CN102980272A - Air conditioner system energy saving optimization method based on load prediction - Google Patents

Abstract

The invention discloses an air conditioner system energy saving optimization method based on load prediction. Historical data is utilized to predict the load of a future-moment air conditioner system; then, an energy consumption model of an air conditioner system is optimized by a particle algorithm to obtain an optimal operation parameter under the load condition; according to the real-time retardation time of the system, each parameter variable of the system is controlled in advance; a control time difference brought by the big time delay and big inertia of the air conditioner system is avoided; the amount of coldness supplied by the system and the amount of coldness used by the load are guaranteed to be same; and synchronism in time is synchronous realized. In addition, according to the air conditioner system energy saving optimization method based on load prediction, the load prediction and the optimization control of the air conditioner system can be carried out in real time, and therefore the air conditioner system is always under the optimal working state or the near-optimal working state to achieve the purpose of energy saving optimization.

Description

A kind of air conditioner system energy saving optimization method based on load prediction

Technical field

The present invention relates to a kind of air conditioner system energy saving optimization method, belong to the energy saving optimizing field of air-conditioning system.

Background technology

Along with the development of Chinese Urbanization and process of industrialization, construction industry development is swift and violent, and building energy consumption constantly increases, and building energy consumption has occupied more than 27% of social total energy consumption, and some area is near 40%, and wherein 2/3rds energy consumption is consumed by air-conditioning system.Account at building energy consumption under the ever-increasing present situation of ratio of whole energy resource consumption, the air conditioner system energy saving in the building has become emphasis and the focus in the energy-saving field.According to the energy-conservation concept of terminal, the dynamics that strengthens air conditioner energy saving has huge theoretical and practical significance to energy savings.

Owing to lack advanced control technology means and equipment, central air conditioner system still adopts traditional labor management mode and easy switching control device mostly, can not realize that air-condition freezing discharge follows the variation of terminal load and dynamic adjustments, when operation at part load, cause energy waste very large, make China's energy for building inefficiency.In recent years, along with application and the development of building automatic (BA) system and variable-frequency control technique, people begin to adopt BA or frequency converter that air-conditioning system equipment is controlled.A kind of is by the BA system equipment such as handpiece Water Chilling Units, water pump, blower fan to be carried out long-range on off control, realizes that its " use becomes more meticulous " is energy-conservation; Another kind is by the collection to air-conditioning system ductwork pressure or temperature, as controlled parameter, adopts PI or PID control frequency converter to regulate the operating frequency of water pump with pressure reduction or the temperature difference, makes pump capacity follow controlled parameter and changes, thereby reach the purpose of pump energy saving.But, central air conditioner system be one have time lag, the time become, the complication system of non-linear and large inertia, its complexity causes central air conditioner system to be difficult to accurately Mathematical Modeling or method are described, and, the engineering parameter of PID controller depends on commissioning staff's experience to a great extent, it is very large affected by human factor, once adjust, just can not follow load adjusts automatically with the variation of operating mode, not only the poor system that also causes easily of energy-saving effect shakes, and affects the stability of central air conditioner system operation and the service quality of air conditioning terminal.In the article " based on the air conditioning water flow dynamics control technology of load prediction ", the author adopts the method based on load prediction, utilize intelligent fuzzy control technique, according to load and environment temperature, come intelligent decision is carried out in control by fuzzy rule base, the method has reached energy-conservation Optimal Control effect to a certain extent, but the accuracy of the decision rule that obtains by fuzzy control still need improve.

Because the time change attitude feature of central air conditioner system, the online accumulation that traditional Energy Saving Control strategy is can not be in the air-conditioning system running real-time and comprehensive relevant information, carry out the control parameter of instant correction or regulating system, more can not make air-conditioning system be in all the time optimum or approaching optimum duty.On the other hand, because the large dead time of air-conditioning system and large inertia, the output of control system always will just can be worked later on through τ lag time, yet popular constant-pressure drop and the constant difference control model of air conditioner energy saving control field belongs to " following control " at present, generally be only applicable to controlled device or process without time lag, be used in to be difficult in the air-conditioning system obtain and control preferably effect.

Summary of the invention

For the deficiency that existing air conditioner system energy saving control method exists, the present invention proposes a kind of air conditioner system energy saving optimization method based on load prediction.Basic thought of the present invention is, analysis by comprehensive systematic parameter detection and historical data is judged, the load of etching system (chilling requirement) when Inference Forecast goes out future, then optimize the energy consumption function model of air-conditioning system, obtain the optimized operation parameter under this load, again according to real-time lag time of the τ of system, in advance each parametric variable of system is controlled, quantitatively equate for the cold-peace load is cold with the assurance system, synchronously, make air-conditioning system be in all the time optimum or approaching optimum duty on time.

The specific implementation step of the present invention proposes a kind of air conditioner system energy saving optimization method based on load prediction is as follows:

The first step is collected the data line number Data preprocess of going forward side by side, and the data of collection comprise room occupancy rate and the air-conditioning system load in the moment to be measured, and the historical data of outdoor mean temperature and air-conditioning system load, and sample data is carried out normalization;

Second step utilizes neutral net that the air-conditioning system load in the air-conditioning system moment to be measured is predicted;

In the 3rd step, the energy consumption model of setting air-conditioning system is P=f (Q, T _1o, T _2o, v ₁, v ₂, Fair), wherein P is the energy consumption power of air-conditioning system, Q is the air-conditioning system load, T _1oBe air-conditioning system chilled water leaving water temperature, T _2oBe air-conditioning system cooling water leaving water temperature, v ₁Be the chilled water pump flow of air-conditioning system, v ₂Cooling water pump flow for air-conditioning system, Fair is the air mass flow of air-conditioning system, under the prerequisite of the air-conditioning system load Q that obtains the moment to be measured by described second step, utilize particle cluster algorithm to carry out the optimization of energy consumption model, obtain the best parameter group the when moment to be measured, the energy consumption power P was got minimum of a value, i.e. T _1o, T _2o, v ₁, v ₂, the best parameter group of Fair.

Step 4: according to the T that obtains the moment to be measured _1o, T _2o, v ₁, v ₂, the best parameter group of Fair is controlled in advance to each parametric variable, and when guaranteeing that moment to be measured arrives, air-conditioning system is in optimum duty.

Description of drawings

Fig. 1 is the schematic diagram of optimizing based on the air conditioner system energy saving of load prediction among the present invention.

Fig. 2 is the network model structure chart of neutral net among the present invention.

Fig. 3 utilizes particle cluster algorithm to carry out the flow chart of air conditioning energy consumption model optimization among the present invention.

The specific embodiment

The below is take Suzhou deluxe hotel as embodiment, and by reference to the accompanying drawings the specific embodiment of the present invention is described in detail.

With reference to accompanying drawing 1, the method for the air conditioner system energy saving optimization based on load prediction of the present invention mainly comprises following step:

Step 1: collect the historical data line number Data preprocess of going forward side by side.

According to the factor analysis that affects air conditioning energy consumption, the historical data that the present invention collects comprises: the occupancy rate of t and air-conditioning system are loaded constantly, moment t front 24,48,72,168 hours, be t-24, t-48, t-72, the t-168 outdoor mean temperature of actual measurement and air-conditioning system load constantly, and above-mentioned data as sample data.In order to reduce unusual sample to the impact of neutral net performance, the load of the air-conditioning system in the sample data and outdoor mean temperature are carried out respectively following normalization, make its scope between [0,1].The normalization formula is:

$y=\frac{x-x_{\min }}{x_{\max }-x_{\min }},$

Wherein, x is sample data, and y is the sample data after the normalization, x _MinBe the minimum of a value of x, x _MaxMaximum for x.

Step 2: neuron network simulation.

Utilize neutral net that the following load constantly of air-conditioning system is predicted, specifically comprise as follows:

(1) determines the input variable number of neutral net.(2) determine the output variable number of neutral net.(3) determine the hidden layer element number of neutral net.(4) netinit and carry out network training.(5) utilize the network that trains to carry out load prediction.

The input variable of neutral net is among the present invention: the occupancy rate of moment t, t-24, t-48, t-72, t-168 actual measurement outdoor temperature and air-conditioning system load constantly; Output variable is the air-conditioning system load of constantly t, and wherein, outdoor temperature refers to outdoor mean temperature.Then, all data messages are consisted of sample data, utilize BP algorithm of neural network, carry out network training according to network structure shown in Figure 2.

The input variable number of network is 9, the air conditioner load the when outdoor temperature the when air conditioner load the when outdoor temperature the when air conditioner load the when outdoor temperature the when air conditioner load the when outdoor temperature when being respectively occupancy rate, the t-24 of t constantly, t-24, t-48, t-48, t-72, t-72, t-168 and t-168.

The output variable number of network is 1, is the air conditioner load of moment t.

The hidden layer number of network is determined by following formula

$s=\sqrt{0.43\mathrm{kn}+0.12n^2+2.54k+0.77n+0.35}+0.5$

Wherein, k is the number of input layer, and n is the neuronic number of output layer, and s is the number of hidden layer neuron.Among the present invention, k=9, n=1, the substitution following formula can get s=5.7887, so hidden layer is elected 6 as.After network training is finished, namely obtain predicting the neural network model of air-conditioning system load.

Step 3: the load that utilizes the neural network prediction air-conditioning system.

Utilize the neural network model that generates in the step 2, input the input variable information in the moment to be measured, can obtain the air-conditioning system load Q in the moment to be measured.

Step 4: utilize population that the air conditioning energy consumption model is optimized.

Among the present invention, whole air-conditioning system mainly is comprised of handpiece Water Chilling Units, chilled water pump, cooling water pump and four main energy consumption equipments of blower fan of cooling tower.Can get corresponding power consumption power according to the equipment operation logic is respectively: P ₁, P ₂, P ₃And P ₄So, the total energy consumption P=P of whole air-conditioning system ₁+ P ₂+ P ₃+ P ₄, its energy consumption model is P=f (Q, T _1o, T _2o, v ₁, v ₂, Fair), the factor that wherein affects energy consumption power has, air-conditioning system load Q, chilled water leaving water temperature T _1o, cooling water leaving water temperature T _2o, chilled water pump flow v ₁, cooling water pump flow v ₂With air mass flow Fair.

The energy saving optimizing of air-conditioning system refers to equipment when operation, and the power consumption of whole air-conditioning system is minimum, rather than wherein the power consumption of certain equipment is minimum, and therefore, the present invention is the object function of the energy consumption P of whole air-conditioning system as optimization.Then, under the known prerequisite of prediction load Q, utilize population to carry out the optimization of energy consumption model, the best parameter group when obtaining the energy consumption power P and getting minimum of a value, namely

(T _1o,T _2o,v ₁,v ₂,Fair)。

With reference to accompanying drawing 3, utilize particle cluster algorithm as follows to the process that air conditioning energy consumption is optimized:

(1) parameter initialization.Mainly be to the 0th generation Particle Swarm random site x _Ij(0) and speed v _Ij(0) carries out initial setting.In particle cluster algorithm, a particle correspondence a solution of object function.Among the present invention, moment air-conditioning system load Q to be measured is obtained by neural network prediction by step 3, and namely Q is known, so the initialization particle is X _i=(x _I1, x _I2, x _I3, x _I4, x _I5), wherein, x _I1Corresponding chilled water leaving water temperature T _1o, x _I2Corresponding cooling water leaving water temperature T _2o, x _I3Corresponding chilled water pump flow v ₁, x _I4Corresponding cooling water pump flow v ₂, x _I5Corresponding air mass flow Fair.Setting the Particle Swarm scale is s, and evolutionary generation is t _Max, to each particulate X _i(i=1,2 ..., s) carry out the initialization of position and speed, namely to i arbitrarily, j (j=1,2 ..., 5), all obeying in range of variables evenly distributes produces x _Ij(0) and v _Ij(0).Leave during evolution the possibility of search volume in order to reduce particle, with v _IjLimit within the specific limits, i.e. [v _Min, v _Max].If the search volume is limited to [x _Min, x _Max] in, then set v _Min=0.75x _Min, v _Max=0.75x _MaxFor example, when j=1, x _I1Range of variables be [T _1omin, T _1omax], then can obtain v _I1Scope be [0.75T _1omin, 0.75T _1omax], respectively at range of variables [T _1omin, T _1omax] and [0.75T _1omin, 0.75T _1omax] in evenly produce x _I1(0) and v _I1(0) (i=1,2 ..., s).Local desired positions P _i(0) initialization is set to X _i(0), (i=1,2 ..., s), overall desired positions P _g(0) initializes the P that is set to the adaptive value minimum _i(0) (i=1,2 ..., s), wherein adaptive value is the target function value f (X of current particulate _i).

(2) speed and the position of renewal particulate.Formula is as follows,

v _ij(t+1)=ω·v _ij(t)+m ₁r ₁(t)(p _ij(t)-x _ij(t))+m ₂r ₂(t)(p _gj(t)-x _ij(t))

x _ij(t+1)=x _ij(t)+v _ij(t+1)(i=1,2,…,s)，(j=1,2,…,5)

Wherein, v _Ij(t) and x _Ij(t) be respectively particulate t for the time speed and position, ω is inertia weight, m ₁And m ₂Be aceleration pulse, r ₁And r ₂Be the random number between 0 and 1, P _i(t) be the t of particulate i for local desired positions, P _g(t) be the t overall desired positions in generation, p _Ij(t) be vectorial P _j(t) the j dimension element in, p _Gj(t) be vectorial P _g(t) the j dimension element in.

(3) calculate the adaptive value of each particulate, namely t+1 is for particulate X _i(t+1) target function value f (X _i).

(4) the local desired positions P of new particle more _iWith overall desired positions P _g

For each particulate, with its adaptive value and the desired positions P that lives through _iAdaptive value compare, if better, then with it as current desired positions.For minimization problem of the present invention, target function value is less, and corresponding adaptive value is better, that is:

$P_i(t+1)=\left\{\begin{array}{cc}{P}_i\left(t\right)& f\left(X_i(t-1)\right)\&GreaterEqual;f\left(P_i\left(t\right)\right)\\ X_i(t+1)& f\left(X_i(t+1)\right)<f\left(P_i\left(t\right)\right)\end{array}\right.,(i=\mathrm{1,2},\&CenterDot;\&CenterDot;\&CenterDot;,s)$

Overall desired positions P in the colony _g, P _g(t) ∈ { P ₀(t), P ₁(t) ..., P _sAnd f (P (t) }, _g(t))=min{f (P ₀(t)), f (P ₁(t)) ..., f (P _s(t)) }.For each particulate, with its adaptive value and the overall desired positions P that lives through _gAdaptive value compare, if better, then with it as current overall desired positions.That is:

$P_g(t+1)=\left\{\begin{array}{cc}{P}_g\left(t\right)& f\left(X_i(t-1)\right)\&GreaterEqual;f\left(P_g\left(t\right)\right)\\ X_i(t+1)& f\left(X_i(t+1)\right)<f\left(P_g\left(t\right)\right)\end{array}\right.$

(5) if evolutionary generation t＜t _Max, then return step (2), otherwise finish and output optimal solution P _g

Step 5: according to the optimized operation parameter, each parametric variable is controlled in advance.

The optimal solution P that obtains in the step 4 _gCorresponding air-conditioning system the consume energy best value of respectively controlling parameter when minimum, i.e. optimum parameter combinations (T _1o, T _1o, v ₁, v ₂, Fair).Then according to real-time lag time of the τ of system, in advance each parametric variable of system is controlled, when guaranteeing following constantly t, system quantitatively equates for the cold-peace load is cold, synchronous on time, make air-conditioning system be in all the time optimum or approaching optimum duty, reach the purpose of energy saving optimizing.

The present invention utilizes historical data, dope the following constantly load of air-conditioning system, then optimize the energy consumption model of air-conditioning system, obtain the optimized operation parameter under this loading condiction, according to real-time lag time of the τ of system, in advance each parametric variable of system is controlled again, not only avoided since the control time that the large dead time of air-conditioning system and large inertia are brought poor, and the system that guaranteed is cold quantitatively equal for the cold-peace load, and is synchronous on the time.On the other hand, the present invention can also carry out load prediction and the optimal control of air-conditioning system in real time, makes air-conditioning system be in all the time optimum or approaching optimum duty, thereby reaches the purpose of energy saving optimizing.

Claims (3)

1. air conditioner system energy saving optimization method based on load prediction mainly comprises following concrete steps:

The first step is collected historical data, and data are carried out normalization;

Second step utilizes the historical data after the normalization, and the air-conditioning system load Q to the air-conditioning system moment to be measured predicts by neutral net;

In the 3rd step, the energy consumption model of setting air-conditioning system is P=f (Q, T _1o, T _2o, v ₁, v ₂, Fair), wherein P is the energy consumption power of air-conditioning system, Q is the air-conditioning system load, T _1oBe air-conditioning system chilled water leaving water temperature, T _2oBe air-conditioning system cooling water leaving water temperature, v ₁Be the chilled water pump flow of air-conditioning system, v ₂Cooling water pump flow for air-conditioning system, Fair is the air mass flow of air-conditioning system, under the prerequisite of the air-conditioning system load Q that obtains the moment to be measured by described second step, utilize particle cluster algorithm to carry out the optimization of energy consumption model, obtain the best parameter group when the energy consumption power P in the moment to be measured is got minimum of a value, i.e. T _1o, T _2o, v ₁, v ₂, the best parameter group of Fair.

Step 4: according to the T that obtains _1o, T _2o, v ₁, v ₂, the best parameter group of Fair is controlled in advance to each parametric variable, and when guaranteeing that moment to be measured arrives, air-conditioning system is in optimum duty.

2. air conditioner system energy saving optimization method as claimed in claim 1, wherein particle cluster algorithm may further comprise the steps:

The first step is expressed as X with particulate _i=(x _I1, x _I2, x _I3, x _I4, x _I5), wherein, x _I1Corresponding chilled water leaving water temperature T _1o, x _I2Corresponding cooling water leaving water temperature T _2o, x _I3Corresponding chilled water pump flow v ₁, x _I4Corresponding cooling water pump flow v ₂, x _I5Corresponding air mass flow Fair, setting the Particle Swarm scale is s, evolutionary generation is t _Max, to each particulate X _i(i=1,2 ..., s) carry out the initialization of position and speed, namely to i arbitrarily, j (j=1,2 ..., 5), all in its range of variables, obey equally distributed generation x _IjAnd v _IjInitialization value, and to the local desired positions P of particulate _iWith overall desired positions P _gInitialize;

Second step, speed and the position of renewal particulate, formula is following to be expressed as:

v _ij(t+1)=ω·v _ij(t)+m ₁r ₁(t)(p _ij(t)-x _ij(t))+m ₂r ₂(t)(p _gj(t)-x _ij(t))；(i=1,2,…,s)(j=1,2,…,5)

x _ij(t+1)＝x _ij(t)+v _ij(t+1)；(i=1,2,…,s)(j=1,2,…,5)

Wherein, v _Ij(t) and x _Ij(t) be respectively particulate t for the time speed and position, ω is inertia weight, m ₁And m ₂Be aceleration pulse, r ₁And r ₂Be the random number between 0 and 1, P _i(t) be the local desired positions in t generation of particulate, P _g(t) be the t overall desired positions in generation, p _Ij(t) be vectorial P _i(t) the j dimension element in, p _Gj(t) be vectorial P _g(t) the j dimension element in;

In the 3rd step, calculate the target function value f (X of each particulate in t+1 generation _i);

The 4th step, the local desired positions P of renewal particulate _iWith overall desired positions P _g, formula table is shown:

$P_i(t+1)=\left\{\begin{array}{cc}{P}_i\left(t\right)& f\left(X_i(t-1)\right)\&GreaterEqual;f\left(P_i\left(t\right)\right)\\ X_i(t+1)& f\left(X_i(t+1)\right)<f\left(P_i\left(t\right)\right)\end{array}\right.$

$P_g(t+1)=\left\{\begin{array}{cc}{P}_g\left(t\right)& f\left(X_i(t-1)\right)\&GreaterEqual;f\left(P_g\left(t\right)\right)\\ X_i(t+1)& f\left(X_i(t+1)\right)<f\left(P_g\left(t\right)\right)\end{array}\right.$

P wherein _i(t+1) be the local desired positions in t+1 generation of particulate, P _g(t+1) be the t+1 overall desired positions in generation, X _i(t+1) be that t+1 is for particulate;

If the 5th step is evolutionary generation t＜t _Max, then return second step, otherwise finish and export overall desired positions P _g

3. air conditioner system energy saving optimization method as claimed in claim 1, the historical data of wherein collecting comprise room occupancy rate and the air-conditioning system load in a certain moment, and the outdoor mean temperature before the described a certain moment and air-conditioning system load.

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