，k-EAC，k-1
ΔEAC，kenergy consumption of the air conditioning system at the kth sampling interval is as follows: kW.h;
EAC，kthe unit of the k-th sampled energy consumption measured value of the air conditioning system is as follows: kW.h;
EAC，k-1the unit is the measured value of the energy consumption of the air conditioning system sampled at the k-1 th time: kW.h.
Further, the step 3 of calculating the energy saving amount of the air conditioning system at the kth sampling interval specifically includes:
Figure BSA0000160365920000021
Figure BSA0000160365920000022
for the kth sampling interval, the air conditioning system saves energy in the unit: kW.h;
Figure BSA0000160365920000023
predicting reference energy consumption for the k sampling interval air conditioner part, wherein the unit is as follows: kW.h, the calculation formula is
Figure BSA0000160365920000024
Figure BSA0000160365920000025
And predicting a reference energy efficiency prediction value for the k sampling interval air conditioning part.
Further, the step 4 of calculating the energy saving rate of the air conditioning system at the kth sampling interval specifically includes:
Figure BSA0000160365920000026
ζAC，kthe energy saving rate of the air conditioning system is set for the kth sampling interval;
ΔEAC，kenergy consumption of the air conditioning system at the kth sampling interval is as follows: kW.h;
Figure BSA0000160365920000031
for the kth sampling interval, the air conditioning system saves energy in the unit: kW.h.
Further, the step 5 of calculating the total energy saving amount of the air conditioning system in the energy saving optimization period specifically includes:
Figure BSA0000160365920000032
Figure BSA0000160365920000033
for the total energy saving of the air conditioning system in the energy-saving optimization period, the unit is as follows: kW.h;
Figure BSA0000160365920000034
for the kth sampling interval, the air conditioning system saves energy in the unit: kW.h.
Further, the step 6 of calculating the average energy saving rate of the air conditioning system in the energy saving optimization period specifically includes:
Figure BSA0000160365920000035
Figure BSA0000160365920000036
average energy-saving rate of the air conditioning system in the energy-saving optimization period;
EAC，OptimStartfor energy-saving optimization period initial air conditioning system energy consumption, unit: kW.h;
EAC，OptimEndoptimizing end of term for energy savingsEnergy consumption of air conditioning system, unit: kW.h.
Further, the k sampling interval air conditioning part predicts a reference energy efficiency predicted value
Figure BSA0000160365920000037
The obtaining method comprises the following steps:
firstly, historical sampling data is utilized, and an air conditioner part energy efficiency prediction model is obtained through a supervised learning method, wherein the formula is as follows:
Figure BSA0000160365920000038
the Model is a supervised learning Model, which is obtained by using historical sampling data through supervised learning training;
sample is input as a feature Sample;
Figure BSA0000160365920000039
predicting energy efficiency for the k-t sampling interval air conditioning section;
f () represents the mapping of model inputs and outputs;
taking design condition parameters of an air conditioning system in a data center as a characteristic sample of the prediction reference energy efficiency of an air conditioning part, namely:
Figure BSA00001603659200000310
the invention has the beneficial effects that:
the invention objectively reflects the energy saving and the energy saving rate after the optimization measures are taken, has simple calculation and can calculate the energy saving and the energy saving rate of the sampling interval in real time.
The conception, the specific structure and the technical effects of the present invention will be further described with reference to the accompanying drawings to fully understand the objects, the features and the effects of the present invention.
Drawings
FIG. 1 is a flow chart of the present invention.
Detailed Description
As shown in fig. 1, the method for calculating the energy saving amount of the data center air conditioning system based on the reference energy efficiency prediction includes the following steps:
step 1, collecting the energy consumption of a kth sampling IT device and the energy consumption of a kth sampling air-conditioning system;
step 2, calculating the energy consumption of IT equipment at the k-th sampling interval and the energy consumption of an air conditioning system at the k-th sampling interval;
step 3, calculating the energy saving amount of the air conditioning system at the kth sampling interval;
step 4, calculating the energy saving rate of the air conditioning system at the kth sampling interval;
step 5, calculating the total energy saving amount of the air conditioning system in the energy-saving optimization period;
