CN104298191A

CN104298191A - Heat prediction management based energy consumption control method in intelligent building

Info

Publication number: CN104298191A
Application number: CN201410415239.5A
Authority: CN
Inventors: 周海航; 姚建国; 管海兵
Original assignee: Shanghai Jiao Tong University
Current assignee: Shanghai Jiao Tong University
Priority date: 2014-08-21
Filing date: 2014-08-21
Publication date: 2015-01-21
Anticipated expiration: 2034-08-21
Also published as: CN104298191B

Abstract

The invention provides a heat prediction management based energy consumption control method in an intelligent building, which comprises the steps of 1, establishing a heat model of the building; 2, establishing a thermodynamic model in a state space; 3, carrying out a schedulability test and judging whether a heat load of the building has schedulability or not under given energy consumption load budget; and 4, operating a model predictive control (MPC) based heat control strategy to solve a corresponding control output variable. According to the invention, an HVAC (heating, ventilating and air conditioning) system in the building is adjusted, and the peak power is enabled to be reduced under various constraints such that the room temperature is small in variation, a task processing constraint is met, and the like. In addition, the system can carry out schedulability analysis very well, and carries out good judgment and correction on whether the current energy consumption budget can meet requirements or not.

Description

Energy consumption control method based on heat prediction management in intelligent buildings

技术领域technical field

本发明涉及消耗管理，具体地，涉及智能建筑中基于热量预测管理的能耗控制方法。The invention relates to consumption management, in particular to an energy consumption control method based on heat forecast management in intelligent buildings.

背景技术Background technique

最近这些年，建筑物中的能源消耗增长的非常快。2009年的相关研究显示，美国建筑物中能源消耗占美国总能源消耗将近40％。最近，美国的能源信息部(EnergyInformation Administration)预测，2012年到2030年间，建筑物中的总能源消耗将会达到4.74QBtu。建筑物中的能源消耗具有鲜明的特点，即用电峰值需求较明显。为保证对用户的用电服务质量，建筑物建造时，需要使其满足用户用电峰值的能力，这将提高建筑物的建造成本。另一方面，为减少电网的运行风险，电网往往对于峰值负载施加了较为严厉的价格惩罚，即提高电网峰值电价。因此，平缓建筑物用电曲线对于用电使用者和提供者，都是有益的。Energy consumption in buildings has grown very rapidly in recent years. A related study in 2009 showed that energy consumption in buildings in the United States accounts for nearly 40% of the total energy consumption in the United States. Recently, the US Department of Energy Information (Energy Information Administration) predicted that between 2012 and 2030, the total energy consumption in buildings will reach 4.74QBtu. Energy consumption in buildings is characterized by high peak demand for electricity. In order to ensure the quality of electricity service for users, the building needs to be constructed to meet the peak power consumption of users, which will increase the construction cost of the building. On the other hand, in order to reduce the operation risk of the power grid, the power grid often imposes a severe price penalty on the peak load, that is, to increase the peak power price of the power grid. Therefore, flattening the electricity consumption curve of buildings is beneficial to both electricity users and providers.

在建筑中，能量的消耗相当大一部分来自建筑中HVAC(Heating,Ventilatingand Air Conditioning，供热通风与空气调节)系统。同时，大的建筑中，一般也需要涉及到复杂的HVAC系统。因此，对于建筑中的HVAC系统，进行电能管理，使其获得较为平稳的用电曲线，能够使用户获得较大的效益。HVAC系统主要用于消除建筑物中耗能应用产生的热量。因此，我们使用MPC(Model Predictive Control，模型预测控制)方法考虑热量负载预测管理，并综合能耗控制方法，建筑物HVAC系统中的用电负载峰值问题进行建模控制。In buildings, a considerable part of energy consumption comes from the HVAC (Heating, Ventilating and Air Conditioning) system in the building. At the same time, in large buildings, complex HVAC systems are generally involved. Therefore, for the HVAC system in the building, the power management is carried out to make it obtain a relatively stable power consumption curve, which can enable users to obtain greater benefits. HVAC systems are primarily used to remove heat generated by energy-consuming applications in buildings. Therefore, we use the MPC (Model Predictive Control, Model Predictive Control) method to consider the thermal load forecast management, and integrate the energy consumption control method, and the peak power load problem in the building HVAC system for modeling control.

当前，对于建筑物中的耗能系统综合模型预测方法和能耗控制方法相结合进行控制的方式较少。已有的方法，集中于在一个多层次的结构下，进行耗能应用操作控制和最优能量消耗管理。该结构包含一个多层调度系统，底层是基于混合整数线性规划问题进行在线负载调度策略。该策略用于最小化总的操作费用，并进行了能耗能力限制。负载系统和电网间的交互由需求响应管理器处理。不过，该策略并没有量化地研究可调度性问题，即在限定能耗下，能否使HVAC系统调节建筑物温度达到特定温度范围。At present, there are few ways to control the comprehensive model prediction method and energy consumption control method of the energy consumption system in the building. Existing methods focus on the operational control of energy-consuming applications and the management of optimal energy consumption in a multi-level structure. The structure includes a multi-layer scheduling system, and the bottom layer is an online load scheduling strategy based on mixed integer linear programming problems. This strategy is used to minimize the total operating cost, and the energy consumption capacity is limited. The interaction between the load system and the grid is handled by the Demand Response Manager. However, this strategy does not quantitatively study the dispatchability problem, that is, whether the HVAC system can regulate the temperature of the building to a specific temperature range under the constraint of energy consumption.

发明内容Contents of the invention

针对现有技术中的缺陷，本发明的目的是提供一种智能建筑中基于热量预测管理的能耗控制方法。In view of the defects in the prior art, the object of the present invention is to provide an energy consumption control method based on thermal forecast management in intelligent buildings.

根据本发明提供的一种智能建筑中基于热量预测管理的能耗控制方法，包括如下步骤：According to an energy consumption control method based on thermal forecast management in an intelligent building provided by the present invention, the method comprises the following steps:

步骤1：建立建筑物中热量模型；Step 1: Establish a thermal model in the building;

步骤2：建立状态空间下的热力学模型；Step 2: Establish a thermodynamic model in the state space;

步骤3：进行可调度性测试，判定在给定负载预算budget下，该建筑物的热量负载是否具有可调度性；Step 3: Conduct a dispatchability test to determine whether the thermal load of the building is dispatchable under a given load budget budget;

步骤4：运行基于模型预测控制MPC的热量控制策略，对式(10)进行最小化求解，解出相应的控制输出变量；Step 4: Run the heat control strategy based on the model predictive control MPC, minimize and solve the formula (10), and solve the corresponding control output variables;

其中，所述步骤4具体如下：Wherein, the step 4 is specifically as follows:

对于式(11)表示的目标函数，在满足式(10)中的约束下，采用求解标准的整数最小二乘优化问题的方式进行求解：For the objective function represented by formula (11), under the constraints in formula (10), the method of solving the standard integer least squares optimization problem is used to solve:

其中，表示温度参考向量，表示表示F个区域加热器参考功率输入速率矩阵，表示预算参考向量，W_c(k)表示扰动向量，L表示加热器个数，N表示预测长度，表示状态预测向量，表示状态模型参数，表示状态模型参数，表示第j个区域第i个加热器的ON/OFF状态，U_p(k)表示式(10)变形后使用的新的系统输入变量；in, represents the temperature reference vector, Denotes the F zone heater reference power input rate matrix, represents the budget reference vector, W _c (k) represents the disturbance vector, L represents the number of heaters, N represents the predicted length, represents the state prediction vector, represents the state model parameters, represents the state model parameters, Indicates the ON/OFF state of the i-th heater in the j-th area, and U _p (k) represents the new system input variable used after the transformation of formula (10);

式(10)中代式的含义如下：The meaning of the substitution formula in formula (10) is as follows:

${\overset{&OverBar; &OverBar;}{Y Y}}_{r r} ((k k)) = = {(\begin{matrix} {Y Y}_{r r} ((k k + + 11 | | k k)) & {Y Y}_{r r} ((k k + + 22 | | k k)) & . . . . . . & {Y Y}_{r r} ((k k + + M m | | k k)) \end{matrix})}^{T T} . .$

$\overset{&OverBar; &OverBar;}{X x} ((k k)) = = (\begin{matrix} ΦX ΦX ((k k)) \\ {Φ Φ}^{22} X x ((k k)) \\ . . \\ . . \\ . . \\ {Φ Φ}^{M m} X x ((k k)) \end{matrix})$

${U u}_{p p} ((k k)) = = (\begin{matrix} U u ((k k | | k k)) \\ U u ((k k + + 11 | | k k)) \\ . . \\ . . \\ . . \\ U u ((k k + + N N - - 11 | | k k)) \end{matrix}),,$

${W W}_{c c} ((k k)) = = {(\begin{matrix} {T T}_{a a}^{11} & {Φ Φ}_{s the s}^{11} & O o & . . . . . . & {T T}_{a a}^{F f} & {Φ Φ}_{s the s}^{F f} & O o \end{matrix})}^{T T},,$

$\overset{&OverBar; &OverBar;}{Ψ Ψ} = = diag diag (\begin{matrix} Ψ Ψ & . . . . . . & Ψ Ψ \end{matrix}),,$

其中，M表示预测长度，Y_r(k+M|k)表示第k个时间后第M个采样间隔的状态预测；X(k+M|k)表示第k个时间后第M个采样间隔的状态预测，M表示预测长度，N表示预测长度，U(k+N-1|k)表示第k个时间后第N-1个采样间隔的状态预测，表示第F个区域的外部空气温度，X(k)表示k时刻系统状态，R表示两区域间等效电阻，Ψ表示F个区域加热器功率输入速率矩阵，G表示离散状态空间模型参数，H表示离散状态空间模型参数，Φ表示离散状态空间模型参数，表示建筑物中的总功率预算数，表示第F个区域加热器的功率输入速率向量；Among them, M represents the prediction length, Y _r (k+M|k) represents the state prediction of the M-th sampling interval after the k-th time; X(k+M|k) represents the M-th sampling interval after the k-th time The state prediction of , M represents the prediction length, N represents the prediction length, U(k+N-1|k) represents the state prediction of the N-1th sampling interval after the kth time, Indicates the outside air temperature in the Fth region, X(k) represents the system state at time k, R represents the equivalent resistance between the two regions, Ψ represents the power input rate matrix of F regional heaters, G represents the discrete state space model parameters, H Represents the discrete state space model parameters, Φ represents the discrete state space model parameters, represents the total power budget in the building, represents the power input rate vector of the Fth zone heater;

${min min}_{{U u}_{p p} ((k k))} {||\begin{matrix} {S S}_{Q Q} \overset{&OverBar; &OverBar;}{G G} {U u}_{p p} ((k k)) - - {S S}_{Q Q} {\overset{&OverBar; &OverBar;}{Y Y}}_{r r} ((k k)) \\ {S S}_{R R} {U u}_{p p} ((k k)) \end{matrix}||}^{22} - - - - - - ((1111))$

其中， $S_{Q}^{T} S_{Q} = Q, S_{R}^{T} S_{R} = R,$ S_Q表示平方根矩阵，表示S_Q转置矩阵，S_R表示平方根矩阵，表示S_R转置矩阵，U_p(k)表示式(10)变形后使用的新的系统输入变量；Q和R分别是用来对时间误差和输入功率的补偿矩阵。in, $S_{Q}^{T} S_{Q} = Q, S_{R}^{T} S_{R} = R,$ S _Q represents the square root matrix, Represents the S _Q transpose matrix, S _R represents the square root matrix, Represents the S _R transpose matrix, U _p (k) represents the new system input variable used after the transformation of equation (10); Q and R are compensation matrices for time error and input power, respectively.

优选地，所述热量模型，具体如下：Preferably, the heat model is specifically as follows:

对于有n个加热器的第j个区域来说，通过能量守恒定律，得到热平衡方程式为For the jth region with n heaters, through the law of energy conservation, the heat balance equation is obtained as

$\begin{matrix} \frac{{dT dT}_{j j}}{dt dt} = = \frac{11}{{R R}_{j j}^{a a} {C C}_{j j}} (({T T}_{a a}^{j j} - - {T T}_{j j})) + + \frac{11}{{C C}_{j j}} {Σ Σ}_{i i = = 11,, i i &NotEqual; &NotEqual; j j}^{F f} \frac{11}{{R R}_{ij ij}^{r r}} (({T T}_{i i} - - {T T}_{j j})) \\ + + \frac{11}{{C C}_{j j}} {A A}_{j j}^{w w} {Φ Φ}_{s the s}^{j j} + + \frac{11}{{C C}_{j j}} {Σ Σ}_{n no = = 11}^{{N N}_{j j}} {Φ Φ}_{j j}^{n no} + + \frac{11}{{C C}_{j j}} {σ σ}_{j j} {ω ω}_{j j},, \end{matrix} - - - - - - ((11))$

t表示时间，F表示区域个数，N_j表示区域j中加热器个数，σ_j表示维纳噪声变量，T_j表示第j个区域内部温度，表示第j个区域同外部的热电阻，C_j表示第j个区域的热容，表示第j个区域外部温度，表示第i个区域和第j个区域的热电阻，T_i表示第i个区域内部温度，表示太阳辐射量Φ_s能够进入的有效窗户面积，表示太阳辐射产生的能量变化,表示第j个区域内第n个加热器的功率，ω_j表示标准维纳噪声；t represents time, F represents the number of regions, N _j represents the number of heaters in region j, σ _j represents the Wiener noise variable, T _j represents the internal temperature of the jth region, Indicates the thermal resistance between the jth area and the outside, C _j indicates the heat capacity of the jth area, Indicates the external temperature of the jth region, Indicates the thermal resistance of the i-th area and the j-th area, T _i indicates the internal temperature of the i-th area, Indicates the effective window area that the solar radiation Φ _s can enter, Indicates the energy change produced by solar radiation, Indicates the power of the nth heater in the jth region, and ω _j represents the standard Wiener noise;

在第j个区域的系统模型中，系统状态是室内空气温度，系统输入是加热器功率；扰动因数包含三个方面，外界温度、太阳辐射热量、标准维纳噪声。In the system model of the jth area, the system state is the indoor air temperature, and the system input is the heater power; the disturbance factor includes three aspects, the external temperature, the solar radiation heat, and the standard Wiener noise.

优选地，所述热力学模型，具体如下：Preferably, the thermodynamic model is as follows:

X(k)＝ΦX(k-1)+GU(k-1)+HW(k-1)，X(k)=ΦX(k-1)+GU(k-1)+HW(k-1),

Y(k)＝X(k)， (4)Y(k)=X(k), (4)

X(k)表示k时刻系统状态，Φ表示离散状态空间模型参数，X(k-1)表示k-1时刻系统状态，G表示离散状态空间模型参数，U(k-1)表示k-1时刻系统输入量，H表示离散状态空间模型参数，W(k-1)表示k-1时刻系统扰动量，Y(k)表示k时刻系统控制输出量；X(k) represents the state of the system at time k, Φ represents the parameters of the discrete state space model, X(k-1) represents the state of the system at time k-1, G represents the parameters of the discrete state space model, and U(k-1) represents k-1 Time system input, H represents discrete state space model parameters, W(k-1) represents system disturbance at time k-1, Y(k) represents system control output at time k;

其中，是第k次采样间隔T_s下系统状态，离散时间系统模型矩阵表示为 $Φ = e^{ATs}, G = {&Integral;}_{0}^{Ts} e^{As} Bds,$ 和 $H = {&Integral;}_{0}^{Ts} e^{As} Dds .$ in, is the system state at the kth sampling interval T _s , and the discrete-time system model matrix is expressed as $Φ = e^{ATs}, G = {&Integral;}_{0}^{Ts} e^{As} Bds,$ and $h = {&Integral;}_{0}^{Ts} e^{As} Dds .$

其中，T_s表示时间间隔，e表示常数e，s表示积分项，A表示状态空间模型参数，B表示状态空间模型参数，D表示状态空间模型参数，G表示离散状态空间模型参数，H表示离散状态空间模型参数。Among them, T _s represents the time interval, e represents the constant e, s represents the integral term, A represents the state space model parameters, B represents the state space model parameters, D represents the state space model parameters, G represents the discrete state space model parameters, H represents the discrete State-space model parameters.

