CN113837665B - Regional electric heating load prediction method based on intelligent body modeling - Google Patents
Regional electric heating load prediction method based on intelligent body modeling Download PDFInfo
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- 238000005485 electric heating Methods 0.000 title claims abstract description 45
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- 238000010438 heat treatment Methods 0.000 claims abstract description 73
- 230000006399 behavior Effects 0.000 claims abstract description 28
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- 230000017525 heat dissipation Effects 0.000 claims description 3
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- 239000003795 chemical substances by application Substances 0.000 description 6
- 239000003245 coal Substances 0.000 description 4
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- 230000003993 interaction Effects 0.000 description 3
- 230000002354 daily effect Effects 0.000 description 2
- 238000004088 simulation Methods 0.000 description 2
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Abstract
The invention discloses a regional electric heating load prediction method based on intelligent body modeling, which belongs to the technical field of electric power systems. The method comprises the following steps: step 1: inputting the attribute of the target crowd; step 2: generating a time chain of travel behaviors of each person; step 3: obtaining a heating temperature decision of each person based on travel behaviors and optimal comfort level of the human body; step 4: constructing a home network; step 5: obtaining a temperature decision authority based on a family temperature decision priority model; step 6: correcting the heating temperature decision of the household; step 7: deciding the household heating temperature; step 8: performing residential heat load calculation; step 9: and simulating and measuring the heating electricity consumption conditions of each household to obtain the heating electricity consumption load of the regional layer. The invention can support the power grid upgrading and reconstruction suitable for clean heating reconstruction areas, and can predict the heating electricity load under the condition of lack of load data in the earlier stage of coal-to-electricity engineering.
Description
Technical Field
The invention relates to the technical field of power systems, in particular to a regional electric heating load prediction method based on intelligent body modeling.
Background
With the gradual advancement of coal-to-electricity engineering in China, the access scale of electric heating loads is continuously improved, the access area possibly faces the problem of power grid upgrading and transformation, and load prediction is an important basis for power grid upgrading and transformation planning. However, the regional electric heating load history data for performing coal-to-electric engineering transformation is insufficient, and the electric heating load has the characteristics of large volume, high synchronous rate, small utilization hours and the like, and a certain deviation can exist by adopting a traditional top-down load prediction method, so that the installation cost is increased, the equipment utilization hours are reduced during the power grid upgrading transformation, and the sustainable development of the power grid is not facilitated. The modeling of the intelligent body is a modeling method in complexity science, the intelligent body modeling method can be introduced into electric heating load prediction, daily behavior simulation is carried out on the intelligent body by mapping a person into the intelligent body in a model, electric heating load prediction is carried out from bottom to top, the modeling of the intelligent body gets rid of dependence on historical load data, and the influence of various objective factors and subjective factors is considered, wherein the subjective factors can also simulate incomplete rational decisions of the person, and support is provided for power grid upgrading and reconstruction planning caused by electric heating load in a coal power conversion area.
The intelligent load prediction method is represented by an artificial neural network (ARTIFICIAL NEURAL NETWORK), fuzzy logic (fuzzy logic), a support vector machine (Support Vector Mechanism) and the like, the artificial neural network needs to be trained based on historical data, a large amount of historical data is needed in the process, a large amount of electric heating load historical data is lacking in a newly-reformed coal power-change area, and the existing intelligent method is difficult to provide powerful load prediction support for upgrading and reforming a coal power-change area power grid in a power planning stage.
The invention provides a regional electric heating load prediction method based on intelligent body modeling, which is used for predicting electric heating load of a newly-transformed coal-to-electricity region with insufficient historical heating load data.
Disclosure of Invention
The invention aims to provide a regional electric heating load prediction method based on intelligent agent modeling, which is characterized in that the method adopts intelligent agent modeling to simulate daily behaviors of people, takes the person as a minimum decision unit from the viewpoint of families, adopts a hierarchical structure to predict electric heating load from bottom to top, and comprises the following steps:
step 1: inputting attributes of target crowd, including age group, income level, clothing habit, family structure and thermal comfort degree parameters;
step 2: generating a time chain of travel behaviors of each person based on personal attributes of the target group;
Step 3: obtaining a heating temperature decision of each person based on travel behaviors and optimal comfort level of the human body;
Step 4: constructing a home network;
Step 5: obtaining a temperature decision authority based on a family temperature decision priority model;
Step 6: correcting the heating temperature decision of the household in consideration of the income level and the consumption habit;
step 7: deciding a home heating temperature based on a home heating temperature decision model;
Step 8: carrying out house heat load calculation by combining meteorological data and heating temperature decision;
step 9: and simulating and metering the heating electricity consumption conditions of each household by combining the operation strategy of the electric heating equipment to obtain the heating electricity consumption load of the regional layer.
The hierarchy includes a personal level, a family level, and an area level.
The heating temperature decision model of the personal layer is as follows:
The two-state variable L tra,t of the formula (1) is adopted to represent the travel behavior of the individual,
Wherein L tra,t (i) represents a home-based travel state of the individual i at time t;
The thermal comfort of the human body of the individual is calculated by adopting a thermal comfort model (2),
IPMV＝aTa+bPv-c (2)
Wherein I PMV is a predicted average number of votes PMV index, the interval of the human body comfort temperature corresponding to PMV is [ -0.5,0.5], wherein the optimal human body comfort level corresponding to I PMV＝0;Ta is indoor temperature, P v is relative humidity, and a, b and c are known parameters;
The heating temperature decision of the family member based on the travel behavior and the optimal comfort of the human body is shown as formula (3):
PMVi＝ξiIP (4)
Wherein T tem,t (i) represents a heating temperature decision of the individual i at time T; t i is the travel duration of the individual i; PMV i represents the predicted average vote value for individual i; i P is a threshold value of PMV corresponding to the human comfort temperature boundary; and xi i is a random number with a value interval of [0,1 ].
The heating temperature decision model of the family layer is as follows:
For a certain family n in a residential area, establishing a family temperature decision priority model in the formula (5), wherein a family member i corresponding to the optimal solution is a temperature decision right attribution;
Wherein r clo,i is the clothing thermal resistance of the member i, and the value range of the clothing thermal resistance in winter is [1.01,1.65]; e i is the ingress of the node where the member i is located; d ij is a constitution factor of a member i belonging to the j-th group of people, and the value range is [0,1]; z i is a family temperature decision weight comprehensive index of the member i; a j is a set of each group of people, and k is the total number of members of family n;
correcting the temperature decision of the member having the family temperature decision right according to the formula (6):
Wherein T tem,n,i represents a heating temperature decision of a member i having a temperature decision right in a family n, T r is a human comfort temperature threshold, and u is an energy consumption behavior coefficient; wherein, the energy behavior coefficient u is used for satisfying:
wherein p is an economic consumer trend scale factor and s i is the income level of the individual i;
The home heating temperature decision taking into account the home income and consumption habits is as shown in equation (8):
wherein T set,n,t (i) represents a heating temperature decision of a member i in the family n at the time T;
and a household heating temperature decision model taking the household member temperature decision and trip behavior into consideration is shown as a formula (9).
Wherein T house,n,t (i) represents a heating temperature decision of the family n dominated by the member i at the time T, and T is the family idle time.
The method for calculating the residential heat load q heat (tau) in the step 8 is as follows:
For a residential space consisting of wall enclosures, the residential heat load q heat (τ) is calculated according to the dynamic heat transfer equation,
Wherein T house is the temperature to be maintained obtained by the family temperature decision, and C a is the air heat capacity in the family residence space; q heat (τ) is the residential heat load at time τ; f j is the inner surface area of the inner wall j of the building; h in is the heat transfer coefficient of the inner surface of the wall body and the air surface; t j (tau) is the surface temperature of the wall j at tau moment; t a (τ) is the indoor temperature at τ; c p is the specific heat capacity of the wall material; ρ is the air density of the residential home space; g out is the ventilation of the residential space of the family and the external environment; g adj is the ventilation of the indoor and adjacent chambers; c cov is the internal disturbance cold load coefficient; n cov is the heat dissipation capacity per unit area; s is the area of a family residence room; c win,w is the cold load coefficient of the w-th window; s win,w is the surface area of the w-th window; d w is the solar heat gain factor of the w-th window; z win,w is the shading coefficient of the w-th window.
The step 9 specifically comprises the following steps:
When all family members go out, namely the residence is empty for longer than 4 hours, the equipment operates in a low energy consumption state of maintaining the room temperature at 18 ℃ and recovers the residence heating set temperature value 2 hours before the expected home returning time of the family members; when a member is at home, the electric heating equipment continuously operates, and the output model of the electric heating equipment is as follows:
wherein P h (tau) is the output power of the electric heating equipment; q heat,n (τ) is the actual heating heat load demand of home n at τ; η is the thermoelectric conversion efficiency of the electric heating apparatus;
The heating electricity load model of the home n in the t period is:
Wherein Q heat,n (t) is the heating electricity load of the family n in the period t;
The district heating electric load prediction model is:
Wherein Q heat (t) is the heating power load of the residential area, and H is the number of families of the residential area.
The invention has the beneficial effects that:
The regional electric heating load prediction method based on the intelligent agent modeling is a method for predicting before engineering, and other load prediction methods at present are methods for predicting after engineering; according to the invention, through individual space-time behavior simulation and family temperature decision, and by combining with weather temperature and building performance, thermodynamic calculation is carried out to obtain the heat load of the house, the system can support the power grid upgrading and transformation suitable for clean heating transformation areas, and the heating electricity load can be predicted under the condition of lack of load data in the earlier stage of coal electricity transformation engineering.
Drawings
FIG. 1 is a flow chart of a regional electric heating load prediction method based on agent modeling of the present invention.
Detailed Description
The invention provides a regional electric heating load prediction method based on intelligent body modeling, and the method is further described below with reference to the accompanying drawings and specific embodiments.
FIG. 1 is a flow chart of a regional electric heating load prediction method based on agent modeling of the present invention. In the personal aspect, the regional electric heating load prediction method based on the intelligent body modeling provided by the invention considers individual factors influencing the behavior of the electric heating equipment to be used as the main traveling behavior and the optimal comfortable temperature, and establishes a family member traveling rule and a heating temperature decision based on the individual factors. The invention divides the social roles of family members into four categories, namely office workers, school workers, retirement people and children.
The group of people in a residential area is denoted by a, the group of people of each category is denoted by a j,
The travel rules of family members are:
1) Office workers: the working day is the early peak trip, the late peak returns, the rest day trip/return is random, the early peak time period is about 7:00-9:00, the late peak time period is about 17:00-19:00, and the working day of the office worker is monday to Saturday in consideration of the reasons that a plurality of units currently carry out a single system and the like;
2) The literature group: the method comprises the steps of going from Monday to friday on the early peak, returning the late peak/sub-late peak, going over the weekend on the late peak/returning randomly, taking into account the fact that part of schools have late self-learning and the like, wherein the school groups have sub-late peaks, the early peak time period is about 6:30-7:30, the late peak time period is about 17:30-18:30, and the sub-late peak time period is about 20:45-21:30;
3) Retired population: the travel of the early peak and the return of the late peak are carried out every day, the time period of the early peak is about 7:30-11:30, and the time period of the late peak is about 13:15-17:35.
4) Children: travel/return to random with the parents accompanying.
For the state of whether each family member is at home or not, the invention adopts a two-state variable L tra,t to represent the travel situation of the individual based on home, as shown in a formula (1).
Where L tra,t (i) represents the home-based travel state of the individual i at time t.
The invention calculates the human body thermal comfort level by adopting a simplified ISO7730 thermal comfort level model, as shown in a formula (2).
IPMV＝aTa+bPv-c (2)
Wherein I PMV is a predicted average number of votes PMV index, the interval of the human body comfort temperature corresponding to PMV is [ -0.5,0.5] according to the standard of ISO7730, wherein the optimal human body comfort level corresponding to I PMV＝0;Ta is indoor temperature, P v is relative humidity, and a, b and c are known parameters.
The heating temperature decision of the family member based on the travel behavior and the optimal comfort of the human body is shown as formula (3):
PMVi＝ξiIP (4)
Wherein T tem,t (i) represents a heating temperature decision of the individual i at time T; t i is the travel duration of the individual i; PMV i represents the predicted average vote value for individual i; i P is a threshold value of PMV corresponding to the human comfort temperature boundary; and xi i is a random number with a value interval of [0,1 ].
In a family level, the regional electric heating load prediction method based on the intelligent agent modeling provided by the invention considers family factors influencing the behavior of the electric heating equipment to be mainly family structures, family internal interaction rules, family income and family consumption habits, and performs family temperature decision weight distribution and family heating electricity consumption temperature decision based on the family factors.
The household temperature decision right distribution of the invention firstly needs to construct a household network, each individual in the household is regarded as a single node by the household network, the connection generated between the individuals is regarded as the edge between the nodes, and the relation set of the household temperature decision right distribution characterizes the relation among all the individuals in the household. In a home network, there is a relationship between all the member nodes that make up the home network, i.e., internal interactions of the family members. The invention considers the home network as a directed graph, the internal interaction of the temperature decision trend of the family members is a directed edge, and for a certain family n in the residential area, the invention establishes a family temperature decision priority model as shown in a formula (5), and the family member i corresponding to the optimal solution is a temperature decision right attribution.
Wherein r clo,i is the clothing thermal resistance of the member i, and the value range of the clothing thermal resistance in winter is [1.01,1.65]; e i is the ingress of the node where the member i is located; d ij is a constitution factor of a member i belonging to the j-th group of people, and the value range is [0,1]; z i is a family temperature decision weight comprehensive index of the member i; k is the total number of members of family n.
The household heating electricity consumption temperature decision is to consider the influence of household income and consumption habit on household member energy consumption behavior on the basis of temperature decision weight distribution, and further correct the temperature decision of the member with the household temperature decision weight, as shown in formula (6):
Wherein T tem,n,i represents a heating temperature decision of a member i having a temperature decision right in a family n, T r is a human comfort temperature threshold, and u is an energy consumption behavior coefficient.
Wherein, the energy behavior coefficient u is used for satisfying:
Where p is an economic consumer propensity scaling factor and s i is the revenue level of individual i.
The family member heating temperature decision taking into account the household income and consumption habits is shown in equation (8).
In the formula, T set,n,t (i) represents a heating temperature decision of the member i in the home n at time T.
When the temperature decision of the household heating and electricity consumption is made, taking the heterogeneity of the temperature decision of the household members and the travel behavior into consideration, on one hand, making a transverse decision among the household members to obtain the temperature decision of the household members with temperature decision rights; and on the other hand, making a longitudinal decision on a family member travel time chain to obtain a family vacant period, and combining the two decisions to obtain a family heating electricity consumption temperature decision model, wherein the decision model is shown in a formula (9).
Wherein T house,n,t (i) represents a heating temperature decision of the family n dominated by the member i at the time T, and T is the family idle time.
In the regional aspect, the regional electric heating load prediction method based on the intelligent body modeling provided by the invention is used for predicting the regional electric heating load by considering factors such as weather temperature, residence warmth retention performance, electric heating equipment operation strategy and the like.
The heat load calculation steps of the family residence are as follows: firstly, obtaining the temperature to be maintained by a household based on a household heating electricity consumption temperature decision model in the formula (9); next, parameters such as air temperature and residence property are substituted into the formula (10); and finally, solving a differential equation to obtain the electric heating heat load required by the residence temperature at the decision temperature.
For a family residence space formed by wall body and other enclosing structures, the dynamic heat transfer equation is as follows:
Wherein T house is the temperature to be maintained obtained by the family temperature decision, and C a is the air heat capacity in the family residence space; q heat (τ) is the heat supply of the electric heating device to the domestic dwelling space at τ; f j is the inner surface area of the inner wall j of the building; h in is the heat transfer coefficient of the inner surface of the wall body and the air surface; t j (tau) is the surface temperature of the wall j at tau moment; t a (τ) is the indoor temperature at τ; c p is the specific heat capacity of the wall material; ρ is the air density of the residential home space; g out is the ventilation of the residential space of the family and the external environment; g adj is the ventilation of the indoor and adjacent chambers; c cov is the internal disturbance cold load coefficient; n cov is the heat dissipation capacity per unit area; s is the area of a family residence room; c win,w is the cold load coefficient of the w-th window; s win,w is the surface area of the w-th window; d w is the solar heat gain factor of the w-th window; z win,w is the shading coefficient of the w-th window.
The operation strategy of the electric heating equipment is as follows: when all family members go out, namely the residence is empty for longer than 4 hours, the equipment operates in a low energy consumption state of maintaining the room temperature at 18 ℃ and recovers the residence heating set temperature value 2 hours before the expected home returning time of the family members; when a member is at home, the electric heating equipment continuously operates, and the output model of the electric heating equipment is as follows:
wherein P h (tau) is the output power of the electric heating equipment; q heat,n (τ) is the actual heating heat load demand of home n at τ; η is the thermoelectric conversion efficiency of the electric heating apparatus.
The heating electricity load model of the home n in the t period is:
wherein Q heat,n (t) is the heating electricity load of the family n in the period t.
The district heating electric load prediction model is:
Wherein Q heat (t) is a residential heating load, H is the number of households in the residential area, Q heat,n (t) is a heating load for household n, and the result is obtained by the formula (12).
The present invention is not limited to the preferred embodiments, and any changes or substitutions that would be apparent to one skilled in the art within the scope of the present invention are intended to be included in the scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.
Claims (1)
1. The regional electric heating load prediction method based on the intelligent agent modeling is characterized in that the method adopts the intelligent agent modeling to simulate the daily behaviors of people, and adopts a hierarchical structure to predict the electric heating load from bottom to top by taking the person as a minimum decision unit from the viewpoint of families, and comprises the following steps:
step 1: inputting attributes of target crowd, including age group, income level, clothing habit, family structure and thermal comfort degree parameters;
step 2: generating a time chain of travel behaviors of each person based on personal attributes of the target group;
Step 3: obtaining a heating temperature decision of each person based on travel behaviors and optimal comfort level of the human body;
Step 4: constructing a home network;
Step 5: obtaining a temperature decision authority based on a family temperature decision priority model;
Step 6: correcting the heating temperature decision of the household in consideration of the income level and the consumption habit;
step 7: deciding a home heating temperature based on a home heating temperature decision model;
Step 8: carrying out house heat load calculation by combining meteorological data and heating temperature decision;
The method for calculating the residential heat load q heat (tau) in the step 8 is as follows:
For a residential space consisting of wall enclosures, the residential heat load q heat (τ) is calculated according to the dynamic heat transfer equation,
Wherein T house is the temperature to be maintained obtained by the family temperature decision, and C a is the air heat capacity in the family residence space; q heat (τ) is the residential heat load at time τ; f j is the inner surface area of the inner wall j of the building; h in is the heat transfer coefficient of the inner surface of the wall body and the air surface; t j (tau) is the surface temperature of the wall j at tau moment; t a (τ) is the indoor temperature at τ; c p is the specific heat capacity of the wall material; ρ is the air density of the residential home space; g out is the ventilation of the residential space of the family and the external environment; g adj is the ventilation of the indoor and adjacent chambers; c cov is the internal disturbance cold load coefficient; n cov is the heat dissipation capacity per unit area; s is the area of a family residence room; c win,w is the cold load coefficient of the w-th window; s win,w is the surface area of the w-th window; d w is the solar heat gain factor of the w-th window; z win,w is the sunshade coefficient of the w window;
