CN109409617A - A kind of public building the Environmental Factor Prediction uncertainty electricity consumption robust Optimal methods - Google Patents
A kind of public building the Environmental Factor Prediction uncertainty electricity consumption robust Optimal methods Download PDFInfo
- Publication number
- CN109409617A CN109409617A CN201811438136.5A CN201811438136A CN109409617A CN 109409617 A CN109409617 A CN 109409617A CN 201811438136 A CN201811438136 A CN 201811438136A CN 109409617 A CN109409617 A CN 109409617A
- Authority
- CN
- China
- Prior art keywords
- robust
- air
- exponential
- car
- value
- 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.)
- Granted
Links
- 230000005611 electricity Effects 0.000 title claims abstract description 84
- 230000007613 environmental effect Effects 0.000 title claims abstract description 22
- 238000000034 method Methods 0.000 title claims abstract description 22
- 238000004378 air conditioning Methods 0.000 claims abstract description 44
- 238000005265 energy consumption Methods 0.000 claims abstract description 34
- 238000005457 optimization Methods 0.000 claims abstract description 7
- 238000005286 illumination Methods 0.000 claims description 37
- 238000001816 cooling Methods 0.000 claims description 14
- 241001465382 Physalis alkekengi Species 0.000 claims description 9
- 238000005057 refrigeration Methods 0.000 claims description 8
- 238000004364 calculation method Methods 0.000 claims description 7
- 239000011521 glass Substances 0.000 claims description 7
- 230000000694 effects Effects 0.000 claims description 6
- 230000017525 heat dissipation Effects 0.000 claims description 6
- 238000010606 normalization Methods 0.000 claims description 6
- 238000012546 transfer Methods 0.000 claims description 6
- 238000012423 maintenance Methods 0.000 claims description 5
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 5
- 230000004907 flux Effects 0.000 claims description 4
- 239000000203 mixture Substances 0.000 claims description 4
- 230000008569 process Effects 0.000 claims description 4
- 238000010276 construction Methods 0.000 claims description 3
- 230000001502 supplementing effect Effects 0.000 claims description 3
- 230000003287 optical effect Effects 0.000 claims 1
- 238000012216 screening Methods 0.000 claims 1
- 230000004044 response Effects 0.000 abstract description 6
- 230000003247 decreasing effect Effects 0.000 abstract description 2
- 230000008859 change Effects 0.000 description 3
- 238000009435 building construction Methods 0.000 description 2
- 230000002596 correlated effect Effects 0.000 description 2
- 230000000875 corresponding effect Effects 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 208000019901 Anxiety disease Diseases 0.000 description 1
- 240000002853 Nelumbo nucifera Species 0.000 description 1
- 235000006508 Nelumbo nucifera Nutrition 0.000 description 1
- 235000006510 Nelumbo pentapetala Nutrition 0.000 description 1
- ISWSIDIOOBJBQZ-UHFFFAOYSA-N Phenol Chemical compound OC1=CC=CC=C1 ISWSIDIOOBJBQZ-UHFFFAOYSA-N 0.000 description 1
- 230000001133 acceleration Effects 0.000 description 1
- 230000036506 anxiety Effects 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000013178 mathematical model Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 238000004088 simulation Methods 0.000 description 1
- 238000003786 synthesis reaction Methods 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
Classifications
-
- 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
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- 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—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/06—Energy or water supply
Landscapes
- Business, Economics & Management (AREA)
- Engineering & Computer Science (AREA)
- Economics (AREA)
- Human Resources & Organizations (AREA)
- Strategic Management (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Marketing (AREA)
- General Physics & Mathematics (AREA)
- General Business, Economics & Management (AREA)
- Tourism & Hospitality (AREA)
- Quality & Reliability (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Development Economics (AREA)
- Entrepreneurship & Innovation (AREA)
- Public Health (AREA)
- Water Supply & Treatment (AREA)
- General Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Elevator Control (AREA)
Abstract
The present invention proposes a kind of public building the Environmental Factor Prediction uncertainty electricity consumption robust Optimal methods, it establishes lighting system energy consumption model, air conditioning energy consumption model and elevator device energy consumption model.It establishes simultaneously so that the smallest optimization object function of electricity cost, and establishes its electricity consumption Robust Exponential and the comprehensive Robust Exponential of building for each electricity system respectively, the power load scheduling strategy that can reach expected robust level is made further according to Robust Exponential.The present invention analyzes uncertainty caused by environmental factor in public building demand response, reduces prediction data and real data deviation, while decreasing the loss of user, reduces electricity cost.
Description
Technical field
The invention belongs to intelligent power technical fields, and in particular to a kind of public building the Environmental Factor Prediction uncertainty use
Electric robust Optimal methods.
Background technique
As global resources and the dual-pressure of environment increasingly increase, efficiency problem has attracted the concern of more and more people.
The power industry in the whole world steps into the smart grid epoch, and the main feature of smart grid be exactly can comprehensively, reasonably
Electric power resource is configured, therefore it is proposed that the concept of intelligent power on energy management field.It is excellent in building electricity consumption
In change, intelligent power main purpose is that electricity cost is cut down under conditions of meeting power demand.According to statistics, public building conduct
The main composition of urban architecture, energy input account for nearly the 33% of social total energy consumption, therefore, excellent by the electricity consumption for studying public building
Change operation, to achieve the purpose that reduce building entirety electricity cost, there is biggish practical value.
It is more for the research of resident's intelligent power optimization problem at present, and demand response pipe is participated in public building
The analysis of reason is less.And this less analysis is since there are deviation, prediction data for many factors, prediction data and real data
Uncertainty may bring certain loss to user, the research for uncertain problem, at present mainly to electricity price and negative
Power load under lotus uncertain condition is controlled, but is not carried out for the uncertain robust Optimal methods of the Environmental Factor Prediction
It discusses.
Summary of the invention
Goal of the invention: the present invention proposes a kind of public building the Environmental Factor Prediction uncertainty electricity consumption robust Optimal methods,
Reduce the error of public building demand response prediction.
Technical solution: the present invention proposes a kind of public building the Environmental Factor Prediction uncertainty electricity consumption robust Optimal methods,
The following steps are included:
1) lighting system energy consumption model is established；
2) air conditioning energy consumption model is established；
3) elevator device energy consumption model is established；
4) constraint condition is set up according to the actual situation, is established so that the smallest optimization object function of electricity cost；
5) consider that the Environmental Factor Prediction is uncertain, establish it for lighting system, air-conditioning system and elevator device respectively
Respective electricity consumption Robust Exponential, and establish the comprehensive Robust Exponential of building.
It further include that step 6) makes the power load scheduling strategy that can reach expected robust level according to Robust Exponential.
Indoor average illumination can be calculated by the following formula in the step 1):
In formula, EavFor working face average illumination, unit lx；φ is the specified total light flux of light source in each lamps and lanterns, unit
For lm；N is lamps and lanterns number；U is illumination usage factor；A is face area, unit m2；K is lamps and lanterns maintenance factor,
Usually guarantee the comfortable working environment of human body, need for the intensity of illumination in environment to be maintained at certain level it
On, i.e., the stack result of natural lighting and artificial light source illumination is able to satisfy demand of the indoor occupant to light illumination, indoor comprehensive
Group photo angle value can be calculated by the following formula:
Es,τ=Eb,τ+ηeEe,τ
In formula, Es,τFor indoor comprehensive brightness value, unit lx；Eb,τIt carries out supplementing generated brightness value for artificial light,
Unit lx；Ee,τFor the illumination of natural light, unit lx；ηeFor natural light usage factor,
Therefore, public building internal unit time lighting load calculates as follows:
In formula, Pl,tThe power of additional illumination is carried out for lighting system t moment artificial light source；PlFor single artificial light source
Power；EsetFor object illumination value；EeFor natural light illumination, tworkFor the working time.
Indoor heat is passed to from outdoor by exterior wall and roof in the step 2):
Q1=KA (Tout-Tin)
In formula, Q is to be passed to indoor heat, W from outdoor by Fence structure；K is the heat transfer coefficient of Fence structure, unit
W/(m2·k)；A is the heat transfer area of Fence structure, unit m2；Tout、TinFor outdoor, room temperature, unit DEG C；
Enter the indoor solar radiant heat of air-conditioning by building glass window:
Q2=CaACsCiDjmaxCLQ
In formula, CaFor effective area coefficient；A is window areas, unit m2；CsFor shading coefficient；CiFor window internal sunshade facility
Shading coefficient；DjmaxFor maximum solar heat, unit W/m2；CLQFor glass pane cooling load coefficient；
Human-body radiating refrigeration duty:
Q3=qsnψCLQ
In formula, qsFor the mild quality of work adult man sensible heat heat dissipation capacity of different chamber, unit W；N is indoor total number of persons, and ψ is
Cluster coefficient, CLQFor sensible heat gain from human bodies cooling load coefficient；
Illuminator heat dissipation:
Q4=1000PlCLQ
In formula, PlFor power needed for illuminator, unit kW；
New wind load, it is the key that guarantee good indoor air quality that outdoor fresh air (fresh air) is introduced in air-conditioning system
Summer cooling load from outdoor air is calculated as follows:
Q5=M (hout-hin)
In formula, M is fresh air volume, units/kg/s；houtFor outdoor air enthalpy, unit kj/kg；hinFor room air enthalpy,
Unit kj/kg；
The enthalpy of air is the sum of the enthalpy of dry air and the enthalpy of vapor in humid air, includes sensible heat and latent heat:
H=1.01T+d (2500+1.84T)
In formula, d indicates water capacity, usually takes d=0.014kg/ (kg.dryair)；Fresh air volume needed for interior has with number
It closes, if everyone minimum fresh air figureofmerit is Rp, then M=Rp×n+Rb×s；RbMinimum fresh air requirmente standard needed for being every square metre；n
For occupancy；S is construction area；
To sum up, calculation of cooling load formula are as follows:
Q=KA (Tout-Tin)+CaACsCiDjmaxCLQ+qsnψCLQ+
1000PCLQ+M(Rp×n+Rb×S)(hout-hin)
Then air-conditioning system energy consumption calculation formula are as follows:
In formula, xa,tFor air-conditioning system t period electricity consumption, QtFor t moment refrigeration duty, COP is Performance for Air Conditioning Systems index.
In the step 3)
Elevator device energy consumption model when up peak mode are as follows:
ELF(τ)=365.32-1.115FP,τ-0.199Ncar,τ+0.143FP,τNcar,τ
+0.087FP,τ 2-0.228Ncar,τ 2
Flow of the people condition under the mode meets:
In formula, Pu,τIndicate uplink passenger flow total amount；Pd,τIndicate downlink passenger flow total amount；It indicates to enter electricity in building entrance hall
The volume of the flow of passengers of ladder and uplink；It indicates to leave the volume of the flow of passengers of elevator in building top；
Likewise, passenger is also flow of the people and elevator operation number of units coefficient the average latency as a result, can be by
Quadratic effect curved surface indicates that therefore, the average latency under this mode may be expressed as:
TAW(τ)=600.403-359.239ln (FP,τ)+86.113ln(Ncar,τ)
-57.995ln(FP,τNcar,τ)+62.096ln(FP,τ)2+36.007ln(Ncar,τ)2
The energy consumption model of elevator device when lowering peak mode are as follows:
ELF(τ)=- 523.806+30.355FP,τ-14.123Ncar,τ+0.241FP,τNcar,τ
-0.188FP,τ 2+0.774Ncar,τ 2
Flow of the people condition under the mode meets:
Average latency are as follows:
TAW(τ)=729.197-429.455ln (FP,τ)+99.519ln(Ncar,τ)
-54.286ln(FP,τNcar,τ)+69.213ln(FP,τ)2+29.390ln(Ncar,τ)2
The energy consumption model of elevator device when flat peak mode are as follows:
ELF(τ)=276.885-5.409FP,τ-4.1Ncar,τ+0.067FP,τNcar,τ
+0.058FP,τ 2+0.321Ncar,τ 2
Flow of the people condition under the mode meets:
Average latency are as follows:
TAW(τ)=622.589-352.932ln (FP,τ)+56.316ln(Ncar,τ)
-43.369ln(FP,τNcar,τ)+56.403ln(FP,τ)2+30.878ln(Ncar,τ)2。
Objective function in the step 4) are as follows:
In formula, ptFor t moment electricity price；xtFor t period lighting system, air-conditioning system and elevator device total electricity consumption；
Consider public building illumination comfort standard, temperature pleasant range and elevator average latency as constraint item
Part:
Emin≤ Eset≤ Emax
Tmin≤ Tset≤ Tmax
Xmin≤ Xset≤ Xmax。
Lighting system Robust Exponential in the step 5):
El.preFor the outdoor illuminance value under current time deterministic forecast, El,supCarry out with it is electrically optimized when assume
Outdoor illuminance value, RIlFor lighting system Robust Exponential, then:
El.pre–El,sup=RIl>=RIl,set
Outdoor illuminance value under current time interval prediction is [El.min,El.max], which is also El,supValue model
It encloses, then, the maximum value and minimum value of lighting system Robust Exponential are respectively as follows:
RIl,max=El.max–El,min
RIl,min=0
Air-conditioning system Robust Exponential:
Ta,preFor outdoor temp angle value under current time deterministic forecast, Ta,supFor carry out with it is electrically optimized when the outdoor assumed
Temperature value, RIaFor air-conditioning system Robust Exponential, then:
Ta,sup–Ta,pre=RIa>=RIa,set
Outdoor temp angle value under current time interval prediction is [Ta,min,Ta,max], which is also Ta,supValue model
It encloses, then, the maximum value and minimum value of air-conditioning system Robust Exponential are respectively as follows:
RIa,max=Ta,max–Ta,min
RIa,min=0
Elevator device Robust Exponential:
Fp,preFor indoor visitor flow rate value under current time deterministic forecast, Fp,supFor carry out with it is electrically optimized when the room assumed
Interior stream of people's magnitude, RIpFor elevator device Robust Exponential, then:
Fp,sup–Fp,pre=RIp>=RIp,set
Indoor visitor flow rate under current time interval prediction is [Fp,min,Fp,max], which is also Fp,supValue model
It encloses, then, the maximum value and minimum value of elevator device Robust Exponential are respectively as follows:
RIp,max=Fp,max–Fp,min
RIp,min=0
The unit of each electricity system Robust Exponential is not identical, it is difficult to compare robust level, therefore to each electricity system robust
Index is normalized, and value range is [0,1], and normalized process is as follows:
In formula, RINIt is the Robust Exponential value after normalization；RIrawIt is the original Robust Exponential value before normalization；RIminWith
RImaxThe minimum value and maximum value of respectively original Robust Exponential；
It is above-mentioned that Robust Exponential, the whole robust water of public building are established with regard to lighting system, air-conditioning system and elevator device
Flat to be assessed by establishing comprehensive robust index, comprehensive Robust Exponential can be indicated by the way that each system weight value is arranged:
In formula, δ indicates electricity system type；RIδIt is the Robust Exponential of the electricity system；vδIt is corresponding weight coefficient；
RImixIt is comprehensive Robust Exponential；In addition, constraining comprehensive Robust Exponential, make it not less than preset value RIreq, the constraint item
It is horizontal that part can reflect public building overall robustness.
The utility model has the advantages that the present invention analyzes uncertainty caused by environmental factor in public building demand response, reduce
Prediction data and real data deviation, while the loss of user is decreased, reduce electricity cost.
Detailed description of the invention
Fig. 1 is illuminance predicted value figure in outdoor used by example of the present invention；
Fig. 2 is outdoor temperature predicted value figure used by example of the present invention；
Fig. 3 is flow of the people predicted value figure used by example of the present invention.
Specific embodiment
Combined with specific embodiments below, the present invention is furture elucidated, it should be understood that these embodiments are merely to illustrate the present invention
Rather than limit the scope of the invention, after the present invention has been read, those skilled in the art are to various equivalences of the invention
The modification of form falls within the application range as defined in the appended claims.
A kind of public building the Environmental Factor Prediction uncertainty electricity consumption robust Optimal methods of the present invention, specifically include following step
Rapid: step 6) makes the power load scheduling strategy that can reach expected robust level according to Robust Exponential.
Step 1) analyzes the energy feature of the lighting system in public building, establishes lighting system energy consumption model.
Indoor average illumination can be calculated by the following formula:
In formula, EavFor working face average illumination, unit lx；φ is the specified total light flux of light source in each lamps and lanterns, unit
For lm；N is lamps and lanterns number；U is illumination usage factor；A is face area, unit m2；K is lamps and lanterns maintenance factor,
Usually guarantee the comfortable working environment of human body, need for the intensity of illumination in environment to be maintained at certain level it
On, i.e., the stack result of natural lighting and artificial light source illumination is able to satisfy demand of the indoor occupant to light illumination, indoor comprehensive
Group photo angle value can be calculated by the following formula:
Es,τ=Eb,τ+ηeEe,τ
In formula, Es,τFor indoor comprehensive brightness value, unit lx；Eb,τIt carries out supplementing generated brightness value for artificial light,
Unit lx；Ee,τFor the illumination of natural light, unit lx；ηeFor natural light usage factor.
Therefore, public building internal unit time lighting load calculates as follows:
In formula, Pl,tThe power of additional illumination is carried out for lighting system t moment artificial light source；PlFor single artificial light source
Power；EsetFor object illumination value；EeFor natural light illumination；tworkFor the working time.
Step 2) analyzes the energy feature of the air-conditioning system in public building, establishes air conditioning energy consumption model.
Further consider the energy consumption of air-conditioning system in summer public building, summer building internal heat main source has: logical
Cross solar radiant heat, human-body radiating that exterior wall and roof are passed to indoor heat, enter the room by building glass window from outdoor
Amount and illuminating and heat radiating.
And refrigeration duty is exactly to need air-conditioning system inside building in order to maintain or change room temperature inside public building
The cooling capacity of offer, calculation of cooling load is as follows,
Indoor heat is passed to from outdoor by exterior wall and roof:
Q1=KA (Tout-Tin)
In formula, Q is to be passed to indoor heat, W from outdoor by Fence structure；K is the heat transfer coefficient of Fence structure, unit
W/(m2·k)；A is the heat transfer area of Fence structure, unit m2；Tout、TinFor outdoor, room temperature, unit DEG C.
Enter the indoor solar radiant heat of air-conditioning by building glass window:
Q2=CaACsCiDjmaxCLQ
In formula, CaFor effective area coefficient；A is window areas, unit m2；CsFor shading coefficient；CiFor window internal sunshade facility
Shading coefficient；DjmaxFor maximum solar heat, unit W/m2；CLQFor glass pane cooling load coefficient.
Human-body radiating refrigeration duty:
Q3=qsnψCLQ
In formula, qsFor the mild quality of work adult man sensible heat heat dissipation capacity of different chamber, unit W；N is indoor total number of persons, and ψ is
Cluster coefficient, CLQFor sensible heat gain from human bodies cooling load coefficient.
Illuminator heat dissipation:
Q4=1000PlCLQ
In formula, PlFor power needed for illuminator, unit kW.
New wind load, it is the key that guarantee good indoor air quality that outdoor fresh air (fresh air) is introduced in air-conditioning system
Summer cooling load from outdoor air is calculated as follows:
Q5=M (hout-hin)
In formula, M is fresh air volume, units/kg/s；houtFor outdoor air enthalpy, unit kj/kg；hinFor room air enthalpy,
Unit kj/kg.
The enthalpy of air is the sum of the enthalpy of dry air and the enthalpy of vapor in humid air, includes sensible heat and latent heat:
H=1.01T+d (2500+1.84T)
In formula, d indicates water capacity, usually takes d=0.014kg/ (kg.dryair)；Fresh air volume needed for interior has with number
It closes, if everyone minimum fresh air figureofmerit is Rp, then M=Rp×n+Rb×s；RbMinimum fresh air requirmente standard needed for being every square metre；n
For occupancy；S is construction area.
To sum up, calculation of cooling load formula are as follows:
Q=KA (Tout-Tin)+CaACsCiDjmaxCLQ+qsnψCLQ+
1000PCLQ+M(Rp×n+Rb×S)(hout-hin)
Then air-conditioning system energy consumption calculation formula are as follows:
In formula, xa,tFor air-conditioning system t period electricity consumption, QtFor t moment refrigeration duty, COP is Performance for Air Conditioning Systems index.
Step 3) analyzes the energy feature of the elevator device in public building, establishes elevator device energy consumption model.
For Mr. Yu's public building, the disengaging time of building personnel and there is certain rule to the use habit of elevator
Property, therefore the moving model of building elevator system can be established based on passenger flow data, according to the variation pair by passenger flow above and below elevator
The operational mode of elevator is classified, and following a few quasi-modes can be divided into:
1) up peak mode: the uplink volume of the flow of passengers be much larger than the downlink volume of the flow of passengers, main passenger flow is up direction, i.e., all or
Most of personnel enter from building entrance hall, and take a lift uplink, this to be known as up peak mode, and up peak mode is generally sent out
Life is gone to work the period in the morning, and personnel largely flow into building construction and go to work.
2) lowering peak mode: main passenger flow direction is down direction, and main passenger is to take elevator from each layer of building
Downlink reaches building bottom and leaves building construction, this to be known as lowering peak mode, and lowering peak mode typically occurs in night
It comes off duty the period, personnel terminate darg, and seating elevator is concentrated to leave building.
3) flat peak mode: the uplink volume of the flow of passengers and the downlink volume of the flow of passengers are relatively stable, and difference is little, do not dominate passenger flow, passenger flow
Amount is smaller, and this operational mode is the basic operating condition of most of the time in building operating slot.
For general public building, in its operating slot, it is both provided with multiple elevator under normal conditions simultaneously
Operation, meanwhile, under same travel pattern, the operation energy consumption of elevator device and the size of flow of the people are directly related, therefore, in order to
The relationship for determining the electricity consumption of elevator device and flow of the people and elevator operation number of units under different Elevator traffic pattems, need it is clear with
Lower several points: 1) elevator electricity consumption is positively correlated with flow of the people, the increase of flow of the people, certainly will by increase elevator cycle of operation number and
The start and stop number of elevator, the acceleration of elevator and moderating process will consume a large amount of electric energy, that is, the start and stop number of elevator increases and will increase
Electricity consumption, therefore with the increase of flow of the people, elevator electricity consumption also increases with it；2) total electricity consumption and elevator of group elevator system
The elevator number of units run in group's system is positively correlated, and because elevator existence foundation runs power consumption and standby power consumption, increases elevator
Number of units is run, the total basic energy consumption and standby energy consumption that will lead to elevator device increase, therefore the electricity being currently running in elevator device
Halfpace number is more, and the overall operation energy consumption of elevator device is bigger；3) in flow of the people include the uplink volume of the flow of passengers and the downlink volume of the flow of passengers, on
Row is different with ratio with the quantity of downlink passenger flow, and the electricity consumption for also leading to elevator device is different, therefore should be according to traffic mould
The different of formula carry out elevator energy consumption modeling respectively.
According to above-mentioned several points, and the data obtained based on test, elevator device electricity consumption and flow of the people and elevator run platform
Several relationships can be described with a dihydric phenol mathematical model comprising interaction item:
ELF(τ)=a+b1FP,τ+b2Ncar,τ+b3FP,τNcar,τ+b4(FP,τ)2+b5(Ncar,τ)2
Wherein, ELF(τ) is elevator power consumption；FP,τAnd Ncar,τFor independent variable, respectively flow of the people and elevator runs number of units,
Elevator energy consumption is the result under the collective effect of the two different factors of flow of the people and elevator operation number of units.
The elevator energy consumption model of different mode is modeled below:
1) elevator device energy consumption model when up peak mode are as follows:
ELF(τ)=365.32-1.115FP,τ-0.199Ncar,τ+0.143FP,τNcar,τ
+0.087FP,τ 2-0.228Ncar,τ 2
Flow of the people condition under the mode meets:
In formula, Pu,τIndicate uplink passenger flow total amount；Pd,τIndicate downlink passenger flow total amount；It indicates to enter electricity in building entrance hall
The volume of the flow of passengers of ladder and uplink；It indicates to leave the volume of the flow of passengers of elevator in building top.
Likewise, passenger is also flow of the people and elevator operation number of units coefficient the average latency as a result, can be by
Quadratic effect curved surface indicates.Therefore, the average latency under this mode may be expressed as:
TAW(τ)=600.403-359.239ln (FP,τ)+86.113ln(Ncar,τ)
-57.995ln(FP,τNcar,τ)+62.096ln(FP,τ)2+36.007ln(Ncar,τ)2
2) when lowering peak mode elevator device energy consumption model are as follows:
ELF(τ)=- 523.806+30.355FP,τ-14.123Ncar,τ+0.241FP,τNcar,τ
-0.188FP,τ 2+0.774Ncar,τ 2
Flow of the people condition under the mode meets:
Average latency are as follows:
TAW(τ)=729.197-429.455ln (FP,τ)+99.519ln(Ncar,τ)
-54.286ln(FP,τNcar,τ)+69.213ln(FP,τ)2+29.390ln(Ncar,τ)2
3) when flat peak mode elevator device energy consumption model are as follows:
ELF(τ)=276.885-5.409FP,τ-4.1Ncar,τ+0.067FP,τNcar,τ
+0.058FP,τ 2+0.321Ncar,τ 2
Flow of the people condition under the mode meets:
Average latency are as follows:
TAW(τ)=622.589-352.932ln (FP,τ)+56.316ln(Ncar,τ)
-43.369ln(FP,τNcar,τ)+56.403ln(FP,τ)2+30.878ln(Ncar,τ)2
After public building elevator device elevator suggestion operation number of units is calculated, it can actually be run according to public building
Elevator newness degree, maximum functional duration, maintenance requirement and operational efficiency curve, according to the difference of elevator newness degree,
The priority of the height setting elevator operation of operational efficiency, and rotation operation is carried out to each lift facility, extend elevator and uses
Service life ensures lift running safety, formulates to specifically be run/be shut down control program to each elevator of mechanical floor,
So that elevator needed for the total number of units of elevator that day part operates normally meets elevator device runs number, public building electricity is being improved
While terraced running efficiency of system, it is horizontal to reduce elevator device overall energy consumption, while knowing according to statistical result, the anxiety journey of passenger
It spends with the growth of waiting time and the exponentially form increase of function, therefore can be evaluated using the average waiting time of passenger
Satisfaction of the passenger to elevator device.
Step 4) sets up constraint condition according to the actual situation, establishes so that the smallest optimization object function of electricity cost.
In order to keep total electricity bill expenditure minimum, objective function are as follows:
In formula, ptFor t moment electricity price；xtFor t period lighting system, air-conditioning system and elevator device total electricity consumption.
And 3) step 1), 2) modeling formula are the bound for objective function, while considering public building illumination
Comfort standard, temperature pleasant range and elevator average latency are as constraint condition:
Emin≤ Eset≤ Emax
Tmin≤ Tset≤ Tmax
Xmin≤ Xset≤ Xmax。
Step 5) considers that the Environmental Factor Prediction is uncertain, respectively for each electricity system establish its electricity consumption Robust Exponential with
And the comprehensive Robust Exponential of building.
Carry out with it is electrically optimized when, the Environmental Factor Prediction value can have a huge impact optimum results, thus quote Shandong
Stick index method analyzes the uncertainty of prediction, and Robust Exponential value is higher, then this is stronger with electrically optimized robustness, then not
Certainty factor negatively affected caused by optimum results it is smaller, Robust Exponential as additional constraint introduce electrically optimized constraint
In condition,
Lighting system Robust Exponential:
El.preFor the outdoor illuminance value under current time deterministic forecast, El,supCarry out with it is electrically optimized when assume
Outdoor illuminance value, RIlFor lighting system Robust Exponential, then:
El.pre–El,sup=RIl>=RIl,set
Outdoor illuminance value under current time interval prediction is [El.min,El.max], which is also El,supValue model
It encloses, then, the maximum value and minimum value of lighting system Robust Exponential are respectively as follows:
RIl,max=El.max–El,min
RIl,min=0
Air-conditioning system Robust Exponential:
Ta,preFor outdoor temp angle value under current time deterministic forecast, Ta,supFor carry out with it is electrically optimized when the outdoor assumed
Temperature value, RIaFor air-conditioning system Robust Exponential, then:
Ta,sup–Ta,pre=RIa>=RIa,set
Outdoor temp angle value under current time interval prediction is [Ta,min,Ta,max], which is also Ta,supValue model
It encloses, then, the maximum value and minimum value of air-conditioning system Robust Exponential are respectively as follows:
RIa,max=Ta,max–Ta,min
RIa,min=0
Elevator device Robust Exponential:
Fp,preFor indoor visitor flow rate value under current time deterministic forecast, Fp,supFor carry out with it is electrically optimized when the room assumed
Interior stream of people's magnitude, RIpFor elevator device Robust Exponential, then:
Fp,sup–Fp,pre=RIp>=RIp,set
Indoor visitor flow rate under current time interval prediction is [Fp,min,Fp,max], which is also Fp,supValue model
It encloses, then, the maximum value and minimum value of elevator device Robust Exponential are respectively as follows:
RIp,max=Fp,max–Fp,min
RIp,min=0
The unit of each electricity system Robust Exponential is not identical, it is difficult to compare robust level, therefore to each electricity system robust
Index is normalized, and value range is [0,1], and normalized process is as follows:
In formula, RINIt is the Robust Exponential value after normalization；RIrawIt is the original Robust Exponential value before normalization；RIminWith
RImaxThe minimum value and maximum value of respectively original Robust Exponential.
It is above-mentioned that Robust Exponential, the whole robust water of public building are established with regard to lighting system, air-conditioning system and elevator device
Flat to be assessed by establishing comprehensive robust index, comprehensive Robust Exponential can be indicated by the way that each system weight value is arranged:
In formula, δ indicates electricity system type；RIδIt is the Robust Exponential of the electricity system；vδIt is corresponding weight coefficient；
RImixIt is comprehensive Robust Exponential.In addition, constraining comprehensive Robust Exponential, make it not less than preset value RIreq, the constraint item
It is horizontal that part can reflect public building overall robustness.
Step 6) makes the power load scheduling strategy that can reach expected robust level according to Robust Exponential.
Example
In order to verify the feasibility of above-mentioned model, the present invention carries out Simulation Example analysis to a scene of hypothesis.Certain is public
Building working time section is 9:00-21:00,20 layers of building ground, 2 layers of underground altogether.It is illuminated, is shone using LED energy-saving lamp
Bright area is 17768m2, the lighting power of unit area is 3W, luminous flux 100lm/W, and illumination usage factor is 0.5, illumination
Maintenance factor is 0.8.Δ t=1h.The refrigeration gross area of the public building air-conditioning system is 17768m2, the external wall gross area
For 7000m2, average window-wall ratio is 0.38.The building are furnished with 8 elevators, separate unit power 24kW altogether.
Firstly, considering building external environment uncertainty in traffic and assessing the robust level of each system.Illumination system
The electricity charge united under different Robust Exponentials are as shown in table 1.Seen from table 1, the electricity charge of lighting system with Robust Exponential increase
And increase, this is because with the increase of lighting system Robust Exponential, the feasible zone of load optimal becomes smaller, to reduce economy
Property.
Electric cost expenditure of the air-conditioning system under different Robust Exponentials is as shown in table 2.As can be seen from Table 2, with air-conditioning system Shandong
The increase electric cost expenditure of stick index value increases with it, and so that building temperature is kept lower this is because needing to consume more electric energy
Level, so as to preferably response environment factor uncertainty in traffic problem.It follows that the promotion of system robustness level
Along with the reduction of economy.
For elevator device, analyzed only for up peak and lowering peak period.Table 3 gives elevator device and exists
Electricity charge when up peak under difference Robust Exponential, table 4 give elevator device in lowering peak under difference Robust Exponential
The electricity charge.By table 3 and table 4 as it can be seen that elevator device electric cost expenditure increases with the increase of Robust Exponential value.This is because robust refers to
Number is bigger, then can dominate that elevator number is more, meets the uplink and downlink requirement of more people, however will cause the increased consequence of the electricity charge.
The electricity charge of 1 lighting system of table under different Robust Exponentials
The electricity charge of 2 air-conditioning system of table under different Robust Exponentials
The electricity charge (uplink) of 3 elevator device of table under different Robust Exponentials
The electricity charge (downlink) of 4 elevator device of table under different Robust Exponentials
When considering comprehensive Robust Exponential, each system weight coefficient and comprehensive Robust Exponential value are set first, then to three
Kind different scenes are analyzed, and each electricity system weight coefficient is as shown in table 5, and Robust Exponential value is as shown in table 6, calculates different fields
The electricity charge are as shown in table 7 under scape.
The setting of the load weight coefficient of scene 1 and scene 2 is identical, and comprehensive Robust Exponential setting is different.By table 6 as it can be seen that comprehensive
Conjunction Robust Exponential setting is higher, and each system Robust Exponential value after optimization is bigger, and the electricity charge are consequently increased.This is because with comprehensive
It closes Robust Exponential setting to increase, each system occurs for preferably response environment factor uncertainty in traffic event, sacrifices economical
Property come promoted itself robust level.
The setting of the synthesis Robust Exponential of scene 2 and scene 3 is identical, and each electricity system weight coefficient setting is different.It can by table 6
Know, each electricity system weight setting difference changes each system Robust Exponential after optimization, this illustrates load weight
The difference of setting can have an impact electricity consumption optimum results.In addition, the electricity charge of scene 2 and scene 3 are higher than scene 1, this is because
To be that cost improves the comprehensive electricity consumption robust of building horizontal to sacrifice economy (paying more electricity charge) for scene 2 and scene 3.
Each system weight coefficient under 5 different scenes of table
Each system Robust Exponential value of table 6
The total electricity cost of table 7
In conclusion above with respect to the probabilistic public building electricity consumption robust optimisation strategy of the Environmental Factor Prediction is considered
Method is effective.Robust Exponential is established respectively according to the power consumption characteristics of lighting system, air-conditioning system and elevator device, and is built
The comprehensive Robust Exponential of building is found, to make the electricity system optimisation strategy that can achieve expected robust level.But this is excellent
Changing result is only merely the effect that theoretically may be implemented, and specific effect also needs practical application to examine.
The above emulation uses Cplex, and computer is Corei5 3.20Ghz, 4G RAM.
Claims (7)
1. a kind of public building the Environmental Factor Prediction uncertainty electricity consumption robust Optimal methods, which is characterized in that including following step
It is rapid:
1) lighting system energy consumption model is established；
2) air conditioning energy consumption model is established；
3) elevator device energy consumption model is established；
4) constraint condition is set up according to the actual situation, is established so that the smallest optimization object function of electricity cost；
5) consider that the Environmental Factor Prediction is uncertain, establish it respectively for lighting system, air-conditioning system and elevator device respectively
Electricity consumption Robust Exponential, and establish the comprehensive Robust Exponential of building.
2. public building the Environmental Factor Prediction uncertainty electricity consumption robust Optimal methods according to claim 1, feature
It is, further includes that step 6) makes the power load scheduling strategy that can reach expected robust level according to Robust Exponential.
3. public building the Environmental Factor Prediction uncertainty electricity consumption robust Optimal methods according to claim 1, feature
It is, indoor average illumination can be calculated by the following formula in the step 1):
In formula, EavFor working face average illumination, unit lx；φ is the specified total light flux of light source in each lamps and lanterns, unit lm；
N is lamps and lanterns number；U is illumination usage factor；A is face area, unit m2；K is lamps and lanterns maintenance factor,
Usually guarantee the comfortable working environment of human body, needs for the intensity of illumination in environment to be maintained on certain level, i.e.,
The stack result of natural lighting and artificial light source illumination is able to satisfy demand of the indoor occupant to light illumination, indoor comprehensive illumination
Value can be calculated by the following formula:
Es,τ=Eb,τ+ηeEe,τ
In formula, Es,τFor indoor comprehensive brightness value, unit lx；Eb,τIt carries out supplementing generated brightness value, unit for artificial light
lx；Ee,τFor the illumination of natural light, unit lx；ηeFor natural light usage factor,
Therefore, public building internal unit time lighting load calculates as follows:
In formula, Pl,tThe power of additional illumination is carried out for lighting system t moment artificial light source；PlFor the power of single artificial light source；
EsetFor object illumination value；EeFor natural light illumination, tworkFor the working time.
4. public building the Environmental Factor Prediction uncertainty electricity consumption robust Optimal methods according to claim 1, feature
It is, indoor heat is passed to from outdoor by exterior wall and roof in the step 2):
Q1=KA (Tout-Tin)
In formula, Q is to be passed to indoor heat, W from outdoor by Fence structure；K is the heat transfer coefficient of Fence structure, unit W/
(m2·k)；A is the heat transfer area of Fence structure, unit m2；Tout、TinFor outdoor, room temperature, unit DEG C；
Enter the indoor solar radiant heat of air-conditioning by building glass window:
Q2=CaACsCiDjmaxCLQ
In formula, CaFor effective area coefficient；A is window areas, unit m2；CsFor shading coefficient；CiFor the screening of window internal sunshade facility
Positive coefficient；DjmaxFor maximum solar heat, unit W/m2；CLQFor glass pane cooling load coefficient；
Human-body radiating refrigeration duty:
Q3=qsnψCLQ
In formula, qsFor the mild quality of work adult man sensible heat heat dissipation capacity of different chamber, unit W；N is indoor total number of persons, and ψ is cluster
Coefficient, CLQFor sensible heat gain from human bodies cooling load coefficient；
Illuminator heat dissipation:
Q4=1000PlCLQ
In formula, PlFor power needed for illuminator, unit kW；
New wind load, it is the key that guarantee good indoor air quality summer that outdoor fresh air (fresh air) is introduced in air-conditioning system
Cooling load from outdoor air is calculated as follows:
Q5=M (hout-hin)
In formula, M is fresh air volume, units/kg/s；houtFor outdoor air enthalpy, unit kj/kg；hinFor room air enthalpy, unit
kj/kg；
The enthalpy of air is the sum of the enthalpy of dry air and the enthalpy of vapor in humid air, includes sensible heat and latent heat:
H=1.01T+d (2500+1.84T)
In formula, d indicates water capacity, usually takes d=0.014kg/ (kg.dryair)；Fresh air volume needed for interior is related with number, if
Everyone minimum fresh air figureofmerit is Rp, then M=Rp×n+Rb×s；RbMinimum fresh air requirmente standard needed for being every square metre；N is interior
Number；S is construction area；
To sum up, calculation of cooling load formula are as follows:
Q=KA (Tout-Tin)+CaACsCiDjmaxCLQ+qsnψCLQ+1000PCLQ+M(Rp×n+Rb×S)(hout-hin)
Then air-conditioning system energy consumption calculation formula are as follows:
In formula, xa,tFor air-conditioning system t period electricity consumption, QtFor t moment refrigeration duty, COP is Performance for Air Conditioning Systems index.
5. public building the Environmental Factor Prediction uncertainty electricity consumption robust Optimal methods according to claim 1, feature
It is, in the step 3)
Elevator device energy consumption model when up peak mode are as follows:
ELF(τ)=365.32-1.115FP,τ-0.199Ncar,τ+0.143FP,τNcar,τ+0.087FP,τ 2-0.228Ncar,τ 2
Flow of the people condition under the mode meets:
In formula, Pu,τIndicate uplink passenger flow total amount；Pd,τIndicate downlink passenger flow total amount；It indicates to enter elevator simultaneously in building entrance hall
The volume of the flow of passengers of uplink；It indicates to leave the volume of the flow of passengers of elevator in building top；
Likewise, passenger is also that flow of the people and elevator run the coefficient as a result, can be by secondary of number of units the average latency
Effect curved surface indicates that therefore, the average latency under this mode may be expressed as:
TAW(τ)=600.403-359.239ln (FP,τ)+86.113ln(Ncar,τ)-57.995ln(FP,τNcar,τ)+62.096ln
(FP,τ)2+36.007ln(Ncar,τ)2
The energy consumption model of elevator device when lowering peak mode are as follows:
ELF(τ)=- 523.806+30.355FP,τ-14.123Ncar,τ+0.241FP,τNcar,τ-0.188FP,τ 2+0.774Ncar,τ 2
Flow of the people condition under the mode meets:
Average latency are as follows:
TAW(τ)=729.197-429.455ln (FP,τ)+99.519ln(Ncar,τ)-54.286ln(FP,τNcar,τ)+69.213ln
(FP,τ)2+29.390ln(Ncar,τ)2
The energy consumption model of elevator device when flat peak mode are as follows:
ELF(τ)=276.885-5.409FP,τ-4.1Ncar,τ+0.067FP,τNcar,τ+0.058FP,τ 2+0.321Ncar,τ 2
Flow of the people condition under the mode meets:
Average latency are as follows:
TAW(τ)=622.589-352.932ln (FP,τ)+56.316ln(Ncar,τ)-43.369ln(FP,τNcar,τ)+56.403ln
(FP,τ)2+30.878ln(Ncar,τ)2。
6. public building the Environmental Factor Prediction uncertainty electricity consumption robust Optimal methods according to claim 1, feature
It is, objective function in the step 4) are as follows:
In formula, ptFor t moment electricity price；xtFor t period lighting system, air-conditioning system and elevator device total electricity consumption；
Consider public building illumination comfort standard, temperature pleasant range and elevator average latency as constraint condition:
Emin≤ Eset≤ Emax
Tmin≤ Tset≤ Tmax
Xmin≤ Xset≤ Xmax。
7. public building the Environmental Factor Prediction uncertainty electricity consumption robust Optimal methods according to claim 1, feature
It is, lighting system Robust Exponential in the step 5):
El.preFor the outdoor illuminance value under current time deterministic forecast, El,supFor carry out with it is electrically optimized when the outdoor optical assumed
Brightness value, RIlFor lighting system Robust Exponential, then:
El.pre–El,sup=RIl>=RIl,set
Outdoor illuminance value under current time interval prediction is [El.min,El.max], which is also El,supValue range,
So, the maximum value and minimum value of lighting system Robust Exponential are respectively as follows:
RIl,max=El.max–El,min
RIl,min=0
Air-conditioning system Robust Exponential:
Ta,preFor outdoor temp angle value under current time deterministic forecast, Ta,supFor carry out with it is electrically optimized when the outdoor temperature assumed
Value, RIaFor air-conditioning system Robust Exponential, then:
Ta,sup–Ta,pre=RIa>=RIa,set
Outdoor temp angle value under current time interval prediction is [Ta,min,Ta,max], which is also Ta,supValue range, that
, the maximum value and minimum value of air-conditioning system Robust Exponential be respectively as follows:
RIa,max=Ta,max–Ta,min
RIa,min=0
Elevator device Robust Exponential:
Fp,preFor indoor visitor flow rate value under current time deterministic forecast, Fp,supFor carry out with it is electrically optimized when the indoor stream of people that assumes
Magnitude, RIpFor elevator device Robust Exponential, then:
Fp,sup–Fp,pre=RIp>=RIp,set
Indoor visitor flow rate under current time interval prediction is [Fp,min,Fp,max], which is also Fp,supValue range, that
, the maximum value and minimum value of elevator device Robust Exponential be respectively as follows:
RIp,max=Fp,max–Fp,min
RIp,min=0
The unit of each electricity system Robust Exponential is not identical, it is difficult to compare robust level, therefore to each electricity system Robust Exponential
It is normalized, value range is [0,1], and normalized process is as follows:
In formula, RINIt is the Robust Exponential value after normalization；RIrawIt is the original Robust Exponential value before normalization；RIminAnd RImaxPoint
Not Wei original Robust Exponential minimum value and maximum value；
Above-mentioned to establish Robust Exponential with regard to lighting system, air-conditioning system and elevator device, the whole robust level of public building can
It is assessed by establishing comprehensive robust index, comprehensive Robust Exponential can be indicated by the way that each system weight value is arranged:
In formula, δ indicates electricity system type；RIδIt is the Robust Exponential of the electricity system；vδIt is corresponding weight coefficient；RImixIt is
Comprehensive Robust Exponential；In addition, constraining comprehensive Robust Exponential, make it not less than preset value RIreq, which can be with
Reflect that public building overall robustness is horizontal.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811438136.5A CN109409617B (en) | 2018-11-27 | 2018-11-27 | Power utilization robust optimization method for public building environment factor prediction uncertainty |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201811438136.5A CN109409617B (en) | 2018-11-27 | 2018-11-27 | Power utilization robust optimization method for public building environment factor prediction uncertainty |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109409617A true CN109409617A (en) | 2019-03-01 |
| CN109409617B CN109409617B (en) | 2021-11-26 |
Family
ID=65456147
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201811438136.5A Active CN109409617B (en) | 2018-11-27 | 2018-11-27 | Power utilization robust optimization method for public building environment factor prediction uncertainty |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109409617B (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111062536B (en) * | 2019-12-17 | 2021-02-19 | 钱小英 | Central air conditioner load prediction system and method based on big data |
| CN116976150A (en) * | 2023-09-22 | 2023-10-31 | 国网浙江省电力有限公司 | Air conditioner load optimization method considering multi-user uncertainty and demand diversity |
| CN117745043A (en) * | 2024-02-21 | 2024-03-22 | 国网数字科技控股有限公司 | Adjustment potential determining method, device and equipment |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104750069A (en) * | 2015-03-04 | 2015-07-01 | 国家电网公司 | Building control system |
| JP2018056014A (en) * | 2016-09-29 | 2018-04-05 | パナソニックＩｐマネジメント株式会社 | Environment control system, environment control method and program |
-
2018
- 2018-11-27 CN CN201811438136.5A patent/CN109409617B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104750069A (en) * | 2015-03-04 | 2015-07-01 | 国家电网公司 | Building control system |
| JP2018056014A (en) * | 2016-09-29 | 2018-04-05 | パナソニックＩｐマネジメント株式会社 | Environment control system, environment control method and program |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111062536B (en) * | 2019-12-17 | 2021-02-19 | 钱小英 | Central air conditioner load prediction system and method based on big data |
| CN116976150A (en) * | 2023-09-22 | 2023-10-31 | 国网浙江省电力有限公司 | Air conditioner load optimization method considering multi-user uncertainty and demand diversity |
| CN116976150B (en) * | 2023-09-22 | 2023-12-12 | 国网浙江省电力有限公司 | Air conditioner load optimization method considering multi-user uncertainty and demand diversity |
| CN117745043A (en) * | 2024-02-21 | 2024-03-22 | 国网数字科技控股有限公司 | Adjustment potential determining method, device and equipment |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109409617B (en) | 2021-11-26 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Sehar et al. | Demand management to mitigate impacts of plug-in electric vehicle fast charge in buildings with renewables | |
| CN109409617A (en) | A kind of public building the Environmental Factor Prediction uncertainty electricity consumption robust Optimal methods | |
| CN109312951B (en) | Air conditioner management device, heat source equipment management device, air conditioner management method, and heat source equipment management method | |
| Yang et al. | Economic analysis of the daylight-linked lighting control system in office buildings | |
| Lai et al. | Energy saving based lighting system optimization and smart control solutions for rail transportation: Evidence from China | |
| Shishegar et al. | Quantifying electrical energy savings in offices through installing daylight responsive control systems in hot climates | |
| CN109685396B (en) | Power distribution network energy management method considering public building demand response resources | |
| Chen et al. | Realizing natural ventilation potential through window control: The impact of occupant behavior | |
| Bie et al. | HVAC operation planning for electric bus trips based on chance-constrained programming | |
| CN111308901A (en) | Building energy-saving system based on computer | |
| Tang et al. | Energy saving actions toward NZEBs with multiple-criteria optimization in current residential buildings | |
| Zanoli et al. | Thermal and lighting control system with energy saving and users comfort features | |
| Du et al. | Impact of space layout on energy performance of office buildings coupling daylight with thermal simulation | |
| CN117077242A (en) | Near-zero carbon emission design system based on photoelectric power supply and distribution | |
| Sekisov | Problems of achieving energy efficiency in residential low-rise housing construction within the framework of the resource-saving technologies use | |
| Gazal et al. | Evaluation of energy-efficient retrofit potential for government offices in India | |
| CN114390762A (en) | Adaptive dimming system based on edge calculation and working method thereof | |
| Pushkar | The LEED-commercial interiors (V4) projects in California | |
| Si et al. | A prototype of energy saving system for office lighting by using pso and wsn | |
| Irakoze et al. | The Evaluation of Ceiling Depth Impact on Lighting and Overall Energy Consumption of a Building with Top-lighting System | |
| Tan et al. | Investigation into energy performance of a multi-building complex in a hot and humid climate: efficacy of energy saving measures | |
| Fuller et al. | Thermal simulation of an Australian university building | |
| Ren et al. | Indoor Constant Illumination Control Strategy Research Based on Natural Lighting Analysis | |
| Clitan et al. | Principles of Energy Saving in Buildings. A Survey | |
| Tang et al. | Research on Control Metherm of Adaptive Lighting system in Intelligent Building |
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 |