and 6, calculating the average energy saving rate of the air conditioning system in the energy-saving optimization period.
In this embodiment, the calculating of the energy consumption of the IT equipment at the kth sampling interval and the energy consumption of the air conditioning system at the kth sampling interval in step 2 specifically includes:
ΔEIT，k＝EIT，k-EIT，k-1
ΔEIT，kIT equipment energy consumption of the kth sampling interval, unit: kW.h;
EIT，kfor the kth sample IT device energy consumption measurement, the unit: kW.h;
EIT，k-1the energy consumption measured value of the IT equipment is sampled at the k-1 time, and the unit is as follows: kW.h;
ΔEAC，k＝EAC，k-EAC，k-1
ΔEAC，kenergy consumption of the air conditioning system at the kth sampling interval is as follows: kW.h;
EAC，kthe unit of the k-th sampled energy consumption measured value of the air conditioning system is as follows: kW.h;
EAC，k-1the unit is the measured value of the energy consumption of the air conditioning system sampled at the k-1 th time: kW.h.
In this embodiment, the step 3 of calculating the energy saving amount of the air conditioning system at the kth sampling interval specifically includes:
Figure BSA0000160365920000041
Figure BSA0000160365920000042
for the kth sampling interval, the air conditioning system saves energy in the unit: kW.h;
Figure BSA0000160365920000043
predicting reference energy consumption for the k sampling interval air conditioner part, wherein the unit is as follows: kW.h, the calculation formula is
Figure BSA0000160365920000044
Figure BSA0000160365920000051
And predicting a reference energy efficiency prediction value for the k sampling interval air conditioning part.
In this embodiment, the step 4 of calculating the energy saving rate of the air conditioning system at the kth sampling interval specifically includes:
Figure BSA0000160365920000052
ζAC，kthe energy saving rate of the air conditioning system is set for the kth sampling interval;
ΔEAC，kenergy consumption of the air conditioning system at the kth sampling interval is as follows: kW.h;
Figure BSA0000160365920000053
for the kth sampling interval, the air conditioning system saves energy in the unit: kW.h.
In this embodiment, the step 5 of calculating the total energy saving amount of the air conditioning system in the energy saving optimization period specifically includes:
Figure BSA0000160365920000054
Figure BSA0000160365920000055
for the total energy saving of the air conditioning system in the energy-saving optimization period, the unit is as follows: kW.h;
Figure BSA0000160365920000056
for the kth sampling interval, the air conditioning system saves energy in the unit: kW.h.
In this embodiment, the step 6 of calculating the average energy saving rate of the air conditioning system in the energy saving optimization period specifically includes:
Figure BSA0000160365920000057
Figure BSA0000160365920000058
average energy-saving rate of the air conditioning system in the energy-saving optimization period;
EAC，OptimStartfor energy-saving optimization period initial air conditioning system energy consumption, unit: kW.h;
EAC，OptimEndfor energy-saving optimization period end air conditioning system energy consumption, unit: kW.h.
In this embodiment, the k-th sampling interval air conditioner part predicts a reference energy efficiency prediction value
Figure BSA0000160365920000059
The obtaining method comprises the following steps:
firstly, historical sampling data is utilized, and an air conditioner part energy efficiency prediction model is obtained through a supervised learning method, wherein the formula is as follows:
Figure BSA00001603659200000510
the Model is a supervised learning Model, which is obtained by using historical sampling data through supervised learning training;
sample is input as a feature Sample;
Figure BSA00001603659200000511
predicting energy efficiency for the k-t sampling interval air conditioning section;
f () represents the mapping of model inputs and outputs;
taking design condition parameters of an air conditioning system in a data center as a characteristic sample of the prediction reference energy efficiency of an air conditioning part, namely:
Figure BSA00001603659200000512
the invention objectively reflects the energy saving and the energy saving rate after the optimization measures are taken, has simple calculation and can calculate the energy saving and the energy saving rate of the sampling interval in real time.
The foregoing detailed description of the preferred embodiments of the invention has been presented. It should be understood that numerous modifications and variations could be devised by those skilled in the art in light of the present teachings without departing from the inventive concepts. Therefore, the technical solutions available to those skilled in the art through logic analysis, reasoning and limited experiments based on the prior art according to the concept of the present invention should be within the scope of protection defined by the claims.

