CN103617565A - Method for establishing relationship between city micro weather and electric power air conditioner loads - Google Patents
Method for establishing relationship between city micro weather and electric power air conditioner loads Download PDFInfo
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Abstract
The invention relates to a method for predicting weather sensitive loads relevant to temperature in an electric power system, in particular to a method for establishing a relationship between city micro weather and electric power air conditioner loads. The method includes the first step of carrying out statistics on daily electricity consumption, the second step of calculating basic normal loads from Monday to Sunday by using a week as a cycle with regard to the months when air conditioners are not used, the third step of calculating the electric power air conditioner loads by subtracting the basic normal loads with regard to the months when the air conditioners are used, the fourth step of recording a daily average temperature numerical value, the fifth step of correcting the temperature according to a heat island effect, the sixth step of correcting the temperature according to a temperature and humidity effect, the seventh step of correcting the temperature according to an accumulative effect, the eighth step of establishing an electric power air conditioner load relational expression, and the ninth step of solving the relational expression by using the least square method. According to the method for establishing the relationship between the city micro weather and the electric power air conditioner loads, the load trend within a short time can be predicted according to the basic normal loads, the electric power air conditioner loads and the temperatures corrected through the three types of effects, and the method can provide a basis for dispatching of the electric power system and prevent power rationing caused by insufficient prediction.
Description
Technical field
The present invention relates to the Forecasting Methodology of weather sensitive load in a kind of electric system, in particular, relate in particular to the method for building up of a kind of city microclimate and electric air conditioner load relation.
Background technology
The environment that affects mankind's electricity consumption mainly refer to occur in microclimate all affect physical phenomenon and the physical process of electric load, as temperature, relative humidity, wind speed, sleet, hail, arid etc., wherein in summer, the impact of temperature, relative humidity and the tropical island effect, cumulative effect and the warm and humid effect that produce is on its basis the most outstanding.
In hot summer, larger weather responsive type load proportion affected by environment is higher, peak load period, the ratio of the air conditioner load of most cities reaches 30%-50%, some city even surpasses 50%, therefore grasp the Changing Pattern of weather sensitive load, just can predict load trend in a short time.
Electric load is at every moment not identical, but can be summarized as by four components and form according to the rule of its variation: normal load, weather sensitive load, special event load and random component load.Normal load is mainly industrial load, and industrial load increases along with socioeconomic development, so normal load stable maintenance within short-term, in certain level, is the trend of rising appreciably for a long time.Weather sensitive load is to be affected by climate change and the load that produces, in summer, is mainly electric air conditioner load, and this part electric load is that we will predict, is load for heating in the winter time.Special event load is affected by occasion and the load that forms, the load causing as the celebration activity of National Day etc.The influence factor of random load is how comparatively subjective, as certain region people's general consumption habit etc., belongs to unpredictable load part.Special event load and random load usually significantly change with hour Huo Tianwei unit.
Because normal is substantially constant in the load short time, and the proportion that weather sensitive load accounts in electric load is much larger than the summation of special event load and random load, grasped the Changing Pattern of weather sensitive load, just can predict load trend in a short time, for electric power system dispatching provides foundation.In presents, alleged load and the relation between temperature, all refer to the relation between weather sensitive load and temperature, refers to electric air conditioner load in summer.
Summary of the invention
The present invention, in order to overcome the shortcoming of above-mentioned technical matters, provides the method for building up of a kind of city microclimate and electric air conditioner load relation.
The method for building up of city of the present invention microclimate and electric air conditioner load relation, its special feature is, comprises the following steps: a). statistics daily power consumption, for its annual daily power consumption of certain Urban Statistical to be studied; B). calculate normal load, according to step a) described in the weather conditions in city, pick out some or certain several month of not using air-conditioning, take and week go out the average normal load of every day on Monday to Sun. as computation of Period; C). calculate electric air conditioner load in summer, according to step a) described in the weather conditions in city, pick out some or certain use the month of air-conditioning several summers; The daily power consumption of every day is deducted to the normal load of corresponding day, calculate electric air conditioner load in summer; If obtain summer electric air conditioner load be respectively
,
,
,
; D). the medial temperature of adding up every day, for the month of the use air-conditioning of picking out, record the medial temperature numerical value of every day, establish it and be respectively
,
,
,
; E). set up electric air conditioner load relational expression, the daily power consumption of electric air conditioner load and the relation of temperature represent with following functional relation:
Wherein,
electric air conditioner load daily power consumption,
for mean daily temperature,
be
the maximal value that can get, the i.e. upper limit of electric air conditioner load;
be
time
value, i.e. the lower limit of electric air conditioner load;
it is the medium temperature of power consumption temperature section of slope maximum while rising with temperature;
it is the growth power law of temperature; F). adopt least square method to solve, according to step c) in the load of the electric air conditioner asked for
,
,
,
and steps d) medial temperature of every day in
,
,
,
, utilize least square method to ask each parameter value in definite formula (1-1); G). the prediction of electric air conditioner load, according to the daily power consumption of the electric air conditioner load of asking for and the relation of temperature
, utilize the medial temperature of following one day or a few days prediction, i.e. the trend of measurable load in a short time, for the scheduling of electric system provides the reference frame of science.