Step 9: simulating and metering the heating electricity consumption conditions of each household by combining the operation strategy of the electric heating equipment to obtain heating electricity consumption load of the regional layer;
The step 9 specifically comprises the following steps:
When all family members go out, namely the residence is empty for longer than 4 hours, the equipment operates in a low energy consumption state of maintaining the room temperature at 18 ℃ and recovers the residence heating set temperature value 2 hours before the expected home returning time of the family members; when a member is at home, the electric heating equipment continuously operates, and the output model of the electric heating equipment is as follows:
wherein P h (tau) is the output power of the electric heating equipment; q heat,n (τ) is the actual heating heat load demand of home n at τ; η is the thermoelectric conversion efficiency of the electric heating apparatus;
The heating electricity load model of the home n in the t period is:
Wherein Q heat,n (t) is the heating electricity load of the family n in the period t;
The district heating electric load prediction model is:
Wherein Q heat (t) is the heating power load of the residential area, and H is the number of families of the residential area;
the hierarchy includes a personal level, a family level, and an area level;
The heating temperature decision model of the personal layer is as follows:
The two-state variable L tra,t of the formula (1) is adopted to represent the travel behavior of the individual,
Wherein L tra,t (i) represents a home-based travel state of the individual i at time t;
The thermal comfort of the human body of the individual is calculated by adopting a thermal comfort model (2),
IPMV＝aTa+bPv-c (2)
Wherein I PMV is a predicted average number of votes PMV index, the interval of the human body comfort temperature corresponding to PMV is [ -0.5,0.5], wherein the optimal human body comfort level corresponding to I PMV＝0;Ta is indoor temperature, P v is relative humidity, and a, b and c are known parameters;
The heating temperature decision of the family member based on the travel behavior and the optimal comfort of the human body is shown as formula (3):
PMVi＝ξiIP (4)
Wherein T tem,t (i) represents a heating temperature decision of the individual i at time T; t i is the travel duration of the individual i; PMV i represents the predicted average vote value for individual i; i P is a threshold value of PMV corresponding to the human comfort temperature boundary; xi i is a random number with a value interval of [0,1 ];
the heating temperature decision model of the family layer is as follows:
For a certain family n in a residential area, establishing a family temperature decision priority model in the formula (5), wherein a family member i corresponding to the optimal solution is a temperature decision right attribution;
Wherein r clo,i is the clothing thermal resistance of the member i, and the value range of the clothing thermal resistance in winter is [1.01,1.65]; e i is the ingress of the node where the member i is located; d ij is a constitution factor of a member i belonging to the j-th group of people, and the value range is [0,1]; z i is a family temperature decision weight comprehensive index of the member i; a j is a set of each group of people, and k is the total number of members of family n;
correcting the temperature decision of the member having the family temperature decision right according to the formula (6):
Wherein T tem,n,i represents a heating temperature decision of a member i having a temperature decision right in a family n, T r is a human comfort temperature threshold, and u is an energy consumption behavior coefficient; wherein, the energy behavior coefficient u is used for satisfying:
wherein p is an economic consumer trend scale factor and s i is the income level of the individual i;
The home heating temperature decision taking into account the home income and consumption habits is as shown in equation (8):
wherein T set,n,t (i) represents a heating temperature decision of a member i in the family n at the time T;
the household heating temperature decision model taking the household member temperature decision and trip behavior into consideration is shown as a formula (9):
Wherein T house,n,t (i) represents a heating temperature decision of the family n dominated by the member i at the time T, and T is the family idle time.
Priority Applications (1)
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