Claims (2)

1. The energy-saving calculation method of the data center air conditioning system based on the reference energy efficiency prediction is characterized by comprising the following steps of:
step 1, collecting the energy consumption of a kth sampling IT device and the energy consumption of a kth sampling air-conditioning system;
step 2, calculating the energy consumption of the IT equipment at the k-th sampling interval and the energy consumption of the air conditioning system at the k-th sampling interval, specifically:
ΔEIT，k＝EIT，k-EIT，k-1
ΔEIT，kIT equipment energy consumption of the kth sampling interval, unit: kW.h;
EIT，kfor the kth sample IT device energy consumption measurement, the unit: kW.h;
EIT，k-1the energy consumption measured value of the IT equipment is sampled at the k-1 time, and the unit is as follows: kW.h;
ΔEAC，k＝EAC，k-EAC，k-1
ΔEAC，kenergy consumption of the air conditioning system at the kth sampling interval is as follows: kW.h;
EAC，kthe unit of the k-th sampled energy consumption measured value of the air conditioning system is as follows: kW.h;
EAC，k-1the unit is the measured value of the energy consumption of the air conditioning system sampled at the k-1 th time: kW.h;
step 3, calculating the energy saving amount of the air conditioning system at the kth sampling interval, specifically:
Figure FSB0000197957110000011
Figure FSB0000197957110000012
for the kth sampling interval, the air conditioning system saves energy in the unit: kW.h;
Figure FSB0000197957110000013
predicting reference energy consumption for the k sampling interval air conditioner part, wherein the unit is as follows: kW.h, the calculation formula is
Figure FSB0000197957110000014
Figure FSB0000197957110000015
Predicting a reference energy efficiency prediction value for the k sampling interval air conditioner part;
step 4, calculating the energy saving rate of the air conditioning system at the kth sampling interval, which specifically comprises the following steps:
Figure FSB0000197957110000016
ζAC，kthe energy saving rate of the air conditioning system is set for the kth sampling interval;
ΔEAC，kenergy consumption of the air conditioning system at the kth sampling interval is as follows: kW.h;
Figure FSB0000197957110000017
for the kth sampling interval, the air conditioning system saves energy in the unit: kW.h;
step 5, calculating the total energy saving amount of the air conditioning system in the energy-saving optimization period, specifically:
Figure FSB0000197957110000021
Figure FSB0000197957110000022
for the total energy saving of the air conditioning system in the energy-saving optimization period, the unit is as follows: kW.h;
Figure FSB0000197957110000023
for the kth sampling interval, the air conditioning system saves energy in the unit: kW.h;
step 6, calculating the average energy saving rate of the air conditioning system in the energy-saving optimization period, specifically:
Figure FSB0000197957110000024
Figure FSB0000197957110000025
average energy-saving rate of the air conditioning system in the energy-saving optimization period;
EAC，OptimStartfor energy-saving optimization period initial air conditioning system energy consumption, unit: kW.h;
EAC，OptimEndis a section ofThe energy consumption of the air conditioning system at the end of the optimal period can be optimized, and the unit is as follows: kW.h.
2. The method according to claim 1, wherein the k-th sampling interval air conditioning part predicts a predicted value of the reference energy efficiency
Figure FSB0000197957110000026
The obtaining method comprises the following steps:
firstly, historical sampling data is utilized, and an air conditioner part energy efficiency prediction model is obtained through a supervised learning method, wherein the formula is as follows:
Figure FSB0000197957110000027
the Model is a supervised learning Model, which is obtained by using historical sampling data through supervised learning training; sample is input as a feature Sample;
Figure FSB0000197957110000028
predicting energy efficiency for the k-t sampling interval air conditioning section; f () represents the mapping of model inputs and outputs;
taking design condition parameters of an air conditioning system in a data center as a characteristic sample of the prediction reference energy efficiency of an air conditioning part, namely:
Figure FSB0000197957110000029
CN201810194018.8A 2018-03-09 2018-03-09 Energy saving amount calculation method of data center air conditioning system based on reference energy efficiency prediction Active CN108427840B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN201810194018.8A CN108427840B (en) 2018-03-09 2018-03-09 Energy saving amount calculation method of data center air conditioning system based on reference energy efficiency prediction

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN201810194018.8A CN108427840B (en) 2018-03-09 2018-03-09 Energy saving amount calculation method of data center air conditioning system based on reference energy efficiency prediction

Publications (2)

Publication Number Publication Date
CN108427840A CN108427840A (en) 2018-08-21
CN108427840B true CN108427840B (en) 2022-04-12

Family

ID=63157929

Family Applications (1)

Application Number Title Priority Date Filing Date
CN201810194018.8A Active CN108427840B (en) 2018-03-09 2018-03-09 Energy saving amount calculation method of data center air conditioning system based on reference energy efficiency prediction

Country Status (1)

Country Link
CN (1) CN108427840B (en)

Families Citing this family (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109586285A (en) * 2018-12-06 2019-04-05 成都载信智能科技有限公司 Energy consumption mark post value calculating method, energy consumption management method and energy consumption management system
CN112747416B (en) * 2019-10-31 2022-04-05 北京国双科技有限公司 Energy consumption prediction method and device for air conditioning system
CN114429285A (en) * 2021-12-31 2022-05-03 博锐尚格科技股份有限公司 Energy-saving amount calculation method and device, electronic equipment and storage medium