Step is a) the annual daily power consumption of statistics, to calculate normal load and electric air conditioner load.For city electricity consumption, generally take week as the cycle presents electricity consumption regularity, step b) in, after the daily power consumption on inapplicable air-conditioning all Mondaies in month is added, average and can obtain the normal load on Monday, the normal load on Monday to Sun. is adopted to use the same method and is calculated.Step c), in, for the month of using air-conditioning, by the daily power consumption of every day being deducted to the power consumption on corresponding date in week, can obtain the electric air conditioner load of this day.Steps d) for recording air-conditioning, use the medial temperature of day, to set up the relation of electric air conditioner load and temperature on average.Step e) provided the functional relation of electric air conditioner load with medial temperature, step f) for adopting least square method to carry out curve fitting according to the Temperature numerical gathering and electric air conditioner load, to determine each parameter value in functional relation.
The method for building up of city of the present invention microclimate and electric air conditioner load relation, step f) in, utilize least square method to ask the method for true parameter value to comprise the following steps:
F-1). formula (1-1) belongs to the variation of following formula:
（1-2）
F-2). formula (1-2) is carried out to linearization process, formula (1-2) is changed to rear both sides and take the logarithm, carry out variable conversion, obtain following relational expression:
After further changing, obtain
(1-4)
F-3). formula (1-4) is transformed to linear equation, order
,
,
, formula (1-4) is changed to linear equation:
Wherein,
for constant,
with
be about
with
nonlinear function;
F-4). utilize least square method to solve formula (1-5), finally can obtain each parameter value in formula (1-1).
Step f-1) to f-4) provided the concrete grammar of each parameter value in the least square method derivation of equation (1-1).
The invention has the beneficial effects as follows: the method for building up of city of the present invention microclimate and electric air conditioner load relation, first according to the month of not using air-conditioning, take and week go out the normal load on Monday to Sun. as computation of Period; Then for the month of using air-conditioning, according to daily power consumption, deduct the method for corresponding day normal load, obtain the load of electric air conditioner in summer; And obtain the correction temperature after tropical island effect, warm and humid effect and cumulative effect impact; Finally, according to electric air conditioner load and revised temperature data, adopt least square method to ask for each parameter value in functional relation, obtain the relational expression of electric air conditioner load and medial temperature.Like this, in summer, comparatively the following temperature of Accurate Prediction in the situation that, the measurable trend of load in a short time, for electric power system dispatching provides foundation, has avoided the generation due to the not enough power cuts to limit consumption situation causing of prediction.
Accompanying drawing explanation
Fig. 1 is the daily power consumption curve of Beijing whole year in 2005;
Fig. 2 is the graph of a relation of existing typical load and weather;
Fig. 3 is standard logic curve map;
Fig. 4 is the matched curve schematic diagram of temperature and electric air conditioner load in the present invention;
Fig. 5 is the matched curve of the lowest temperature and electric air conditioner load in embodiment;
Fig. 6 is the matched curve of the highest temperature and electric air conditioner load in embodiment;
Fig. 7 is the matched curve of temperature on average and electric air conditioner load in embodiment;
Fig. 8 is the function relation figure of temperature and weight λ;
Fig. 9 is comfort index and the matched curve of idle call electric weight;
Figure 10 is the rear temperature of accumulation correction and the matched curve of idle call electric weight;
Figure 11 is that temperature and the electric air conditioner power consumption of city microclimate impact is related to matched curve.
Embodiment
Below in conjunction with accompanying drawing and embodiment, the invention will be further described.
As shown in Figure 1, provided the daily power consumption curve of Beijing whole year in 2005, can find, the daily power consumption in April is annual minimum, this is because of Pekinese's temperature optimum human body in April, does not need to make electrical appliance to change indoor temperature, the basic not load relevant to weather.Since May, air conditioner load increases along with the rising of temperature, and daily power consumption is soaring month by month, reaches peak in August in July; Along with the decline of temperature, air conditioner load reduces subsequently, and power consumption also progressively declines.The weather in October substantially can not produce cooling or heating load yet, but October compared to April, average daily power consumption promotes to some extent, this is because through half a year, the economy of Beijing has development to a certain degree, this has been also embodied in the growth of normal load.Before April, after October, daily power consumption becomes large because of the existence of heating load.In addition, on May 1 to 7, on October 1 to 7, normal production work suspended, so daily power consumption reaches low ebb owing to being country's legal festivals and holidays.
Therefore, we think that the daily power consumption in April and October (removing on October 1 to 7) is the energy consumption that normal load produces, and have hardly weather sensitive load.During this period of time, do not have special event to occur, do not consider special event load.The shared ratio of random load is little and cannot quantize, and also ignores.The load in May (removing on May 1 to 7) to September has comprised normal load, weather sensitive load, special event load and random component load simultaneously, the daily power consumption in this period is deducted to the energy consumption that normal load produces, ignore special event load and random component load, think that remaining power consumption is approximately equal to electric air conditioner energy consumption.
As can be seen from Figure 1, the fluctuation of daily power consumption be take week as unit, and the daily power consumption on Saturday and Sun. is starkly lower than working day, even and working day, the daily power consumption on Monday to Friday is also to have certain rule governed.Therefore, take and be the energy consumption that computation of Period normal load produces week, and take week as unit by summer daily power consumption deduct the energy consumption that normal load produces.Daily power consumption to April and (removing on October 1 to 7) whole Mondaies in October is averaged, and obtains the energy consumption that Monday, corresponding normal load produced.To April and October this bimestrial daily power consumption average, the growth of the base normal load can partial offset producing due to economic development.The algorithm on Tu. to Sun. is similar.Through calculating, obtain the as shown in table 1 electric energy that normal load that week is unit produces of take:
Table 1
By May (remove on May 1 to 7) to table one in the daily power consumption correspondence in September in week every day be worth accordingly, deduct normal load energy consumption, just obtained the approximate electric air conditioner power consumption of Beijing in May, 2005 (removing on May 1 to 7) to every day in September.
J. Y. Fan and J. D. McDonald have studied the relation between weather sensitive load and weather, and they have provided the relation of typical load and weather in article, as shown in Figure 2.Numerous researchs show, in meteorological factors, temperature is the most remarkable and the most regular on the impact of weather responsive type electric load.In the time of the cold or heating of drastic change out of breath on the same day, will there be a large amount of heatings or temperature-lowering load to put into operation.And continue when too high or too low, to compare with the phase same date in former time when medial temperature, daily load will have a greater change.
As can be seen from the figure, between temperature and electric load, be a nonlinear relation, the similar tank of curve shape.When temperature is during lower than 14 ~ 18 ℃, along with the reduction of temperature, electric load rises, and this is mainly caused by heating load.When temperature is during higher than 14 ~ 18 ℃, along with the rising of temperature, electric load rises, and mainly by cooling load, is caused.Temperature is in the time of 14 ~ 18 ℃, and electric load is minimum, because the temperature in this stage for human body the most comfortable, neither needs heating also not need refrigeration.It should be noted that when temperature is during lower than-10 ℃, even if temperature continues to reduce, electric load can not raise yet again, because heating load exists the upper limit, when-10 ℃ of left and right, nearly all heating system has all been located in use, there will be no the rising space in short-term.When same situation is also applicable to temperature higher than 35 ℃, now air conditioner load has substantially reached peak, even if temperature continues rising air conditioner load, is also difficult to increase.
Our research be high temperature season time city microclimate and electric air conditioner load reciprocal effect rule, so the emphasis of our concern in temperature the curve during higher than 14 ~ 18 ℃.Can find, this part curve is S-type.When temperature is during at 16 ~ 24 ℃, the slope that load rises with temperature is less, even if now temperature rises, uses the people of air-conditioning temperature-reducing also few.When temperature is during at 24 ~ 35 ℃, the slope that load rises with temperature is larger, and 1 ℃ of the now every rising of temperature, all can increase a considerable amount of air-conditionings and come into operation.When temperature is greater than 35 ℃, the slope that load rises with temperature returns to again smaller, and now air conditioner load approaches saturatedly, even if temperature continues rising, the air conditioner load amount of increase is all limited.
In engineering problem, according to the data of series of discrete, we often wish to obtain a continuous function or more discrete equation and the given data of crypto set match, this process is called matching.If this function undetermined is linear, is linear fit, otherwise is nonlinear fitting.In real work, linear less between variable, conventionally select suitable curve type to carry out matching observation data, and analyze the relation between two variablees with the curvilinear equation simulating.
One of method of conventional curve is linear regression, and its general calculation procedure is as follows:
(a) draw scatter diagram, in conjunction with distribution and the data characteristic of discrete point, select suitable curve type;
(b) carry out change of variable,,
, make two variablees after conversion
with
in linear relation
;
(c) by principle of least square method, ask linear equation
, between the data of trying to achieve and real data, the quadratic sum of error is the coefficient to be asked of hour definite equation, and carries out variance analysis;
(d) by the inverse transformation of variable
,
, by linearize equation
be converted to the function expression of former variable Y and X
.