优选地，所述步骤3包括如下步骤：Preferably, said step 3 includes the following steps:

步骤3.1：通过可调度性测试算法，得到需要满足的最小的负载预算budget_min；Step 3.1: Obtain the minimum load budget budget _min that needs to be satisfied through the schedulability test algorithm;

步骤3.2：如果当前得到的负载预算budget＜budget_min，则执行步骤3.3，否则，执行步骤3.4；Step 3.2: If the currently obtained load budget budget<budget _min , go to step 3.3; otherwise, go to step 3.4;

步骤3.3：请求增加能耗负载预算，得到一个新的负载预算budget′，重新执行步骤3.1，或是放松热量限制，得到一个新的最小的负载budget′_min，重新执行步骤3.2；Step 3.3: Request to increase the energy consumption load budget, get a new load budget budget′, re-execute step 3.1, or relax the calorie restriction, obtain a new minimum load _budget′min , and re-execute step 3.2;

步骤3.4：执行步骤4。Step 3.4: Execute step 4.

优选地，所述可调度性测试算法，具体为：Preferably, the schedulability test algorithm is specifically:

首先，将可调度性问题设计成为下列优化问题：First, formulate the schedulability problem as the following optimization problem:

$\underset{η η}{min min} {Σ Σ}_{j j = = 11}^{F f} {Σ Σ}_{i i = = 11}^{{N N}^{j j}} {η η}_{j j}^{i i} {P P}_{j j}^{i i},,$

s.t.-CA^-1(DW+Bη)∈int(Safe). (7)st-CA ^-1 (DW+Bη)∈int(Safe). (7)

其中，s.t.表示受约束于；Among them, s.t. means subject to;

定义存在η＝[η_j]∈[0,1]^F，使得 $Σ_{j = 1}^{F} Σ_{i = 1}^{N^{j}} η_{j}^{i} P_{j}^{i} \leq budget$ Define that there exists η=[η _j ]∈[0,1] ^F , such that $Σ_{j = 1}^{f} Σ_{i = 1}^{N^{j}} η_{j}^{i} P_{j}^{i} \leq budget$

η_j表示存在的[0,1]区间中的一个数，[·]^F表示F维向量，F表示F-可调度性，N^j表示第j个区域加热器个数，表示第j个区域第i个加热器的存在的η值，表示功率输入速率；η _j represents a number in the existing [0,1] interval, [ ] ^F represents the F-dimensional vector, F represents F-schedulability, N ^j represents the number of the jth district heater, the value of η representing the presence of the i-th heater in the j-th region, Indicates the power input rate;

A表示状态空间模型参数，B表示状态空间模型参数，D表示状态空间模型参数，W表示系统扰动，C表示状态空间模型参数；A represents the parameters of the state space model, B represents the parameters of the state space model, D represents the parameters of the state space model, W represents the system disturbance, and C represents the parameters of the state space model;

集合Safe定义为The set Safe is defined as

$[[{Y Y}_{l l}^{11},, {Y Y}_{u u}^{11}]] \times \times [[{Y Y}_{l l}^{22},, {Y Y}_{u u}^{22}]] \times \times . . . . . . \times \times [[{Y Y}_{l l}^{F f},, {Y Y}_{u u}^{F f}]] . .$

其中，Y_l ^F表示第F个区域加热器的下界向量，表示第F个区域加热器的上界向量；where Y _l ^F represents the lower bound vector of the Fth district heater, represents the upper bound vector of the Fth district heater;

最小的负载预算budget_min通过该优化问题求解得到；budget_min应为不小于目标函数值的负载预算数budget，设置 The minimum load budget budget _min is obtained by solving this optimization problem; budget _min should be the load budget number budget not less than the value of the objective function, set

优化问题中的限制是线性的，被表示为：The constraints in optimization problems are linear and are expressed as:

Y_l+CA^-1DW＜-CA^-1BPη＜Y_u+CA^-1DW.⑻Y _l +CA ^-1 DW<-CA ^-1 BPη<Y _u +CA ^-1 DW.⑻

其中，Y_l表示控制输出变量的下界，Y_u表示控制输出变量的上界，P表示功率输入向量；Among them, Y _l represents the lower bound of the control output variable, Y _u represents the upper bound of the control output variable, and P represents the power input vector;

其中in

Y_l＝[Y_l ¹，Y_l ²，…，Y_l ^F]^T，Y _l = [Y _l ¹ , Y _l ² ,..., Y _l ^F ] ^T ,

${Y Y}_{u u} = = {[[{Y Y}_{u u}^{11},, {Y Y}_{u u}^{22},, . . . . . .,, {Y Y}_{u u}^{F f}]]}^{T T} . .$

该优化问题通过线性规划求解。The optimization problem is solved by linear programming.

与现有技术相比，本发明具有如下的有益效果：Compared with the prior art, the present invention has the following beneficial effects:

本发明主要设计了一种智能建筑中基于热量预测管理的能耗控制方法，对于建筑物中HVAC系统进行调节，使其在房间温度较小变动、满足任务处理约束等各项约束的同时，降低峰值功率。并且，系统能够很好地进行可调度性分析，对于当前的能耗预算是否能够满足要求，进行良好地判断和修正。The present invention mainly designs an energy consumption control method based on heat prediction management in an intelligent building, which adjusts the HVAC system in the building so that it can reduce the energy consumption while the room temperature fluctuates slightly and meets various constraints such as task processing constraints. peak power. Moreover, the system can perform schedulability analysis very well, and make good judgments and corrections on whether the current energy consumption budget can meet the requirements.

通过该发明点，我们能够得到的效果是：Through this invention point, the effect we can get is:

1、当使建筑物各区域温度保持在某一定值附近时，温度变化平稳。1. When the temperature of each area of the building is kept near a certain value, the temperature changes smoothly.

2、能够有效地对热负载做负载均衡，使得热负载的负载峰值降低。2. It can effectively balance the heat load, so that the peak load of the heat load can be reduced.

3、很好地对建筑物内热量负载的可调度性进行分析，进行良好地判断和修正。3. To analyze the dispatchability of the heat load in the building well, and make a good judgment and correction.

附图说明Description of drawings

通过阅读参照以下附图对非限制性实施例所作的详细描述，本发明的其它特征、目的和优点将会变得更明显：Other characteristics, objects and advantages of the present invention will become more apparent by reading the detailed description of non-limiting embodiments made with reference to the following drawings:

图1为房间布局。Figure 1 shows the room layout.

图2为外界温度和太阳辐射强度扰动。Figure 2 shows the disturbance of external temperature and solar radiation intensity.

图3为on/off控制算法下加热器1-4功率输入。Figure 3 shows the heater 1-4 power input under the on/off control algorithm.

图4为on/off控制算法下加热器5-8功率输入。Figure 4 shows the power input of the heaters 5-8 under the on/off control algorithm.

图5为MPC控制算法下加热器1-4功率输入。Figure 5 shows the power input of heaters 1-4 under the MPC control algorithm.

图6为MPC控制算法下加热器5-8功率输入。Fig. 6 shows the power input of the heater 5-8 under the MPC control algorithm.

图7为MPC控制下房间1-4温度变化范围。Figure 7 shows the range of room 1-4 temperature variation under MPC control.

图8为MPC控制下房间5-8温度变化范围。Figure 8 shows the range of temperature change in room 5-8 under MPC control.

图9为比较ON/OFF和MPC控制的功率峰值。Figure 9 compares the power peaks of ON/OFF and MPC control.

图10为系统结构。Figure 10 shows the system structure.

具体实施方式Detailed ways

下面结合具体实施例对本发明进行详细说明。以下实施例将有助于本领域的技术人员进一步理解本发明，但不以任何形式限制本发明。应当指出的是，对本领域的普通技术人员来说，在不脱离本发明构思的前提下，还可以做出若干变形和改进。这些都属于本发明的保护范围。The present invention will be described in detail below in conjunction with specific embodiments. The following examples will help those skilled in the art to further understand the present invention, but do not limit the present invention in any form. It should be noted that those skilled in the art can make several modifications and improvements without departing from the concept of the present invention. These all belong to the protection scope of the present invention.

有鉴于此，我们设计了智能建筑中基于热量预测管理的能耗控制方法。对于热力系统中的功率调度问题，形成了一个带约束的优化问题。使用MPC技术，对热量进行预测。相应的能量峰值问题，成为优化问题中的一个约束，予以解决。并对可调度性问题进行测试解决。In view of this, we design an energy consumption control method based on thermal predictive management in smart buildings. For the power scheduling problem in thermal system, an optimization problem with constraints is formed. Use MPC technology to predict heat. The corresponding energy peak problem, which becomes a constraint in the optimization problem, is solved. And test and solve the schedulability problem.

在该方法中，我们的基本结构如图10所示。In this approach, our basic structure is shown in Figure 10.

在图10示出的结构化的系统设计中，主要包含几个层次：(1)PAC(PredictiveAdmission Controller,预测性准入控制器)(2)LB(Load Balance,负载均衡)(3)D/R(Demand Response，需求响应)管理器和负载预测器。In the structured system design shown in Figure 10, it mainly includes several levels: (1) PAC (PredictiveAdmission Controller, predictive admission controller) (2) LB (Load Balance, load balancing) (3) D/ R (Demand Response, demand response) manager and load forecaster.

PAC部分处于最底层，用于和物理设备相互作用，进行实时的功率准入控制。在顶层，D/R管理器是系统的进入点，作为同电网交互的接口。LF层基于预测的负载提供信息给D/R管理器和LB层。LB层基于预测的负载信息和当前价格信息，提供能耗预算给PAC层。The PAC part is at the bottom layer and is used to interact with physical devices to perform real-time power admission control. At the top level, the D/R Manager is the entry point to the system, acting as an interface to the grid. The LF layer provides information to the D/R manager and the LB layer based on the predicted load. The LB layer provides energy budget to the PAC layer based on the predicted load information and current price information.

在本发明中，主要关注PAC层，用于管理能量的进入和控制用电应用的操作。所有的用电请求提交给PAC层。PAC层，基于总的负载情况，决定该请求接受或是拒绝。PAC层主要包括两个模块：可调度性测试和热量控制。可调度性测试模块在执行时用来检查总的能耗预算是否被遵守。热量控制模块，基于当前温度、总的能耗预算、设定的温度范围，管理建筑物HVAC系统中每个加热器(制冷器)。In the present invention, the main focus is on the PAC layer, which manages the ingress of energy and controls the operation of powered applications. All power requests are submitted to the PAC layer. The PAC layer, based on the overall load situation, decides whether to accept or reject the request. The PAC layer mainly includes two modules: schedulability test and thermal control. The schedulability test module is executed to check whether the total energy budget is being respected. The thermal control module manages each heater (cooler) in the building's HVAC system based on the current temperature, the total energy budget, and the set temperature range.

之后，对问题进行建模处理。参数表如下：Afterwards, the problem is modeled. The parameter table is as follows:

1.首先，建立建筑物中热量模型1. First, build the thermal model in the building

在建筑物中，加热和制冷系统用于控制建筑物不同区域间(例如，不同房间)或建筑物不同区域同外界环境的能量传递。简化处理，本发明中只考虑加热系统(制冷系统情况类似)。加热系统控制的目标是确保房间中每个区域温度都维持在舒适的范围。每个区域内的热动力学，都被以下三个因素影响：室外能量(例如，太阳辐射到建筑物外表面或是同外界环境的热交换)，室内能量(例如，加热器或其它设备产生的热量，人的活动等)，区域间能量(例如，热量从一个房间通过墙传到另一个房间)。In buildings, heating and cooling systems are used to control the transfer of energy between different areas of a building (for example, different rooms) or between different areas of a building and the external environment. To simplify the process, only the heating system is considered in the present invention (the situation of the refrigeration system is similar). The goal of heating system control is to ensure that the temperature in each zone of the room is maintained within a comfortable range. The thermal dynamics within each zone are affected by three factors: outdoor energy (for example, solar radiation onto the exterior of a building or heat exchange with the outside environment), indoor energy (for example, heaters or other equipment producing heat, human activity, etc.), interzone energy (e.g., heat transfer from one room to another through a wall).

对于有n个加热器的第j个区域来说，通过能量守恒定律，我们可以得到热平衡方程式为For the jth area with n heaters, through the law of energy conservation, we can get the heat balance equation as

t表示时间，F表示区域个数，N_j表示区域j中加热器个数，σ_j表示维纳噪声变量；t represents time, F represents the number of regions, N _j represents the number of heaters in region j, σ _j represents the Wiener noise variable;

在第j个区域的系统模型中，系统状态是室内空气温度，系统输入是加热器功率。扰动因数包含三个方面，外界温度、太阳辐射热量、标准维纳噪声。相关参数含义详见参数表。In the system model of region j, the system state is the indoor air temperature and the system input is the heater power. The disturbance factor includes three aspects, external temperature, solar radiation heat, and standard Wiener noise. See the parameter table for the meaning of relevant parameters.

加热器的控制输入遵循不同的操作情况表示为其中表示加热器OFF(关)，表示加热器ON(开)。用表示相应加热器的功率，则实际加热器功率为The heater control input follows different operating conditions Expressed as in Indicates that the heater is OFF (closed), Indicates that the heater is ON (open). use Indicates the power of the corresponding heater, then the actual heater power for

${Φ Φ}_{j j}^{n no} = = {P P}_{j j}^{n no} {u u}_{j j}^{n no} . . - - - - - - ((22))$

因此，加热器可以通过ON-OFF控制。其中，ON状态下，加热器的功率为常数，不变。Therefore, the heater can be controlled by ON-OFF. Among them, in the ON state, the power of the heater is constant and does not change.

2.状态空间下的热力学模型2. Thermodynamic model in state space

设定为建筑物中的区域数，并且每个第j个区域有N_j个加热器。因此，总共有个加热器。用X＝[T₁，T₂，…，T_F]^T表示状态向量，用表示N_j个加热器的控制输入向量，用U＝[U¹,U²,…,U^F]^T表示系统控制输入向量。用表示第j个区域中的扰动。系统扰动向量表示为W＝[W¹,W²,…,W^F]^T。set up is the number of zones in the building, and each jth zone has N _j heaters. Therefore, a total of a heater. Use X=[T ₁ , T ₂ ,..., T _F ] ^T to represent the state vector, use Denote the control input vectors of N _j heaters, and use U=[U ¹ , U ² ,..., U ^F ] ^T to denote the system control input vectors. use Denotes the perturbation in the jth region. The system disturbance vector is expressed as W=[W ¹ ,W ² ,...,W ^F ] ^T .