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN102520679A (en) * 2011-11-07 2012-06-27 朱建斌 Energy saving data metering and calculating method
CN102711136A (en) * 2012-01-12 2012-10-03 李彬 Energy saving amount calculation method and system for communication base station
US20130190930A1 (en) * 2011-01-27 2013-07-25 International Business Machines Corporation Energy Saving Control for Data Center
CN104794269A (en) * 2015-04-09 2015-07-22 重庆大学 Energy-saving transformation energy-saving amount checking and ratifying method based on standard effect judgment
CN105959975A (en) * 2016-06-22 2016-09-21 湖南大学 Automatic assessment method for energy saving quantity of large-scale base station energy saving project
CN106874581A (en) * 2016-12-30 2017-06-20 浙江大学 A kind of energy consumption of air conditioning system in buildings Forecasting Methodology based on BP neural network model

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20130190930A1 (en) * 2011-01-27 2013-07-25 International Business Machines Corporation Energy Saving Control for Data Center
CN102520679A (en) * 2011-11-07 2012-06-27 朱建斌 Energy saving data metering and calculating method
CN102711136A (en) * 2012-01-12 2012-10-03 李彬 Energy saving amount calculation method and system for communication base station
CN104794269A (en) * 2015-04-09 2015-07-22 重庆大学 Energy-saving transformation energy-saving amount checking and ratifying method based on standard effect judgment
CN105959975A (en) * 2016-06-22 2016-09-21 湖南大学 Automatic assessment method for energy saving quantity of large-scale base station energy saving project
CN106874581A (en) * 2016-12-30 2017-06-20 浙江大学 A kind of energy consumption of air conditioning system in buildings Forecasting Methodology based on BP neural network model

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
Research of Airflow Distribution for Center Data Equipment Room Air Conditioning System;Shengliang Li等;《2012 International Conference on Computing, Measurement, Control and Sensor Network》;20120723;第311-314页 *
基于相似日法的空调***节能改造节能量计算方法;昂超;《暖通空调》;20161014;第46卷(第8期);第88-91页 *
大型数据中心机房新风空调节能研究;顾小杰等;《暖通空调》;20171015;第47卷(第10期);第55-61、119页 *

Also Published As

Publication number Publication date
CN108427840A (en) 2018-08-21

Similar Documents

Publication Publication Date Title
CN108427840B (en) Energy saving amount calculation method of data center air conditioning system based on reference energy efficiency prediction
US10545525B2 (en) Self-driving building energy engine
CN105143830B (en) Monitoring system
CN110889088B (en) Enterprise pollution discharge supervision method assisted by electric power model
US20130338948A1 (en) Methods and system for nonintrusive load monitoring
CN106910144B (en) Large building time-by-time energy consumption online prediction method based on time-division actual energy consumption coefficient
CN109935280B (en) Blast furnace molten iron quality prediction system and method based on ensemble learning
CN104075403A (en) Air-conditioning energy consumption monitoring and diagnosing system and method
CN105465946A (en) Method and system for energy consumption analysis of central air conditioner refrigeration station
CN107860102A (en) A kind of method and device for controlling central air-conditioning
CN107606745B (en) Metro Air conditioner season by when ring control energy consumption prediction technique
CN105959975B (en) Energy-saving automatic evaluation method for large-scale base station energy-saving project
CN109725219B (en) Automatic identification method for electric energy meter distribution area
CN111830350B (en) Energy consumption metering method and device and electric appliance
KR20130074043A (en) A method of forecasting of power demand using error correction algorithm and system for it
WO2023029545A1 (en) Central air conditioning system processing method and apparatus and electronic device
CN109738723B (en) Three-phase automatic identification method for electric energy meter
CN104089379A (en) Method for assessing efficiency of underground powerhouse ventilating and air conditioning system
CN113869809B (en) Energy-saving evaluation method and device for user behaviors of central heating system and terminal
CN108332355A (en) A kind of energy conserving system and power-economizing method for communication base station
CN103472297A (en) Electricity subentry measurement method
CN116951780A (en) Intelligent central water heater management method and system
CN105590148A (en) Information acquisition apparatus for energy management and method thereof
CN116938676A (en) Communication risk combined early warning method based on data source analysis
CN110762768A (en) Energy efficiency ratio prediction method and device for refrigeration host of central air-conditioning system

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant