技术领域technical field
本发明属于智能电网和空调控制领域，具体涉及一种基于PMV模型的居民电器混合控制方法。The invention belongs to the field of smart grid and air-conditioning control, and in particular relates to a mixed control method for residential electrical appliances based on a PMV model.
背景技术Background technique
随着居民空调和电暖设备功率和数量的不断增加，用电需求急剧增长，使居民这部分电器负荷对电网用电峰值的影响越来越大。比如夏天持续高温天气中，空调降温作为主要的用电消耗，在同一时段的大规模使用将使电网负荷持续攀升，给电网安全带来严重隐患。为避免超负荷引起的电网故障，供电公司目前只能按序限电，从而给被限电居民的生活造成极大不便。然而，根据实时电价信息，居民用户可以按照需求响应的方式，在电网用电高峰时段通过适当降低舒适度，即降低用电器功率，来降低电网峰值负荷，从而反过来保证每户居民的用电舒适度。With the continuous increase in the power and quantity of residential air conditioners and electric heating equipment, the demand for electricity has increased sharply, making this part of the electrical load of residents have an increasing impact on the peak power consumption of the grid. For example, in the continuous high temperature weather in summer, air-conditioning cooling is the main power consumption. Large-scale use at the same time will cause the grid load to continue to rise, bringing serious hidden dangers to grid security. In order to avoid power grid failures caused by overload, power supply companies can only limit power in sequence at present, thus causing great inconvenience to the lives of residents who have been limited by electricity. However, according to the real-time electricity price information, residential users can reduce the peak load of the grid by appropriately reducing the comfort level during the peak period of grid electricity consumption, that is, reducing the power of electrical appliances, thereby ensuring the electricity consumption of each household in turn. comfort.
传统的室内温度在进行调节时，虽然能够控制在设定温度，但是没有考虑其它因素，除了室温以外，室内热舒适度还受相对湿度、风速、辐射温度、居民的衣服热阻以及人体的新陈代谢率影响。When the traditional indoor temperature is adjusted, although it can be controlled at the set temperature, other factors are not considered. In addition to the room temperature, the indoor thermal comfort is also affected by relative humidity, wind speed, radiation temperature, the thermal resistance of residents' clothes and the metabolism of the human body. rate impact.
PMV(Predicted Mean Vote)是当前国际公认的描述室内热环境的一个指标，可以比较客观的反映室内热环境的热舒适度。PMV模型的预测结果与人体的感知是基本一致的，因此基于热舒适度的空调控制方法也取得了很好的效果。但是，目前针对居民热舒适度的研究仅考虑对电器控制方法优化以提高舒适度，对于利用PMV模型实现有效平衡热舒适度和电网负荷的研究尚不全面。PMV (Predicted Mean Vote) is currently an internationally recognized indicator for describing the indoor thermal environment, which can objectively reflect the thermal comfort of the indoor thermal environment. The prediction results of the PMV model are basically consistent with the perception of the human body, so the air conditioning control method based on thermal comfort has also achieved good results. However, the current research on residents' thermal comfort only considers the optimization of electrical control methods to improve comfort, and the research on using the PMV model to effectively balance thermal comfort and grid load is not comprehensive.
发明内容SUMMARY OF THE INVENTION
针对现有技术存在的不足，本发明的目的是提出一种基于PMV模型的居民电器混合控制方法，以实现有效平衡和优化居民热舒适度和电网负荷。Aiming at the deficiencies of the prior art, the purpose of the present invention is to propose a mixed control method for residential electrical appliances based on the PMV model, so as to effectively balance and optimize the thermal comfort of the residents and the load of the power grid.
一种基于PMV模型的居民电器混合控制方法，包括以下步骤：A hybrid control method for residential electrical appliances based on a PMV model, comprising the following steps:
步骤1，获取PMV模型中参数的数据；Step 1, obtain the data of the parameters in the PMV model;
步骤2，根据在t时间的实时电价y(t)计算热舒适度的值：Step 2: Calculate thermal comfort according to the real-time electricity price y(t) at time t The value of:
在夏季时，有：During summer, there are:
在冬季时，有：During winter there are:
其中，σ∈[4,10]和α∈[0.5,1]表示用户自定义参数；Among them, σ∈[4,10] and α∈[0.5,1] represent user-defined parameters;
步骤3，将计算得到的热舒适度的值代入到PMV模型中，利用优化过的粒子群算法求解出室内最优温度；Step 3, will calculate the thermal comfort The value of is substituted into the PMV model, and the optimal indoor temperature is solved by using the optimized particle swarm algorithm;
步骤4，根据得到的室内最优温度对家用电器进行调节。In step 4, the household appliances are adjusted according to the obtained optimal indoor temperature.
进一步地，所述的PMV模型为：Further, described PMV model is:
PMV＝[0.303·exp(-0.036·M)+0.028]·{(M-W)PMV=[0.303·exp(-0.036·M)+0.028]·{(M-W)
-3.05·10-3·[5733-6.99·(M-W)-pa]-3.05·10-3 ·[5733-6.99·(MW)-pa ]
-0.42·[(M-W)-58.15]-1.7·10-5·M·(5867-pa)-0.42·[(MW)-58.15]-1.7·10-5 ·M·(5867-pa )
-0.0014·M·(34-Tair)-3.96·10-8·fcl·[(Tcl+273)4-0.0014 · M · (34-Tair ) -3.96 · 10-8 · fcl · [(Tcl +273)4
-(TMRT+273)4]-fcl·hc·(Tcl-Tair)}-(TMRT +273)4 ]-fcl hc (Tcl -Tair )}
其中：in:
Tcl＝35.7-0.028·(M-W)-Icl·{3.96·10-8·fcl·[(Tcl+273)4-(TMRT+273)4]+fcl·hc·(Tcl-Tair)}Tcl =35.7-0.028·(MW)-Icl ·{3.96·10-8 ·fcl ·[(Tcl +273)4 -(TMRT +273)4 ]+fcl ·hc ·(Tcl -Tair )}
M表示单位面积人体新陈代谢率，单位：W/m2；W表示单位面积人体做功率，单位：W/m2；pa为室内空气中水蒸气分压力，单位：Pa；Tair表示室内环境温度，单位：℃；fcl表示人体穿衣与裸体表面积之比，Tcl表示衣服表面温度，单位：℃；TMRT表示平均室内辐射温度，单位：℃；hc为对流热交换系数，单位：W/(m2·K)；vair表示室内空气流速，单位：m/s；Icl表示衣服热阻，单位：m2·K/W。M represents the metabolic rate of the human body per unit area, unit: W/m2 ; W represents the power of the human body per unit area, unit: W/m2 ; pa is the partial pressure of water vapor in the indoor air, unit: Pa; Tair represents the indoor environment Temperature, unit: °C; fcl is the ratio of the surface area of clothing to naked body, Tcl is the surface temperature of clothing, unit: ° C; TMRT is the average indoor radiation temperature, unit: ° C; hc is the convective heat exchange coefficient, unit : W/(m2 ·K); vair is the indoor air flow rate, unit: m/s; Icl is the thermal resistance of clothes, unit: m2 ·K/W.
进一步地，步骤3所述的利用优化过的粒子群算法求解出室内最优温度包括：Further, using the optimized particle swarm algorithm described in step 3 to solve the indoor optimal temperature includes:
3.1，生成m个粒子，以室内温度设定值作为粒子的位置其中k是迭代次数，i是粒子序号i∈{1,2,...,m}；3.1, generate m particles, and use the indoor temperature setting value as the position of the particles where k is the number of iterations, i is the particle number i∈{1,2,...,m};
3.2，生成粒子的初始位置计算的适应度值比较所有粒子的适应度值大小，更新的个体最优值和全局最优值3.2, the initial position of the generated particle calculate fitness value of Compare the fitness values of all particles size, update The individual optimal value of and the global optimum
其中abs函数表示取绝对值，为将粒子的位置作为室内温度值带入的PMV模型中的简化表示；The abs function represents taking the absolute value, the position of the particle Simplified representation in the PMV model brought in as an indoor temperature value;
3.3，将更新过的个体最优值和全局最优值带入：3.3, the updated individual optimal value and the global optimum Bring in:
与and
更新粒子的速度与位置，其中是第i个粒子第k次迭代的速度，w是惯性权重，c1、c2是学习因子，rand()是介于(0,1)之间的随机数；update particle velocity and position, where is the velocity of the k-th iteration of the i-th particle, w is the inertia weight, c1 and c2 are the learning factors, and rand() is a random number between (0, 1);
3.4，若迭代次数k达到设定的次数，输出全局最优值即室内最优温度若迭代次数k未达到设定的次数，则将迭代次数设定为k+1，并重新开始执行步骤3.2。3.4, if the number of iterations k reaches the set number of times, output the global optimal value the optimal indoor temperature If the number of iterations k does not reach the set number of times, set the number of iterations to k+1, and restart step 3.2.
4.根据权利要求1和3所述的基于PMV模型的居民电器混合控制方法，其特征在于，所述的生成粒子的初始位置包括使用传统随机方法生成粒子的初始位置和利用4. the household electrical appliance hybrid control method based on PMV model according to claim 1 and 3, is characterized in that, the initial position of described generation particle Including the use of traditional random methods to generate the initial position and utilization of particles
优化生成粒子的初始位置，其中为最近一次运行粒子群算法得到的室内温度最优值，γ为搜索半径，rand()为介于(0,1)之间的随机数；Optimize the initial position of the generated particles, where is the optimal value of indoor temperature obtained by running the particle swarm algorithm last time, γ is the search radius, and rand() is a random number between (0, 1);
与现有技术相比，本发明的有益效果是：Compared with the prior art, the beneficial effects of the present invention are:
(1)通过PMV模型求出了均衡热舒适度和电网负荷的室温设定值，从而在保证人体热舒适度的前提下降低电网负荷，以达到经济、安全、节能、减排的目的。(1) The room temperature setting value of equilibrium thermal comfort and power grid load is obtained through PMV model, so as to reduce power grid load under the premise of ensuring human thermal comfort, so as to achieve the purpose of economy, safety, energy saving and emission reduction.
(2)对PSO算法进行了优化，从而在求解室内最优温度时效果更好。(2) The PSO algorithm is optimized, so that the effect is better in solving the optimal indoor temperature.
附图说明Description of drawings
图1为本发明智能混合控制方法的工作流程图；Fig. 1 is the working flow chart of the intelligent hybrid control method of the present invention;
图2为PMV模型及标尺示意图；Fig. 2 is the schematic diagram of PMV model and scale;
图3为最优温度设定值求解流程图；Fig. 3 is the optimal temperature setting value solution flow chart;
图4为对比有无负载均衡下的室内温度设定值和PMV值的实验数据曲线图。Figure 4 is a graph of experimental data comparing the indoor temperature setting value and PMV value with or without load balancing.
图5为对比有无负载均衡下的电网总负载的实验数据曲线图。Figure 5 is a graph of experimental data comparing the total load of the power grid with and without load balancing.
具体实施方式Detailed ways
如图1所示，一种基于PMV模型的居民电器混合控制方法，包括以下步骤：As shown in Figure 1, a hybrid control method for residential electrical appliances based on PMV model includes the following steps:
步骤1，获取PMV模型中参数的数据；Step 1, obtain the data of the parameters in the PMV model;
PMV(Predicted Mean Vote)，预测平均评价，指数是以人体热平衡的基本方程式以及心理生理学主观热感觉的等级为出发点，考虑了人体热舒适感诸多有关因素的全面评价指标，范围在[-3,3]之间，PMV值从小到大表示热舒适度由冷到热。PMV (Predicted Mean Vote), the predicted average evaluation, the index is based on the basic equation of the human body's heat balance and the level of psychophysiological subjective thermal sensation as a starting point, and a comprehensive evaluation index that considers many factors related to human thermal comfort. 3], the PMV value from small to large indicates thermal comfort from cold to hot.
所述的PMV模型为：The PMV model described is:
其中in
公式(1)可简化表示为：Formula (1) can be simplified as:
PMV＝F(Tair,TMRT,vair,hc,Icl,M) (5)PMV=F(Tair ,TMRT ,vair ,hc ,Icl ,M) (5)
M表示单位面积人体新陈代谢率，单位：W/m2，表示单位面积人体做功率，典型活动新陈代谢率如表1所示；M represents the metabolic rate of the human body per unit area, unit: W/m2 , represents the power of the human body per unit area, and the typical metabolic rate of activities is shown in Table 1;
pa为室内空气中水蒸气分压力，单位：Pa，设定为0.1MPa；pa is the partial pressure of water vapor in the indoor air, unit: Pa, set to 0.1MPa;
Tair表示室内环境温度，单位：℃，一般的室内温度在16℃～30℃之间；Tair represents the indoor ambient temperature, unit: °C, the general indoor temperature is between 16 °C and 30 °C;
fcl表示人体穿衣与裸体表面积之比，在冬季室内着衣量之比设定为1.15，在夏季室内着衣量之比设定为1.1；fcl represents the ratio of the surface area of human clothing to naked body, the ratio of indoor clothing in winter is set to 1.15, and the ratio of indoor clothing in summer is set to 1.1;
Tcl表示衣服表面温度，单位：℃；Tcl represents the surface temperature of the clothes, unit: °C;
TMRT表示平均室内辐射温度，单位：℃，设定为24℃；TMRT represents the average indoor radiation temperature, unit: °C, set to 24 °C;
hc为对流热交换系数，单位：W/(m2·K)；hc is the convective heat exchange coefficient, unit: W/(m2 ·K);
vair表示室内空气流速，单位：m/s，设定为0.1m/s；vair represents the indoor air velocity, unit: m/s, set to 0.1m/s;
Icl表示衣服热阻，单位：m2·K/W，典型服装热阻如表2所示。Icl represents the thermal resistance of clothing, unit: m2 ·K/W. Typical clothing thermal resistance is shown in Table 2.
表1典型活动新陈代谢率(1met＝58.1W/m2)Table 1 Typical activity metabolic rate (1met=58.1W/m2 )
表2典型服装热阻Table 2 Typical garment thermal resistance
步骤2，根据在t时间的实时电价y(t)计算热舒适度的值：Step 2: Calculate thermal comfort according to the real-time electricity price y(t) at time t The value of:
本实施例中考虑到由于时间不同而产生的电价不同，故当在t时间时，获取当地实时电价信息为y(t)，实时电价y(t)的范围一般在[0,2]之间，建立实时电价y(t)与热舒适度值的相关函数：In this embodiment, considering that the electricity prices are different due to different times, when the time t is t, the local real-time electricity price information is obtained as y(t), and the range of the real-time electricity price y(t) is generally between [0, 2] , establish real-time electricity price y(t) and thermal comfort value The relevant function of :
在夏季时，有：During summer, there are:
在冬季时，有：During winter there are:
其中，σ和α表示用户自定义参数，居民可以对不同热舒适度范围定义进行个性化设置，其中σ∈[4,10]为函数幅值，α∈[0.5,1]表示在夏季时，用户可以接受的PMV值的范围为[0,α]，在冬季时，用户可以接受的PMV值为[-α,0]。本实施例中,设定用户在夏季热舒适度的范围在[0,0.8]之间，冬季时在[-0.8,0]之间。Among them, σ and α represent user-defined parameters, and residents can customize the definition of different thermal comfort ranges, where σ∈[4,10] is the function amplitude, and α∈[0.5,1] indicates that in summer, The acceptable range of PMV value for users is [0, α], and in winter, the acceptable PMV value for users is [-α, 0]. In this embodiment, the range of the user's thermal comfort is set between [0, 0.8] in summer, and between [-0.8, 0] in winter.
步骤3，将计算得到的热舒适度的值代入到PMV模型中，利用优化过的粒子群算法求解出室内最优温度；Step 3, will calculate the thermal comfort The value of is substituted into the PMV model, and the optimal indoor temperature is solved by using the optimized particle swarm algorithm;
这里由PMV的简化公式(5)，求出函数F关于室内温度的反函数G，那么可以通过实时电价y(t)时求出的最优PMV值(即PMV*)时的最优室内温度表示为：Here, the inverse function G of the function F with respect to the indoor temperature is obtained from the simplified formula (5) of PMV, then the optimal indoor temperature at the time of the optimal PMV value (ie PMV* ) can be obtained through the real-time electricity price y(t) Expressed as:
然而，PMV值的计算过程中涉及迭代计算，无法直接找到反函数表达式G，因此，采用改进的粒子群算法(PSO)，基于PMV模型反向求解出最优热舒适值下的最优温度具体包括以下几个步骤：However, iterative calculation is involved in the calculation of the PMV value, and the inverse function expression G cannot be found directly. Therefore, an improved particle swarm algorithm (PSO) is used to reversely solve the optimal temperature under the optimal thermal comfort value based on the PMV model. Specifically, it includes the following steps:
3.1，在本实施例中设置m＝50，即生成50个粒子，以室内温度设定值作为粒子的位置其中k是迭代次数，i是粒子序号，i＝1、2、3…50，其范围是3.1. In this embodiment, m=50 is set, that is, 50 particles are generated, and the indoor temperature setting value is used as the position of the particles where k is the number of iterations, i is the particle number, i=1, 2, 3...50, the range is
3.2，生成粒子的初始位置在居民家用电器环境中，不同的时间段内的室内最优温度是不同的，所以需要多次进行求解室内最优温度，在第一次求解室内最优温度时，采用传统的随机生成方法对粒子进行初始化，在之后重新运行粒子群算法求解新的室内最优温度时，按照以下公式对粒子的位置进行初始化：3.2, the initial position of the generated particle In the residential household appliance environment, the indoor optimal temperature is different in different time periods, so it is necessary to solve the indoor optimal temperature multiple times. When solving the indoor optimal temperature for the first time, the traditional random generation method is used to The particles are initialized, and when the particle swarm algorithm is re-run to solve the new indoor optimal temperature, the positions of the particles are initialized according to the following formula:
其中为最近一次运行粒子群算法得到的室内温度最优值，即最近一次运行本方法所得到的室内最优温度，γ为搜索半径，本实施例为3，rand()为介于(0,1)之间的随机数。in is the optimal indoor temperature value obtained by the latest running of the particle swarm algorithm, that is, the indoor optimal temperature obtained by the latest running of this method, γ is the search radius, which is 3 in this embodiment, and rand() is between (0,1 ) between random numbers.
计算所有粒子的位置的适应度值比较适应度值大小，当适应度值越小时，则越优，得出个体最优值和全局最优值，个体最优值指的是单个粒子所找到的历史最优解，全局最优值指的是整个种群目前找到的最优解：Calculate the positions of all particles fitness value of Compare the fitness value, when the fitness value The smaller the The more optimal, the individual optimal value and the global optimal value are obtained. The individual optimal value refers to the historical optimal solution found by a single particle, and the global optimal value refers to the optimal solution currently found by the entire population:
其中abs函数表示取绝对值，为将粒子的位置作为室内温度值带入的PMV模型中的简化表示；The abs function represents taking the absolute value, the position of the particle Simplified representation in the PMV model brought in as an indoor temperature value;
3.3，按照公式(11)更新粒子速度，其中是第i个粒子第k次迭代的速度，w是惯性权重，c1、c2是学习因子，rand()是介于(0,1)之间的随机数，本实施例中w＝0.72，c1＝c2＝0.49，按照公式(12)更新粒子的位置；3.3, update the particle velocity according to formula (11), where is the velocity of the i-th particle at the k-th iteration, w is the inertia weight, c1 , c2 are learning factors, rand() is a random number between (0, 1), in this embodiment w=0.72 , c1 =c2 =0.49, update the position of the particle according to formula (12);
3.4，若迭代次数k达到设定的次数，输出全局最优值即室内最优温度若迭代次数k未达到设定的次数，则将迭代次数设定为k+1，并重新开始执行步骤3.2。3.4, if the number of iterations k reaches the set number of times, output the global optimal value the optimal indoor temperature If the number of iterations k does not reach the set number of times, set the number of iterations to k+1, and restart step 3.2.
步骤4，根据得到的室内最优温度对家用电器进行调节。In step 4, the household appliances are adjusted according to the obtained optimal indoor temperature.
通过步骤三求得了在最优热舒适值下的最优温度再将此温度值作为室内温度设定值，用电器将室温调节至此设定值。Through step 3, the optimal temperature under the optimal thermal comfort value is obtained Then use this temperature value as the indoor temperature set value, and use electrical appliances to adjust the room temperature to this set value.
本实施例通过现有PID算法计算用电器功率Qp(t)：This embodiment calculates the power Qp (t) of the electrical appliance through the existing PID algorithm:
其中，Kp、Ki、Kd分别为比例系数、积分时间常数和微分时间常数，其值为Kp＝2000，Ki＝0.5，Kd＝0。通过PID算法调节用电器功率Qp(t)，从而将室温调节至最优室内温度设定值此温度能够保证在人体热舒适度条件下，降低电网负载。in, Kp , Ki and Kd are proportional coefficient, integral time constant and differential time constant respectively, and their values are Kp =2000, Ki =0.5, Kd =0. Adjust the electrical power Qp (t) through the PID algorithm, so as to adjust the room temperature to the optimal indoor temperature set value This temperature can ensure that the grid load is reduced under the condition of human thermal comfort.
下面结合实验及实验结果附图对本发明的效果做进一步描述：Below in conjunction with experiment and experimental result accompanying drawing, the effect of the present invention is further described:
图4上方两幅子图表示的是在没有考虑均衡电网负载下居民使用电暖的温度设定值及室内PMV值的变化。可以看出，PMV的值一直维持在[-0.5,0.5]范围内，表示用户有最佳的舒适度。但是大量用户寻求舒适度最优，会加剧用电高峰时段电网的负荷压力。下方两幅子图显示的是考虑电网负载下的温度设定值和PMV值的变化。由红色实时电价曲线可以知道18:00～22:00(即64800s～79200s)为高电价、用电高峰时段，使用本发明的方法可以对电暖设备进行调节，通过降低电暖设备功率，在不过度影响用户舒适度的条件下降低电网负荷。从左下图可以看到，在用电高峰时段，PMV值在[-0.5,-0.8]之间，表示温度舒适度在舒适和微凉之间，是用户可以接受的。当参与需求响应、使用本发明方法的用户数量较多时，可以有效降低电网峰值负载，如图5所示，当3000户家庭使用本发明方法时，仅电暖一种电器就可以在用电峰值时段降低约500kw的负载。The top two sub-graphs in Fig. 4 show the changes of the temperature set value and indoor PMV value of the electric heater used by the residents without considering the balanced grid load. It can be seen that the value of PMV has been maintained in the range of [-0.5, 0.5], indicating that the user has the best comfort. However, a large number of users seek the best comfort level, which will increase the load pressure on the power grid during peak hours of electricity consumption. The bottom two subplots show the change in temperature setpoint and PMV value considering grid load. From the red real-time electricity price curve, it can be known that 18:00～22:00 (ie 64800s～79200s) is a high electricity price and peak electricity consumption period. The method of the present invention can be used to adjust the electric heating equipment. Reduce grid load without unduly affecting user comfort. As can be seen from the figure below on the left, during the peak period of electricity consumption, the PMV value is between [-0.5, -0.8], indicating that the temperature comfort is between comfortable and slightly cool, which is acceptable to users. When the number of users participating in demand response and using the method of the present invention is large, the peak load of the power grid can be effectively reduced. As shown in Figure 5, when 3000 households use the method of the present invention, only one electric heating appliance can be used at the peak power consumption. Period to reduce the load of about 500kw.
| Application Number | Priority Date | Filing Date | Title |
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| CN201910083622.8ACN109739093A (en) | 2019-01-29 | 2019-01-29 | A hybrid control method for residential electrical appliances based on PMV model |
| Application Number | Priority Date | Filing Date | Title |
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| CN201910083622.8ACN109739093A (en) | 2019-01-29 | 2019-01-29 | A hybrid control method for residential electrical appliances based on PMV model |
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| Application Number | Title | Priority Date | Filing Date |
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| CN201910083622.8APendingCN109739093A (en) | 2019-01-29 | 2019-01-29 | A hybrid control method for residential electrical appliances based on PMV model |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111522238A (en)* | 2020-04-26 | 2020-08-11 | 上海电力大学 | A comfort-based building integrated energy system control method and control system |
| CN113006868A (en)* | 2021-03-11 | 2021-06-22 | 北京科技大学 | Method for evaluating influence of deep well heat damage on human body working efficiency |
| CN119719805A (en)* | 2025-02-24 | 2025-03-28 | 山东林坔幕墙装饰有限公司 | An intelligent temperature monitoring and processing system for an openable skylight |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130060391A1 (en)* | 2011-09-01 | 2013-03-07 | Sharp Laboratories Of America, Inc. | Energy management system |
| CN104778507A (en)* | 2015-04-02 | 2015-07-15 | 南京邮电大学 | Intelligent building power utilization strategy acquiring method based on self-adaptive particle swarm algorithm |
| CN104896660A (en)* | 2015-05-20 | 2015-09-09 | 中南大学 | Method for optimized setting of air conditioner temperature in office building |
| US20160231725A1 (en)* | 2012-04-04 | 2016-08-11 | Ihi Inc. | Energy storage modeling and control |
| CN107917507A (en)* | 2017-11-14 | 2018-04-17 | 西安建筑科技大学 | A kind of central air conditioning hot comfort PMV control methods of blending image information |
| CN108182507A (en)* | 2017-12-05 | 2018-06-19 | 上海电力学院 | A kind of meter and probabilistic sale of electricity quotient the best electric price decision-making technique |
| CN108321796A (en)* | 2018-01-24 | 2018-07-24 | 上海交通大学 | Household energy management system and method |
| CN108334985A (en)* | 2018-02-05 | 2018-07-27 | 国网江西省电力有限公司电力科学研究院 | Resident's intelligent power Optimized model based on PSO Algorithm |
| CN108954741A (en)* | 2018-09-28 | 2018-12-07 | 珠海派诺科技股份有限公司 | hotel room air conditioner comfort control method |
| CN108964050A (en)* | 2018-08-26 | 2018-12-07 | 燕山大学 | Micro-capacitance sensor dual-layer optimization dispatching method based on Demand Side Response |
| CN109002931A (en)* | 2018-08-22 | 2018-12-14 | 河海大学 | Power generation dispatching and Peak-valley TOU power price combined optimization method a few days ago a few days ago |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20130060391A1 (en)* | 2011-09-01 | 2013-03-07 | Sharp Laboratories Of America, Inc. | Energy management system |
| US20160231725A1 (en)* | 2012-04-04 | 2016-08-11 | Ihi Inc. | Energy storage modeling and control |
| CN104778507A (en)* | 2015-04-02 | 2015-07-15 | 南京邮电大学 | Intelligent building power utilization strategy acquiring method based on self-adaptive particle swarm algorithm |
| CN104896660A (en)* | 2015-05-20 | 2015-09-09 | 中南大学 | Method for optimized setting of air conditioner temperature in office building |
| CN107917507A (en)* | 2017-11-14 | 2018-04-17 | 西安建筑科技大学 | A kind of central air conditioning hot comfort PMV control methods of blending image information |
| CN108182507A (en)* | 2017-12-05 | 2018-06-19 | 上海电力学院 | A kind of meter and probabilistic sale of electricity quotient the best electric price decision-making technique |
| CN108321796A (en)* | 2018-01-24 | 2018-07-24 | 上海交通大学 | Household energy management system and method |
| CN108334985A (en)* | 2018-02-05 | 2018-07-27 | 国网江西省电力有限公司电力科学研究院 | Resident's intelligent power Optimized model based on PSO Algorithm |
| CN109002931A (en)* | 2018-08-22 | 2018-12-14 | 河海大学 | Power generation dispatching and Peak-valley TOU power price combined optimization method a few days ago a few days ago |
| CN108964050A (en)* | 2018-08-26 | 2018-12-07 | 燕山大学 | Micro-capacitance sensor dual-layer optimization dispatching method based on Demand Side Response |
| CN108954741A (en)* | 2018-09-28 | 2018-12-07 | 珠海派诺科技股份有限公司 | hotel room air conditioner comfort control method |
| Title |
|---|
| JIE YANG等: "A Demand-Side Pricing Strategy Considering", 《2017 CHINESE AUTOMATION CONGRESS (CAC)》* |
| RUI TANG等: "Optimal and near-optimal indoor temperature and humidity controls for direct load control and proactive building demand response towards smart grids", 《AUTOMATION IN CONSTRUCTION》* |
| 刘庆: "基于PMV的室内环境智能系统设计", 《中国优秀硕士学位论文全文数据库》* |
| 姜爱华: "信息物理融合的智能空调响应不确定性尖峰折扣电价的动态优化模型及在线控制", 《中国电机工程学报》* |
| 王庆泽等: "分时电价环境下的办公建筑空调负荷双层柔性优化控制策略", 《电力系统保护与控制》* |
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111522238A (en)* | 2020-04-26 | 2020-08-11 | 上海电力大学 | A comfort-based building integrated energy system control method and control system |
| CN113006868A (en)* | 2021-03-11 | 2021-06-22 | 北京科技大学 | Method for evaluating influence of deep well heat damage on human body working efficiency |
| CN113006868B (en)* | 2021-03-11 | 2022-06-24 | 北京科技大学 | A method for evaluating the influence of deep well heat damage on human work efficiency |
| CN119719805A (en)* | 2025-02-24 | 2025-03-28 | 山东林坔幕墙装饰有限公司 | An intelligent temperature monitoring and processing system for an openable skylight |
| CN119719805B (en)* | 2025-02-24 | 2025-06-10 | 山东林坔幕墙装饰有限公司 | Intelligent temperature monitoring and processing system of openable daylighting skylight |
| Publication | Publication Date | Title |
|---|---|---|
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| CN204423200U (en) | A kind of humidity control system based on PMV |
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