状态空间模型表示为The state-space model is expressed as

$\overset{\cdot &Center Dot;}{X x} = = AX AX + + BU BU + + DW DW,,$

Y＝X， ⑶Y=X, ⑶

其中，表示系统状态导数，X表示系统状态向量，A表示状态空间模型参数，B表示状态空间模型参数，D表示状态空间模型参数，W表示系统扰动，Y表示系统控制输出向量，U表示系统输入向量；in, Represents the system state derivative, X represents the system state vector, A represents the state space model parameters, B represents the state space model parameters, D represents the state space model parameters, W represents the system disturbance, Y represents the system control output vector, U represents the system input vector;

系统矩阵定义为：The system matrix is defined as:

B＝diag(B₁ B₂ … B_F)，B=diag(B ₁ B ₂ ... B _F ),

D＝diag(D₁ D₂ … D_F)D=diag(D ₁ D ₂ … D _F )

A^F表示A矩阵中变量参数名，由下式定义，C_F表示F个区域相应热容，B_F表示B矩阵中变量参数名，由下式定义，D_F表示D矩阵中变量参数名，由下式定义；A ^F represents the variable parameter name in the A matrix, which is defined by the following formula, C _F represents the corresponding heat capacity of F regions, B _F represents the variable parameter name in the B matrix, and is defined by the following formula, D _F represents the variable parameter name in the D matrix, is defined by the following formula;

对于j＝1,2,…,FFor j=1,2,...,F

${A A}^{j j} = = - - \frac{11}{{R R}_{j j}^{a a} {C C}_{j j}} - - \frac{11}{{C C}_{j j}} {Σ Σ}_{i i = = 11,, i i &NotEqual; &NotEqual; j j}^{F f} \frac{11}{{R R}_{ij ij}^{T T}},,$

${B B}_{j j} = = (\begin{matrix} \frac{{P P}_{j j}^{11}}{{C C}_{j j}} & \frac{{P P}_{j j}^{22}}{{C C}_{j j}} & . . . . . . & \frac{{P P}_{j j}^{{N N}_{j j}}}{{C C}_{j j}} \end{matrix}),,$

${D D.}_{j j} = = (\begin{matrix} \frac{11}{{R R}_{j j}^{a a} {C C}_{j j}} & \frac{{A A}_{j j}^{w w}}{{C C}_{j j}} & \frac{{σ σ}_{j j}^{T T}}{{C C}_{j j}} \end{matrix}) . .$

其中，表示功率输入速率，表示维纳噪声变量，R表示两区域间等效电阻；in, Indicates the power input rate, Represents the Wiener noise variable, and R represents the equivalent resistance between the two regions;

为了简化设计和分析，我们将连续时间模型转换成离散时间模型。To simplify design and analysis, we convert the continuous-time model to a discrete-time model.

X(k)＝ΦX(k-1)+GU(k-1)+HW(k-1)，X(k)=ΦX(k-1)+GU(k-1)+HW(k-1),

Y(k)＝X(k)， (4)Y(k)=X(k), (4)

其中，是第k次采样间隔T_s下系统状态，离散时间系统模型矩阵表示为 $Φ = e^{A T_{8}}, G = {&Integral;}_{0}^{T_{8}} e^{As} Bds,$ 和 $H = {&Integral;}_{0}^{T_{8}} e^{As} Dds .$ in, is the system state at the kth sampling interval T _s , and the discrete-time system model matrix is expressed as $Φ = e^{A T_{8}}, G = {&Integral;}_{0}^{T_{8}} e^{As} Bds,$ and $h = {&Integral;}_{0}^{T_{8}} e^{As} Dds .$

其中，T_s表示时间间隔，e表示常数e，s表示积分项。Among them, T _s represents the time interval, e represents the constant e, and s represents the integral term.

3.可调度性测试3. Schedulability test

控制的目标是使建筑物不同区域的室内空气温度Y保持在一个舒适的区间。将区间的上下界分别表示为Y_l和Y_u The goal of the control is to keep the indoor air temperature Y in a comfortable range in different areas of the building. Denote the upper and lower bounds of the interval as Y _l and Y _u respectively

Y_l≤Y≤Y_u. ⑸Y _l ≤ Y ≤ _{Y u} .

控制系统服从于由总能耗负载预算决定的能耗负载限制，可表示为：The control system is subject to the energy load limit determined by the total energy load budget, which can be expressed as:

$Ψ^{j} = [P_{j}^{1}, P_{j}^{2}, . . ., P_{j}^{N_{j}}],$ 是建筑物制热系统中总能耗预算，Ψ表示F个区域加热器功率输入速率矩阵，Ψ^F表示第F个区域加热器的功率输入速率向量，表示1*L的实向量，L表示总加热器个数，Ψ^j表示第j个区域加热器的功率输入速率向量，表示功率输入速率，N_j表示第j个区域有N_j个加热器； $Ψ^{j} = [P_{j}^{1}, P_{j}^{2}, . . ., P_{j}^{N_{j}}],$ is the total energy consumption budget in the building heating system, Ψ represents the power input rate matrix of F district heaters, Ψ ^F represents the power input rate vector of the Fth district heater, represents a real vector of 1*L, L represents the total number of heaters, Ψ ^j represents the power input rate vector of the j-th regional heater, Indicates the power input rate, and N _j indicates that there are N _j heaters in the jth area;

另外，加热器的输入必须是ON/OFF状态：Additionally, the heater input must be ON/OFF:

$U u = = {[[{u u}_{j j}^{i i}]]}^{L L},, &ForAll; &ForAll; {u u}_{j j}^{i i} &Element; &Element; {{0,1 0,1}} . . - - - - - - ((66))$

其中，U表示加热器ON/OFF状态向量，表示第j个区域第i个加热器的ON/OFF状态，L表示总加热器个数；Among them, U represents the heater ON/OFF state vector, Indicates the ON/OFF status of the i-th heater in the j-th area, and L indicates the total number of heaters;

因此，可调度性问题为，在自动进行热量管理的同时，满足约束(5)和(6)。Thus, the schedulability problem is to satisfy constraints (5) and (6) while automatically performing thermal management.

我们使用k-可调度性来解决问题，即保证k个加热器的情况下是可调度的。系统的控制输入U被考虑为对单个加热器的ON/OFF状态进行控制。如果在U输入的情况下，能够满足将所有的区域温度调整到舒适区间，并且满足能耗预算限制，那么我们称这个输入为“safe”。更进一步，我们定义系统“Safe”为：存在时间τ≥0，使得当t≥τ时Y(t)∈Safe，其中，t表示t时刻，Y(t)表示系统输出量。我们称系统为k可调度的，当满足：存在安全调度U，使得其中，‖·‖₁表示1-范数，U(t)表示系统的控制输入，表示第j个区域第i个加热器的ON/OFF状态，k表示可调度性度量k。We use k-schedulability to solve the problem, i.e. k heaters are guaranteed to be schedulable. The control input U of the system is considered to control the ON/OFF state of the individual heaters. If in the case of the U input, it is possible to adjust the temperature of all areas to the comfort zone and meet the energy budget constraints, then we call this input "safe". Furthermore, we define the system "Safe" as: the existence time τ≥0, so that Y(t)∈Safe when t≥τ, where t represents the time t, and Y(t) represents the system output. We call the system k-schedulable if: there is a safe schedule U such that Among them, ‖· _‖1 represents the 1-norm, U(t) represents the control input of the system, Indicates the ON/OFF state of the i-th heater in the j-th region, and k represents the schedulability measure k.

进一步，我们定义，当存在η＝[η_i]∈[0,1]^F,使得并且满足-CA^-1(D+Bη)∈int(Safe)。则系统显然也是k可调度的。Further, we define that when there exists η=[η _i ]∈[0,1] ^F , such that And satisfy -CA ^-1 (D+Bη)∈int(Safe). Then the system is obviously also k-schedulable.