Whether the fit equation obtaining after curve is desirable, and same batch data adopts different curvilinear equations to carry out matching, and the better effects if of any equation can be weighed with the degree of fitting of curvilinear equation.The degree of fitting of curvilinear equation refers to coefficient R 2, and its expression way is suc as formula shown in (1-6):
Wherein, Q is regression sum of square, i.e. the quadratic sum of the difference of measured value and predicted value; SSY is total sum of squares.R2 is in the total sum of squares of response variable Y, fitting function can explain in other words the shared ratio of foreseeable part, value between 0 ~ 1, when and just think all sample points all on regression straight line duration be just taken as 1.The value of R2 is larger, and regression straight line matching must be better, otherwise regression straight line matching obtains bad.Therefore, can represent the quality that fitting function and discrete data coordinate with R2.
When the relation between weather sensitive load and temperature is carried out matching, the maximum of use are higher order polynomial matchings.The exponent number of fitting function is higher, and related coefficient is higher.But in actual project fitting, researcher is often more prone to find out general rule, get rid of the interference of special data, rather than simply by improving polynomial exponent number, distortion curve is to cater to the distribution of data point.For high temperature high temperature season, the relation between this section of temperature and weather sensitive load is S curve, as shown in Figure 3.Therefore select the relation between logistic curve matching high-temperature load and temperature.Power consumption is directly proportional to average load, therefore can use equally the relation between this curve temperature and electric air conditioner power consumption.
Logistic curve is typical S type curve, is for describing the relation of the rate of population increase and the density of population at first, is widely applied to afterwards engineering science, sociological numerous areas.The feature of logistic curve is that while starting, functional value slowly increases with the growth of function argument, and the speed increasing in a certain scope afterwards improves rapidly, reaches after certain limit, and the speed of growth slows down again, fixed value of final approach.The definition of a simple logistic curve is suc as formula shown in (1-7):
（1-7）
Its variation has following several:
In the present invention, be used for the function of matching high temperature season electric air conditioner load power consumption and temperature relation suc as formula shown in (1-1), belong to the represented variation of formula (1-2):
In formula, y is electric air conditioner load power consumption; X is temperature; A2 is the maximal value that y can get, i.e. the upper limit of electric air conditioner load; A1 is the value of x=0 o'clock y, i.e. the lower limit of electric air conditioner load; X0 is the medium temperature of the power consumption temperature section that slope is larger while rising with temperature; P is the growth power law of temperature.Curve and physical significance schematic diagram are as shown in Figure 4.
Although with logistic curve and equally matched by the related coefficient obtaining after higher order polynomial matching, can also obtain the related coefficient higher than logistic curve if improve polynomial exponent number.But with fitting of a polynomial, often in field of definition, do not restrain outward, the bound that existence function does not approach, more can embody the physical significance of high temperature season electric load power consumption and temperature relation and carry out matching with logistic curve.
When carrying out curve fitting, first to carry out linearization to logistic curve, formula (1-2) is carried out to linearization process.Formula (1-2) is converted to rear both sides and takes the logarithm, carry out change of variable, obtain linear relationship:
Order
,
,
, A is constant,
with
be the nonlinear function about y and x, formula (1-5) is transformed to linear equation:
According to method recited above, by least square method, solve formula (1-5), finally obtain the parameter value in logistic curve formula (1-1).
Choose the Chinese Capital Beijing city for research city, data time is that in January, 2005 is to Dec.Beijing's total area 1.64 ten thousand sq-kms, total population approximately 2,018 ten thousand; Urban area 1.37 ten thousand sq-kms.Beijing is centered close to N39 ° of 54'20'', and E116 ° of 25'29'' is the moistening continental monsoon climate in typical warm temperate zone half, and summer high temperature is rainy, and winter is cold dry, and Extreme Maximum Temperature in Summer is more than 42 ℃.
The data that gather comprise the every daily mean temperature in this city, the highest temperature, the lowest temperature and relative humidity, 96 load values that Power system load data comprises that every day interval gathers for 15 minutes and daily power consumption, and primary data sample is as shown in table 2 and table 3:
Table 2
| Date | Temperature on average (℃) | The highest temperature (℃) | The lowest temperature (℃) | Relative humidity (%) |
| 20050701 | 31.133 | 36.6 | 21.7 | 84 |
| 20050702 | 29.358 | 32.5 | 22.7 | 73 |
| 20050703 | 30.425 | 36.3 | 24.4 | 86 |
| 20050704 | 31.75 | 37.8 | 25.1 | 73 |
| 20050705 | 32.938 | 39.6 | 26.2 | 74 |
| 20050706 | 32.042 | 38.6 | 26.3 | 65 |
| 20050707 | 28.946 | 34.7 | 25.5 | 73 |
| 20050708 | 27.508 | 30.9 | 22.2 | 85 |
| 20050709 | 27.275 | 35.7 | 21.4 | 88 |
| 20050710 | 26.888 | 31.2 | 21.7 | 87 |
Table 3
Utilize isolated Beijing in 2005 May (removing on May 1 to 7) to electric air conditioner power consumption and the temperature in September to carry out matching, consider respectively a day lowest temperature, daily maximum temperature and these three kinds of situations of temperature on average, the coefficient R 2 obtaining is respectively 0.66,0.58,0.81, partial data is as shown in table 4, and matched curve is respectively as shown in Fig. 5,6 and 7.
Table 4
| Date | Temperature on average (℃) | The highest temperature (℃) | The lowest temperature (℃) | Idle call electric weight (10MWh) |
| 20050701 | 31.133 | 36.6 | 21.7 | 4 999.705 |
| 20050702 | 29.358 | 32.5 | 22.7 | 4 843.815 |
| 20050703 | 30.425 | 36.3 | 24.4 | 5 097.944 |
| 20050704 | 31.75 | 37.8 | 25.1 | 6 151.995 |
| 20050705 | 32.938 | 39.6 | 26.2 | 6 206.678 |
| 20050706 | 32.042 | 38.6 | 26.3 | 6 795.773 |
| 20050707 | 28.946 | 34.7 | 25.5 | 6 393.903 |
| 20050708 | 27.508 | 30.9 | 22.2 | 5 936.437 |
| 20050709 | 27.275 | 35.7 | 21.4 | 4 349.270 |
| 20050710 | 26.888 | 31.2 | 21.7 | 3 796.098 |
The function that daily mean temperature and the matching of electric air conditioner power consumption obtain is
The related coefficient that relatively this cubic fit obtains, can find out that the related coefficient after daily mean temperature and the matching of electric air conditioner power consumption is the highest, therefore daily mean temperature is the most relevant to the relation of electric air conditioner power consumption, this result is the situation in realistic life also, what daily power consumption reacted is the electricity consumption level of whole day, and what temperature on average characterized is the temperature conditions of whole day; Load is electricity consumption situation all the time, and the highest temperature is generally relevant to load peak.
Combined action to electric air conditioner load while first illustrating that by an example the warm and humid effect of tropical island effect, air conditioner load and these three kinds of effects of cumulative effect all exist.Beijing in 2005 continuous three workaday temperature situations in summer are as shown in table 5.
Table 5
| Suburb temperature (℃) | Urban district temperature (℃) | Urban district perception temperature under warm and humid effects (℃) | The revised temperature of process cumulative effect (℃) | |
| 20050719 | 31.3 | 33.8 | 35.07 | 38.24 |
| 20050720 | 33.2 | 35.7 | 34.66 | 42.91 |
| 20050721 | 32.7 | 35.2 | 34.09 | 50.17 |
It is generally acknowledged when temperature surpasses 35 ℃ to be hot weather.Although the temperature of this their location, city in these three days is not high, under the effect of urban heat land effect, temperature average specific suburb, urban district temperature is high 2.5 ℃, and urban district belongs to the state of continuous high temperature.Due to the warm and humid effect that sauna day exists, human perception to temperature further promote, can introduce in detail in after the computing method of perception temperature.Meanwhile, cumulative effect can strengthen the electric air conditioner load of continuous high temperature day, and the temperature of the 3rd day is equivalent to 50.17 ℃ after the correction of cumulative effect, and cumulative effect is introduced in detail after the modification method meeting of temperature.Therefore, actual electric power air conditioner load power consumption can be much larger than the power consumption obtaining according to temperature prediction, in this example, residents in Beijing on July 21st, 2005 the actual temperature of experiencing be 50.17 ℃ rather than 32.7 ℃.Conversely, in summer city, the air conditioner load of great share is intensive to outside environmental emission used heat and greenhouse gases, has seriously aggravated urban heat land effect.
Along with city size is increasing, (it is its lower bound that air sphere be take the surface, land and water of the earth to city underlying surface, be called atmospheric underlying surface, it comprises landform, geology, soil and vegetation etc., one of key factor affecting weather) structure becomes increasingly complex, the waste heat discharge strength increase of industrial load and electric air conditioner load, vehicle exhaust isothermal chamber gas discharging strength increase, causes the temperature of zones of different in city to have larger difference.The degree of urban heat land effect can with " heat island intensity (
hII) " characterize, be poor between certain some temperature and reference point temperature in simple terms.But how accurate description heat island intensity is still explored, and its computing method have multiple.There are a lot of scholars to adopt respectively under study for action daily maximum temperature, daily mean temperature, day lowest temperature, monthly mean temperature, average temperature of the whole year etc. to discuss.While calculating heat island intensity, conventionally all with monitoring point, city, compare with rural monitoring point, urban heat island strength equals monitoring point, city actual measurement temperature and deducts rural monitoring point actual measurement temperature.But along with urbanization process fast, many cities can not find rural monitoring point that longitude and latitude height above sea level is close nearby.If the rural monitoring point of selecting is far away apart from monitoring point, city, should eliminate the temperature deviation bringing because of natural causes such as latitude, longitude, sea level elevation, special geomorphologies.And rural monitoring point is along with expanding economy, monitoring point temperature also can be subject to the impact of tropical island effect to a certain extent.
The heat island strength calculation method that this problem adopts is to deduct basic air temperature with the temperature on average in city, and the temperature mean value of choosing a plurality of places, peri-urban suburb is basic air temperature, expressed suc as formula (1-13).
（1-13）
Human body is taken away heat by sweat evaporation and is reduced self temperature, if relative humidity is around excessive, the rate of evaporation of sweat will reduce, the situation of human body when now institute's heat content can be greater than uniform temp dry air.Therefore, only by the temperature that temperature is described human body sensory, be also nowhere near, relative humidity should be considered into equally.Relative humidity is the ratio of water in air vapor partial pressure power and synthermal lower saturated vapor partial pressure, and it has reflected that in soft air, vapour content approaches saturated degree.The less air of relative humidity is drier, and the ability that absorbs water vapor is stronger; Otherwise air is moister, the ability that absorbs water vapour is more weak.When relative humidity is 100%, air no longer has wettability power.
Effective temperature is a kind of hotness index of human body, by the temperature that makes human body reach comfort under static saturated atmosphere condition (relative humidity=100%, during the m/s of wind speed=0), represent certain specific temperature, wind speed and the relative humidity that makes human body produce same sensation.For example three kinds of following situations are all equivalent to 17.7 ℃ of effective temperatures: (a) temperature=17.7 ℃, relative humidity=100%, wind speed=0 m/s; (b) temperature=22.4 ℃, relative humidity=75%, wind speed=0.5 m/s; (c) temperature=25 ℃, relative humidity=20%, wind speed=2.5 m/s.
The computing method of comfort index are based on formula (1-10), and this formula is binary quadratic polynomial, has considered the impact of humidity on the correction of temperature.But different scholars has proposed different multinomial coefficient choosing methods.
Wherein,
hI=comfort index (Fahrenheit),
t=temperature (Fahrenheit),
r=relative humidity (number percent).
To proxima luce (prox. luc) or even the impact of temperature a few days ago.Therefore, should be weighted on average the temperature of the same day and proxima luce (prox. luc), shown in (1-11):
Wherein,
λweight for temperature.To different temperature
t,
λdifferent.Generally speaking, at high temperature season, temperature is higher, larger on the impact of human perception temperature, therefore
λshould be also larger.There is document to provide the modification method of a cumulative effect, in the method weight
λand temperature
tproportional.Weight
λcomputing method suc as formula shown in (1-12), the method can meet above-mentioned Changing Pattern.As shown in Figure 8, provided weight
λwith temperature
tgraph of a relation
.
It should be noted that, when calculating correction temperature, the temperature of proxima luce (prox. luc) should be used proxima luce (prox. luc) correction temperature later.Because what affect human body sensory is to revise temperature later, but not original temperature.
City microclimate combines the impact of warm and humid effect and these three kinds of effects of cumulative effect of urban heat land effect, air conditioner load on the impact of electric air conditioner load.
If we use
represent the temperature after these three kinds of effect combined actions, so
can use formula (1-13) to represent:
Wherein,
represent the effect of tropical island effect to temperature,
;
represent the effect of warm and humid effect to temperature,
;
represent the effect of cumulative effect to temperature,
.
With
replace changing original
tre-start the matching of relation between temperature and electric air conditioner power consumption, obtain new funtcional relationship
.Owing to being
but not
ttherefore affect electric air conditioner power consumption, can think new funtcional relationship
the actual relationship that can reflect temperature and electric air conditioner power consumption.
If these three kinds of effects of the warm and humid effect of urban heat land effect, air conditioner load and cumulative effect do not exist, electric air conditioner power consumption should be
; Consider the combined influence of these three kinds of effects, electric air conditioner power consumption is
.The variable quantity of the electric air conditioner power consumption that therefore, these three kinds of effect combined actions cause should be
.Consider the independent impact of every kind of effect, the variable quantity of the electric air conditioner power consumption being caused separately by cumulative effect equals
; The variable quantity of the electric air conditioner power consumption being caused separately by warm and humid effect equals
; The variable quantity of the electric air conditioner power consumption being caused separately by urban heat land effect equals
.Because formula (1-13) is nonlinear, so the combined action of the warm and humid effect of urban heat land effect, air conditioner load and these three kinds of effects of cumulative effect is not the effect sum of these three kinds of effect independent roles.
value in part field of definition is amplified rapidly, so can think that the combined action of three kinds of effects is greater than the summation of its independent role.
Adopt Beijing in summer each department temperature record in 2005, form 6 is the average surface temperature in each area under one's jurisdiction, Beijing summer in 2005 (unit: ℃);
Table 6
| Yanqing | Huairou | Miyun | Mentougou | Fangshan | Pinggu | Changping | Shunyi | Tongzhou | Daxing | Urban district |
| 22.68 | 21.26 | 22.64 | 22.78 | 23.86 | 23.53 | 24.27 | 24.86 | 25.14 | 25.24 | 26.26 |
Because overcast area has a reservoir that area is very large, earth's surface temperature is had to great impact, if be elected to be basic air temperature, larger error will be produced, therefore when calculating basic air temperature, disregard the temperature of overcast area, the temperature on average that obtains each district is 23.74 ℃, calculate approximately 2.5 ℃ of average heat island intensity, think that the heat island intensity of Beijing in 2005 is 2.5 ℃.
According to formula (1-10), can calculate comfort index
hI, on the correction of temperature, this formula has been considered the impact of humidity, adopts:
c 1=-42.38,
c 2=2.049,
c 3=10.14,
c 4=-0.2248,
c 5=-6.838 * 10-3,
c 6=-5.482 * 10-2,
c 7=1.228 * 10-3,
c 8=8.528 * 10-4,
c 9=-1.99 * 10-6, temperature
tshould be greater than 80 ℉, be equivalent to 27 ℃; Relative humidity
rshould be greater than 40%, in computation process, temperature to be carried out degrees Fahrenheit and degree Celsius conversion.
According to formula (1-10), with Beijing in 2005, calculated to mean daily temperature and the relative humidity on August 23 June 21, can obtain the comfort index of every day, part is as shown in Table 7.Can find out, in the summer high temperature time, temperature is subject to the impact of relative humidity, make human perception to actual temperature have rising in various degree.
Table 7
| Date | Temperature on average (℃) | Relative humidity (%) | Comfort index (℃) |
| 20050701 | 31.133 | 84 | 32.45 |
| 20050702 | 29.358 | 73 | 30.34 |
| 20050703 | 30.425 | 86 | 31.83 |
| 20050704 | 31.75 | 73 | 32.64 |
| 20050705 | 32.938 | 74 | 33.78 |
| 20050706 | 32.042 | 65 | 32.62 |
| 20050707 | 28.946 | 73 | 29.94 |
| 20050708 | 27.508 | 85 | 28.86 |
| 20050709 | 27.275 | 88 | 28.71 |
| 20050710 | 26.888 | 87 | 26.888 |
By the comfort index calculating, substitute original temperature, with S curve again matching May (removing on May 1 to 7), to the relation of September temperature and idle call electric weight, obtain relation function suc as formula shown in (1-14), matched curve as shown in Figure 9:
The related coefficient of curve
r 2by 0.81 before revising, do not rise to 0.82, this shows, the relation between comfort index and electric air conditioner power consumption is tightr than temperature.
According to the method described above, the temperature in Beijing June 21 to August 23 in 2005 is carried out to the correction of cumulative effect, part day revised temperature as shown in Table 8:
Table 8
| Date | Temperature on average (℃) | The temperature of revising after cumulative effect (℃) |
| 20050630 | 29.979 | |
| 20050701 | 31.133 | 29.960 |
| 20050702 | 29.358 | 29.664 |
| 20050703 | 30.425 | 30.052 |
| 20050704 | 31.750 | 35.493 |
| 20050705 | 32.938 | 40.187 |
| 20050706 | 32.042 | 46.068 |
| 20050707 | 28.946 | 38.433 |
| 20050708 | 27.508 | 33.846 |
| 20050709 | 27.275 | 31.078 |
| 20050710 | 26.888 | 29.269 |
Revised temperature is substituted to original temperature, with S curve again matching May (removing on May 1 to 7), to the relation of September temperature and idle call electric weight, obtains relation function suc as formula shown in (1-15), matched curve as shown in figure 10:
The related coefficient of curve
r 2by 0.81 before revising, do not rise to 0.87, illustrate and consider the service condition of people to the more realistic air-conditioning of accumulation impression of temperature.And the cumulative effect modification method that this problem proposes is effective feasible.
Cumulative effect is not single direction on the impact of electric air conditioner power consumption, and it is sure increase idle call electric weight unlike tropical island effect or warm and humid effect.In the situation of continuous high temperature, the cumulative effect idle call electric weight that can greatly increase electric power.But if while within many days, there is high temperature after low temperature, be subject to the temperature that affects high temperature human body sensory on the same day of cumulative effect lower than actual temperature, to cause high temperature idle call on same day electric weight to reduce to some extent.As a whole, in summer, cumulative effect has increased the total electricity consumption of electric air conditioner.
Three kinds of comprehensively Digital Simulations on air conditioner load impact of effect; Calculate Beijing in 2005 mean daily temperature in summer and be subject to the temperature after city microclimate affects
.Because the weather data gathering is city, in temperature data, comprised the impact of urban heat land effect, so need not carry out the correction of tropical island effect.In the Summer Solstice (June 21) to the Limit of Heat (August 23) that the time interval of definition high temperature season in 2005 is Chinese traditional solar term, think and during this period of time, have urban heat land effect, warm and humid effect and cumulative effect simultaneously.
By the temperature of June 21 to August 23
treplace with
, then matching May (removing on May 1 to 7),, to the daily mean temperature in September and the relation between electric air conditioner power consumption, matched curve was as shown in figure 11.The funtcional relationship that matching obtains is suc as formula shown in (1-16):
（1-16）
Related coefficient rises to 0.90 from original 0.81, illustrates that what affect electric air conditioner power consumption is the temperature that affected by city microclimate
rather than temperature T.
Claims (4)
1. a method for building up for city microclimate and electric air conditioner load relation, is characterized in that, comprises the following steps:
A). statistics daily power consumption, for its annual daily power consumption of certain Urban Statistical to be studied;
B). calculate normal load, according to step a) described in the weather conditions in city, pick out some or certain several month of not using air-conditioning, take and week go out the average normal load of every day on Monday to Sun. as computation of Period;
C). calculate electric air conditioner load in summer, according to step a) described in the weather conditions in city, pick out some or certain use the month of air-conditioning several summers; The daily power consumption of every day is deducted to the normal load of corresponding day, calculate electric air conditioner load in summer; If obtain summer electric air conditioner load be respectively
,
,
,
;
D). the medial temperature of adding up every day, for the month of the use air-conditioning of picking out, records the medial temperature numerical value of every day
, establish it and be respectively
,
,
,
;
E). based on tropical island effect, to temperature correction, establish the heat island intensity in city to be studied
meet:
=
, the temperature after tropical island effect effect
meet:
F). based on warm and humid effect, to temperature correction, the comfort index of establishing city to be studied is
, the temperature after warm and humid effect effect
for:
G). based on cumulative effect to temperature correction, through the revised temperature of cumulative effect, be:
Wherein,
for the weight of temperature, for different temperature
, its weight
be different, it can be asked for by following formula:
H). set up electric air conditioner load relational expression, the daily power consumption of electric air conditioner load and the relation of temperature represent with following functional relation:
Wherein,
electric air conditioner load daily power consumption,
for mean daily temperature is through tropical island effect, warm and humid effect and the revised value of cumulative effect,
be
the maximal value that can get, the i.e. upper limit of electric air conditioner load;
be
time
value, i.e. the lower limit of electric air conditioner load;
it is the medium temperature of power consumption temperature section of slope maximum while rising with temperature;
it is the growth power law of temperature;
I). adopt least square method to solve, according to step c) in the load of the electric air conditioner asked for
,
,
,
and through step e), f), g) revised temperature
, utilize least square method to ask each parameter value in definite formula (1-1);
J). the prediction of electric air conditioner load, according to the daily power consumption of the electric air conditioner load of asking for and the relation of temperature
, utilize the modified value of following one day or a few days prediction medial temperature, i.e. the trend of measurable load in a short time, for the scheduling of electric system provides the reference frame of science.
2. the method for building up of city according to claim 1 microclimate and electric air conditioner load relation, is characterized in that: represent the temperature after urban heat land effect, warm and humid effect and these three kinds of effect combined actions of cumulative effect, so
can represent with formula (1-13):
Wherein,
represent the effect of tropical island effect to temperature,
;
represent the effect of warm and humid effect to temperature,
;
represent the effect of cumulative effect to temperature,
;
The variable quantity of the electric air conditioner power consumption being caused separately by cumulative effect equals
; The variable quantity of the electric air conditioner power consumption being caused separately by warm and humid effect equals
; The variable quantity of the electric air conditioner power consumption being caused separately by urban heat land effect equals
.
3. the method for building up of city according to claim 1 and 2 microclimate and electric air conditioner load relation, it is characterized in that: described c1=-42.38, c2=2.049, c3=10.14, c4=-0.2248, c5=-6.838 * 10-3, c6=-5.482 * 10-2, c7=1.228 * 10-3, c8=8.528 * 10-4, c9=-1.99 * 10-6.
4. the method for building up of city according to claim 1 and 2 microclimate and electric air conditioner load relation, is characterized in that step I) in utilize least square method to ask the method for true parameter value to comprise the following steps:
I-1). formula (1-1) belongs to the variation of following formula:
（1-2）
I-2). formula (1-2) is carried out to linearization process, formula (1-2) is changed to rear both sides and take the logarithm, carry out variable conversion, obtain following relational expression:
（1-3）
After further changing, obtain
(1-4)
I-3). formula (1-4) is transformed to linear equation, order
,
,
, formula (1-4) is changed to linear equation:
(1-5)
Wherein,
for constant,
with
be about
with
nonlinear function;
I-4). utilize least square method to solve formula (1-5), finally can obtain each parameter value in formula (1-1).
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