其中，int(Safe)表示整数集Safe；Among them, int(Safe) represents the integer set Safe;

更进一步，当存在η＝[η_i]∈[0,1]^F，使得以及-CA^-1(D+Bη)∈int(Safe)，系统为预算可调度的。其中，budget为负载预算，表示第j个区域第i个加热器的存在的η值，表示功率输入速率。Furthermore, when there exists η=[η _i ]∈[0,1] ^F , such that And -CA ^-1 (D+Bη)∈int(Safe), the system is budget-schedulable. Among them, budget is the load budget, the value of η representing the presence of the i-th heater in the j-th region, Indicates the power input rate.

基于此，我们设计本发明中的可调度性测试算法。Based on this, we design the schedulability testing algorithm in the present invention.

首先，将可调度性问题设计成为下列优化问题。First, formulate the schedulability problem as the following optimization problem.

s.t.-CA^-1(DW+Bη)∈int(Safe). (7)st-CA ^-1 (DW+Bη)∈int(Safe). (7)

其中，s.t.表示受约束于；Among them, s.t. means subject to;

集合Safe定义为The set Safe is defined as

最小的峰值负载预算budget_min可以通过该优化问题求解得到。budget_min应为不小于目标函数值的负载预算数，可以设置 The minimum peak load budget budget _min can be obtained by solving this optimization problem. budget _min should be the load budget not less than the value of the objective function, which can be set

优化问题中的限制为线性的，可以被表示为：The constraints in the optimization problem are linear and can be expressed as:

其中，Y_l表示控制输出变量的下界，Y_u表示控制输出变量的上界；Among them, Y _l represents the lower bound of the control output variable, Y _u represents the upper bound of the control output variable;

其中in

因此，该优化问题可以通过线性规划求解。Therefore, this optimization problem can be solved by linear programming.

因此，本发明的可调度性问题测试方法为，在每个时间周期内，执行以下算法：Therefore, the schedulability problem testing method of the present invention is, in each time period, execute the following algorithm:

1)解决式(7)中的最优化问题，得到需要满足的最小的负载预算budget_min 1) Solve the optimization problem in formula (7), and get the minimum load budget budget _min that needs to be satisfied

2)如果当前得到的负载预算budget＜budget_min，则执行步骤3)，否则，执行步骤4)2) If the currently obtained load budget budget<budget _min , execute step 3), otherwise, execute step 4)

3)系统向上层(LB层)请求增加能耗预算，得到一个新的budget′,重新执行步骤1)，或是放松热量限制，得到一个新的budget′_min，重新执行步骤23) The system requests to the upper layer (LB layer) to increase the energy consumption budget, get a new budget′, and re-execute step 1), or relax the calorie restriction, obtain a new _budget′min , and re-execute step 2

4)系统运行基于MPC的热量控制策略。4) The system runs a heat control strategy based on MPC.

4.基于MPC的热量控制策略4. MPC-based heat control strategy

在MPC控制计算中，控制器对输入U(k)进行控制，用来最小化如下目标函数J(K)：In the MPC control calculation, the controller controls the input U(k) to minimize the following objective function J(K):

$\begin{matrix} J J ((k k)) = = {Σ Σ}_{s the s = = 11}^{M m} {| | | | Y Y ((k k + + s the s | | k k)) - - {Y Y}_{r r} ((k k + + s the s | | k k)) | | | |}_{Q Q}^{22} \\ + + {Σ Σ}_{s the s = = 00}^{N N - - 11} {| | | | U u ((k k + + s the s | | k k)) | | | |}_{R R}^{22},, \end{matrix} - - - - - - ((99))$

M和N分别表示预测长度范围，M是控制输出向量的预测长度范围，N是扰动向量的预测长度范围，Y(k+s|k)是第k个时间后第s个采样间隔的状态预测，Y_r(k|+s|k)是第k个时间后第s个采样间隔的状态预测，U(k+s|k)第k个时间后第s个采样间隔的状态预测。Q和R分别是用来对时间误差和输入功率的补偿矩阵。M and N represent the predicted length range respectively, M is the predicted length range of the control output vector, N is the predicted length range of the disturbance vector, Y(k+s|k) is the state prediction of the sth sampling interval after the kth time , Y _r (k|+s|k) is the state prediction of the s-th sampling interval after the k-th time, and U(k+s|k) is the state prediction of the s-th sampling interval after the k-th time. Q and R are compensation matrices for time error and input power, respectively.

该费用函数J(K)主要为最小化温度追踪误差和功率需求。即使功率需求较小的同时，使得不同时间间隔间的温度变化较小。The cost function J(K) is mainly to minimize the temperature tracking error and power requirement. Even though the power demand is small, the temperature variation between different time intervals is small.

对于式(3)中的系统模型，重新变化为下列形式，For the system model in formula (3), it is changed into the following form again,

${X x}_{p p} ((k k)) = = \overset{&OverBar; &OverBar;}{X x} ((k k)) + + \overset{&OverBar; &OverBar;}{G G} {U u}_{p p} ((k k)) + + \overset{&OverBar; &OverBar;}{H h} {W W}_{c c} ((k k)),,$

Y_p(k)＝X_p(k)，Y _p (k) = X _p (k),

其中，表示状态预测向量，表示状态模型参数，表示状态模型参数；in, represents the state prediction vector, represents the state model parameters, Indicates the state model parameters;

因此，该策略中，需要在每一个时间间隔中，对该费用函数求解最小值，并满足相应约束条件。优化问题整理如下：Therefore, in this strategy, it is necessary to solve the minimum value of the cost function in each time interval and satisfy the corresponding constraints. The optimization problem is organized as follows:

其中，表示温度参考向量，表示表示F个区域加热器参考功率输入速率矩阵，表示预算参考向量，W_c表示扰动向量，表示输出参考向量下界，表示输出参考向量上界，L表示加热器个数，N表示预测长度；in, represents the temperature reference vector, Denotes the F zone heater reference power input rate matrix, represents the budget reference vector, W _c represents the disturbance vector, Indicates the lower bound of the output reference vector, Indicates the upper bound of the output reference vector, L indicates the number of heaters, and N indicates the predicted length;

相应的矩阵含义如下：The meaning of the corresponding matrix is as follows:

其中，M表示预测长度，Y_r(k+M|k)表示第k个时间后第M个采样间隔的状态预测；Among them, M represents the prediction length, and Y _r (k+M|k) represents the state prediction of the Mth sampling interval after the kth time;

${X x}_{p p} ((k k)) = = (\begin{matrix} X x ((k k + + 11 | | k k)) \\ X x ((k k + + 22 | | k k)) \\ . . \\ . . \\ . . \\ X x ((k k + + M m | | k k)) \end{matrix}),, \overset{&OverBar; &OverBar;}{X x} ((k k)) = = (\begin{matrix} ΦX ΦX ((k k)) \\ {Φ Φ}^{22} X x ((k k)) \\ . . \\ . . \\ . . \\ {Φ Φ}^{M m} X x ((k k)) \end{matrix}),,$

$\overset{&OverBar;}{Ψ} = diag (\begin{matrix} Ψ & . . . & Ψ \end{matrix}),$ $\overset{&OverBar;}{Ψ} = diag (\begin{matrix} Ψ & . . . & Ψ \end{matrix}),$

${\overset{&OverBar; &OverBar;}{Y Y}}_{u u} = = {(\begin{matrix} {Y Y}_{u u}^{T T} & . . . . . . & {Y Y}_{u u}^{T T} \end{matrix})}^{T T},, {\overset{&OverBar; &OverBar;}{Y Y}}_{l l} = = {(\begin{matrix} {Y Y}_{l l}^{T T} & . . . . . . & {Y Y}_{l l}^{T T} \end{matrix})}^{T T},,$

$\overset{&OverBar; &OverBar;}{Q Q} = = diag diag (\begin{matrix} Q Q & . . . . . . & Q Q \end{matrix}),, \overset{&OverBar; &OverBar;}{R R} = = diag diag (\begin{matrix} R R & . . . . . . & R R \end{matrix}) . .$

其中，X(k+M|k)表示第k个时间后第M个采样间隔的状态预测，M表示预测长度，N表示预测长度，U(k+N-1|k)表示第k个时间后第N-1个采样间隔的状态预测，表示第F个区域的外部空气温度；Among them, X(k+M|k) represents the state prediction of the M-th sampling interval after the k-th time, M represents the prediction length, N represents the prediction length, and U(k+N-1|k) represents the k-th time The state prediction of the next N-1th sampling interval, Indicates the outside air temperature in the Fth area;

表示时间误差补偿矩阵的矩阵，表示输入功率的补偿矩阵的矩阵，表示输出参考向量下界的矩阵，表示输出参考向量上界的矩阵，表示建筑物中的总功率预算数，表示离散状态空间模型参数矩阵； matrix representing the time error compensation matrix, The matrix representing the compensation matrix for the input power, matrix representing the lower bound of the output reference vector, matrix representing the upper bound of the output reference vector, represents the total power budget in the building, Represents the discrete state space model parameter matrix;

表示输出参考向量下界，表示输出参考向量上界，Q和R分别是用来对时间误差和输入功率的补偿矩阵； Indicates the lower bound of the output reference vector, Represents the upper bound of the output reference vector, Q and R are compensation matrices for time error and input power, respectively;

目标函数可以变形为：The objective function can be transformed into:

其中，S_Q，S_R表示 $S_{Q}^{T} S_{Q} = Q, S_{R}^{T} S_{R} = R;$ Among them, S _Q , S _R represent $S_{Q}^{T} S_{Q} = Q, S_{R}^{T} S_{R} = R;$

在该目标函数下,满足式(10)中的相应约束，该问题变形为标准的整数最小二乘优化问题，可以使用Matlab中YALMIP库进行求解。Under this objective function, the corresponding constraints in formula (10) are satisfied, and the problem is transformed into a standard integer least squares optimization problem, which can be solved by using the YALMIP library in Matlab.

所述智能建筑中基于热量预测管理的能耗控制方法，具体包括如下步骤：The energy consumption control method based on thermal forecast management in the intelligent building specifically includes the following steps:

步骤3：进行可调度性测试,判定在给定负载预算budget下，该建筑物的热量负载是否具有可调度性；Step 3: Conduct a dispatchability test to determine whether the thermal load of the building is dispatchable under a given load budget;

步骤4：运行基于MPC(Model Predictive Control，模型预测控制)的热量控制策略，对式(10)进行最小化求解，解出相应的控制输出变量。Step 4: Run the heat control strategy based on MPC (Model Predictive Control, Model Predictive Control), minimize and solve the formula (10), and solve the corresponding control output variables.

为使本发明的目的、技术方案和有点更加清楚，下面结合附图和一个具体实施例对本发明做进步地详细描述。In order to make the purpose, technical solution and advantages of the present invention clearer, the present invention will be described in detail below in conjunction with the accompanying drawings and a specific embodiment.

在该实施例中，我们选定了一个有120m²的单层建筑，每个房间都装有一个或两个加热器。其房间布局附图1所示。部分热量参数见图2以及如下配置表：In this example, we selected a single-storey building with ^120m2 , each room is equipped with one or two heaters. The room layout is shown in Figure 1. Some heat parameters are shown in Figure 2 and the following configuration table:

热量参数配置表Heat parameter configuration table

热电阻非零变量设置RTD non-zero variable setting

加热器功率消耗heater power consumption

对于MPC控制器，设置Q(1)＝…＝Q(M)＝diag(10,10,10,10,10,10,10,10),M＝5,R(1)＝…＝R(N)＝diag(1,1,1,1,1,1,1,1),N＝5,采样间隔为2分钟，即T_s＝120s。设置房间温度为22-24℃之间，For an MPC controller, set Q(1)=...=Q(M)=diag(10,10,10,10,10,10,10,10),M=5,R(1)=...=R( N)=diag(1,1,1,1,1,1,1,1), N=5, the sampling interval is 2 minutes, ie T _s =120s. Set the room temperature between 22-24°C,

将本发明介绍的方法，同不使用MPC预测控制，只使用ON-OFF方式进行比较，各房间中功率输入结果见图3-图6。将其结果汇总为图9，可以发现，MPC控制方法下的峰值负载较普通的ON-OFF策略峰值负载小。能够较好地满足能耗预算，表明系统的可调度性较好。同时，附图8-图9显示，MPC控制下的加热器，可以很好地将各个房间温度控制在设定的22-24摄氏度间，并且温度随时变化而变化的幅度较小。Comparing the method introduced in the present invention with the ON-OFF method without using MPC predictive control, the power input results in each room are shown in Fig. 3-Fig. 6. The results are summarized in Figure 9, and it can be found that the peak load under the MPC control method is smaller than that of the common ON-OFF strategy. It can better meet the energy consumption budget, indicating that the system has better schedulability. At the same time, accompanying drawings 8-9 show that the heater under the control of the MPC can well control the temperature of each room between the set 22-24 degrees Celsius, and the range of temperature changes with time is small.

以上对本发明的具体实施例进行了描述。需要理解的是，本发明并不局限于上述特定实施方式，本领域技术人员可以在权利要求的范围内做出各种变形或修改，这并不影响本发明的实质内容。Specific embodiments of the present invention have been described above. It should be understood that the present invention is not limited to the specific embodiments described above, and those skilled in the art may make various changes or modifications within the scope of the claims, which do not affect the essence of the present invention.

Claims

1. an energy consumption control method based on thermal forecast management in an intelligent building, is characterized in that, comprises the steps:

Step 1: Establish a thermal model in the building;

Step 2: Establish a thermodynamic model in the state space;

Step 3: Conduct a dispatchability test to determine whether the thermal load of the building is dispatchable under a given load budget budget;

Step 4: Run the heat control strategy based on the model predictive control MPC, minimize and solve the formula (10), and solve the corresponding control output variables;

Wherein, the step 4 is specifically as follows:

For the objective function represented by formula (11), under the constraints in formula (10), the method of solving the standard integer least squares optimization problem is used to solve:

in, represents the temperature reference vector, Denotes the F zone heater reference power input rate matrix, represents the budget reference vector, W _c (k) represents the disturbance vector, L represents the number of heaters, N represents the predicted length, represents the state prediction vector, represents the state model parameters, represents the state model parameters, Indicates the ON/OFF state of the i-th heater in the j-th area, and U _p (k) represents the new system input variable used after the transformation of formula (10);

The meaning of the substitution formula in formula (10) is as follows:

{\overset{&OverBar; &OverBar;}{Y Y}}_{r r} ((k k)) = = {(\begin{matrix} {Y Y}_{r r} ((k k + + 11 | | k k)) & {Y Y}_{r r} ((k k + + 22 | | k k)) & . . . . . . & {Y Y}_{r r} ((k k + + M m | | k k)) \end{matrix})}^{T T} . .

\overset{&OverBar; &OverBar;}{X x} ((k k)) = = (\begin{matrix} ΦX ΦX ((k k)) \\ {Φ Φ}^{22} X x ((k k)) \\ . . \\ . . \\ . . \\ {Φ Φ}^{M m} X x ((k k)) \end{matrix})

{U u}_{p p} ((k k)) = = (\begin{matrix} U u ((k k | | k k)) \\ U u ((k k + + 11 | | k k)) \\ . . \\ . . \\ . . \\ U u ((k k + + N N - - 11 | | k k)) \end{matrix}),,

{W W}_{c c} ((k k)) = = {(\begin{matrix} {T T}_{a a}^{11} & {Φ Φ}_{s the s}^{11} & O o & . . . . . . & {T T}_{a a}^{F f} & {Φ Φ}_{s the s}^{F f} & O o \end{matrix})}^{T T},,

\overset{&OverBar; &OverBar;}{Ψ Ψ} = = diag diag (\begin{matrix} Ψ Ψ & . . . . . . & Ψ Ψ \end{matrix}),,

Among them, M represents the prediction length, Y _r (k+M|k) represents the state prediction of the M-th sampling interval after the k-th time; X(k+M|k) represents the M-th sampling interval after the k-th time The state prediction of , M represents the prediction length, N represents the prediction length, U(k+N-1|k) represents the state prediction of the N-1th sampling interval after the kth time, Indicates the outside air temperature in the Fth region, X(k) represents the system state at time k, R represents the equivalent resistance between the two regions, Ψ represents the power input rate matrix of F regional heaters, G represents the discrete state space model parameters, H Represents the discrete state space model parameters, Φ represents the discrete state space model parameters, represents the total power budget in the building, represents the power input rate vector of the Fth zone heater;

{min min}_{{U u}_{p p} ((k k))} {||\begin{matrix} {S S}_{Q Q} \overset{&OverBar; &OverBar;}{G G} {U u}_{p p} ((k k)) - - {S S}_{Q Q} {\overset{&OverBar; &OverBar;}{Y Y}}_{r r} ((k k)) \\ {S S}_{R R} {U u}_{p p} ((k k)) \end{matrix}||}^{22} - - - - - - ((1111))

in,

S_{Q}^{T} S_{Q} = Q, S_{R}^{T} S_{R} = R,

S _Q represents the square root matrix, Represents the S _Q transpose matrix, S _R represents the square root matrix, Represents the S _R transpose matrix, U _p (k) represents the new system input variable used after the transformation of equation (10); Q and R are compensation matrices for time error and input power, respectively.

2. The energy consumption control method based on thermal forecast management in the intelligent building according to claim 1, wherein the thermal model is specifically as follows:

For the jth region with n heaters, through the law of energy conservation, the heat balance equation is obtained as

\begin{matrix} \frac{{dT dT}_{j j}}{dt dt} = = \frac{11}{{R R}_{j j}^{a a} {C C}_{j j}} (({T T}_{a a}^{j j} - - {T T}_{j j})) + + \frac{11}{{C C}_{j j}} {Σ Σ}_{i i = = 11,, i i &NotEqual; &NotEqual; j j}^{F f} \frac{11}{{R R}_{ij ij}^{r r}} (({T T}_{i i} - - {T T}_{j j})) \\ + + \frac{11}{{C C}_{j j}} {A A}_{j j}^{w w} {Φ Φ}_{s the s}^{j j} + + \frac{11}{{C C}_{j j}} {Σ Σ}_{n no = = 11}^{{N N}_{j j}} {Φ Φ}_{j j}^{n no} + + \frac{11}{{C C}_{j j}} {σ σ}_{j j} {ω ω}_{j j},, \end{matrix} - - - - - - ((11))

t represents time, F represents the number of regions, N _j represents the number of heaters in region j, σ _j represents the Wiener noise variable, T _j represents the internal temperature of the jth region, Indicates the thermal resistance between the jth area and the outside, C _j indicates the heat capacity of the jth area, Indicates the external temperature of the jth region, Indicates the thermal resistance of the i-th area and the j-th area, T _i indicates the internal temperature of the i-th area, Indicates the effective window area that the solar radiation Φ _s can enter, Indicates the energy change produced by solar radiation, Indicates the power of the nth heater in the jth region, and ω _j represents the standard Wiener noise;

In the system model of the jth area, the system state is the indoor air temperature, and the system input is the heater power; the disturbance factor includes three aspects, the external temperature, the solar radiation heat, and the standard Wiener noise.

3. The energy consumption control method based on thermal forecast management in the intelligent building according to claim 1, wherein the thermodynamic model is as follows:

X(k)=ΦX(k-1)+GU(k-1)+HW(k-1),

Y(k)=X(k), ⑷

X(k) represents the state of the system at time k, Φ represents the parameters of the discrete state space model, X(k-1) represents the state of the system at time k-1, G represents the parameters of the discrete state space model, U(k-1) represents k-1 Time system input, H represents discrete state space model parameters, W(k-1) represents system disturbance at time k-1, Y(k) represents system control output at time k;

in, is the system state at the kth sampling interval T _s , and the discrete-time system model matrix is expressed as

Φ = e^{ATs}, G = {&Integral;}_{0}^{Ts} e^{As} Bds,

and

h = {&Integral;}_{0}^{Ts} e^{As} Dds .

Among them, T _s represents the time interval, e represents the constant e, s represents the integral term, A represents the state space model parameters, B represents the state space model parameters, D represents the state space model parameters, G represents the discrete state space model parameters, H represents the discrete State-space model parameters.

4. The energy consumption control method based on thermal forecast management in an intelligent building according to claim 1, wherein said step 3 comprises the following steps:

Step 3.1: Obtain the minimum load budget budget _min that needs to be satisfied through the schedulability test algorithm;

Step 3.2: If the currently obtained load budget budget<budget _min , go to step 3.3; otherwise, go to step 3.4;

Step 3.3: Request to increase the energy consumption load budget, get a new load budget budget′, re-execute step 3.1, or relax the calorie restriction, obtain a new minimum load _budget′min , and re-execute step 3.2;

Step 3.4: Execute step 4.

5. The energy consumption control method based on thermal forecast management in an intelligent building according to claim 4, wherein the schedulability test algorithm is specifically:

First, formulate the schedulability problem as the following optimization problem:

\underset{η η}{min min} {Σ Σ}_{j j = = 11}^{F f} {Σ Σ}_{i i = = 11}^{{N N}^{j j}} {η η}_{j j}^{i i} {P P}_{j j}^{i i},,

st-CA ^-1 (DW+Bη)∈int(Safe). (7)

Among them, s.t. means subject to;

Define that there exists η=[η _j ]∈[0,1] ^F , such that

Σ_{j = 1}^{f} Σ_{i = 1}^{N^{j}} η_{j}^{i} P_{j}^{i} \leq budget

η _j represents a number in the existing [0,1] interval, [ ] ^F represents the F-dimensional vector, F represents F-schedulability, N ^j represents the number of the jth district heater, the value of η representing the presence of the i-th heater in the j-th region, Indicates the power input rate;

A represents the parameters of the state space model, B represents the parameters of the state space model, D represents the parameters of the state space model, W represents the system disturbance, and C represents the parameters of the state space model;

The set Safe is defined as

[[{Y Y}_{l l}^{11},, {Y Y}_{u u}^{11}]] \times \times [[{Y Y}_{l l}^{22},, {Y Y}_{u u}^{22}]] \times \times . . . . . . \times \times [[{Y Y}_{l l}^{F f},, {Y Y}_{u u}^{F f}]] . .

where Y _l ^F represents the lower bound vector of the Fth district heater, represents the upper bound vector of the Fth district heater;

The minimum load budget budget _min is obtained by solving this optimization problem; budget _min should be the load budget number budget not less than the value of the objective function, set

The constraints in optimization problems are linear and are expressed as:

Y _l +CA ^-1 DW<-CA ^-1 BPη<Y _u +CA ^-1 DW.⑻

Among them, Y _l represents the lower bound of the control output variable, Y _u represents the upper bound of the control output variable, and P represents the power input vector;

in

Y _l = [Y _l ¹ , Y _l ² ,..., Y _l ^F ] ^T ,

{Y Y}_{u u} = = {[[{Y Y}_{u u}^{11},, {Y Y}_{u u}^{22},, . . . . . .,, {Y Y}_{u u}^{F f}]]}^{T T} . .

The optimization problem is solved by linear programming.