CN107120789B - A kind of air-conditioner set power adaptive method of adjustment - Google Patents
A kind of air-conditioner set power adaptive method of adjustment Download PDFInfo
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Abstract
The invention discloses a kind of air-conditioner set power adaptive method of adjustment, including step：(1) air conditioning energy consumption grade is set, sets air-conditioner set least unit power △, detection obtains current air-conditioner set power V (t), setting air-conditioner set theoretical power (horse-power) H (t)；(2) calculate it is following [t, t+L) period L time slot in available mean power increment；(3) number of each energy consumption grade appearance is counted according to historical data, then calculates the frequency that a certain air conditioning energy consumption grade x occurs in the L time slot；(4) the overflow probability P that newly-increased air conditioner load exceedes current air-conditioner set power is calculatedt+L；(5) P is determined whethert+L<PQoS, air-conditioning start power is adjusted to H (t) by V (t) if meeting；If it is not, H ' (t) ← H (t)+△, return to step 2 recalculate.This method according to the demand history data measured can judge that the numerical value of air-conditioner set startup power should be set so that the air-conditioner set peak power of load in certain period of time not over operating.
Description
Technical Field
The invention relates to a self-adaptive adjusting method for the power of an air conditioning unit.
Background
The existing air conditioning unit power adjustment is to match the user load at the current moment according to historical statistical data of a period of time, and the load estimation method based on the empirical mode cannot accurately reflect the load change in a short time, has poor real-time performance, causes the working mode of the air conditioning unit to be frequently switched under two conditions of overload and no load, wastes energy and cannot guarantee the safety of the air conditioning unit.
Disclosure of Invention
The purpose of the invention is as follows: in view of the above-mentioned prior art, a method for adaptively adjusting the power of an air conditioning unit is provided, which can determine the value of the starting power of the air conditioning unit to be set according to the measured historical load data, so that the load in a certain period of time does not exceed the maximum power of the operating air conditioning unit.
The technical scheme is as follows: an adaptive adjusting method for the power of an air conditioning unit comprises the following steps:
step 1: setting the energy consumption level of an air conditioner, setting the minimum unit power delta of an air conditioning unit, setting the power adjustment period T of the air conditioner, detecting to obtain the power V (T) of the current air conditioning unit, and setting the theoretical power H (T) of the air conditioning unit;
step 2: calculating the average power increment a (t, L) available in L slots of the future [ t, t + L) period as:
wherein t is the current moment;
and 3, step 3: counting the frequency of the occurrence of each energy consumption level in the time period of [ t-L +1, t) according to historical data, and then calculating the frequency of the occurrence of the energy consumption level x of a certain air conditioner in the L time slotsComprises the following steps:
wherein L is x Is the number of times of occurrence of the air-conditioning energy consumption level x in the time period of [ t-L +1, t);
for the frequencySmoothing to obtain the probability distribution pi of the energy consumption level x in the future [ t, t + L) time period x (t) is:
where ρ is a smoothing factor, π x (t-1) probability distribution of energy consumption levels in the future L time slots obtained at the last moment;
and 4, step 4: calculating the overflow probability P of the newly added air conditioner load exceeding the current air conditioner set power t+L Comprises the following steps:
according to Crame's theorem in large deviation theory, there are:
wherein the content of the first and second substances,is the total increment of the air-conditioning load in the future [ t, t + L) time period, and L is a time period independent variable; m (theta) is a finite moment generating function, M (theta) = Ee θI(t) E represents a mathematical expectation, and theta is a undetermined parameter; defining the newly increased air conditioning load requirement as I (t) in the time slot [ t-1, t);
when I (t) (t =1,2, \ 8230;) is a markov process,
wherein x is min Indicating the minimum energy consumption level, x, of the air conditioner occurring in the process max Represents the maximum energy consumption level of the air conditioner occurring in the process;
and 5: judging whether P exists t+L <P QoS If so, adjusting the starting power of the air conditioner from V (t) to H (t); if not, H' (t) ← H (t) + Delta, return to step 2 for recalculation; wherein, P QoS Is the air conditioner performance probability;
and 6: and starting judgment of the next time period, T ← T + T.
Further, in the step 3, the number L of times of occurrence of the energy consumption level x in the [ t-L +1, t) time period is counted according to the historical data x The method comprises the following steps: at time t-1, a sliding window Win (t-1) = [ I (t-L), I (t-L + 1), \ 8230; I (t-1) is defined]It contains the measured air-conditioning load information at L moments; the time is pushed back in sequence, and at the moment t, the sliding window comprises Win (t) = [ I (t-L + 1), I (t-L + 2), \ 8230; I (t)]The latest data, if at a certain time slot, has I (t) = x, defines an exemplary function:
thereby obtaining the number L of times that the energy consumption level x appears in the time window [ t-L +1, t) ] x Comprises the following steps:
furthermore, the value range of the smoothing factor rho is 0.7-0.9.
Has the advantages that: the method can judge the value of the starting power of the air conditioning unit to be set according to the measured historical load data, so that the load in a certain time period can not exceed the maximum power of the running air conditioning unit. The number of the air conditioning units is dynamically adjusted according to the change condition of the newly added load, and energy conservation is facilitated.
Detailed Description
The method of the invention is based on the following assumptions:
(1) The load change is a Markov process, i.e. for a certain time t, the load value is related to the load at the last time (t-1) and is not related to the past historical data (t-2, t-3, \8230;).
(2) The load change is an independent increment process, namely the increment of the internal load of [ t-1, t) is independent from the increment of [ t, t + 1), [ t +1, t + 2), \ 8230, and the increment of the internal load is independent from each other.
(3) According to the fuzzy method, the air conditioner power is divided into a plurality of energy consumption levels, and the energy consumption levels are represented by 1,2, \8230N.
And setting the power of the air conditioner host started at the current t moment as V (t). Defining the newly increased air conditioning load demand as I (t) during time slot [ t-1, t), so that the total increase in air conditioning load during the future time period [ t, t + L) is:
the aim is to adjust the current air conditioner main machine power to H (t) so as to increase the total air conditioner load by I t+L (t) the newly increased power of the air conditioning unit is not exceeded, namely:
I t+L (t)≤H(t)-V(t) (1)
the newly increased air conditioning load can be positive or negative, the positive represents the increase of the load, the negative represents the reduction of the load corresponding to the increase of the air conditioning part unit, the negative represents the reduction of the load corresponding to the closing of the air conditioning part unit, and the number of the air conditioning units is dynamically adjusted according to the change condition of the newly increased load, thereby being beneficial to realizing energy conservation.
Note that the establishment of equation (1) only indicates that the total increase of the air conditioning load matches the new increased power of the air conditioning unit in the [ t, t + L) time period, and does not indicate that the power of the air conditioning unit can match the load demand in each time slot in the [ t, t + L) time period, so a further strict matching condition is required:
defining the average power increment a (t, L) available in L slots of the future [ t, t + L) period as:
then (1) is re-expressed as:
namely, it is
Thus, the newly added air conditioning load, namely the possibility (overflow probability) that the load demand exceeds the current air conditioning unit power is as follows:
given that the load variation is an independent incremental process, I (t) (t =1,2, \8230;) is thus random variables independent of each other, and there is a finite moment generating function M (θ) = Ee θI(t) E represents a mathematical expectation, and theta is a undetermined parameter; the newly increased air conditioning load demand is defined as I (t) during the time slot [ t-1, t ].
If it is usedAccording to Crame's theorem in large deviation theory, there are:
according to the formulaTheta and the maximum value of the term can be found. When I (t) (t =1,2, \8230;) is a markov process,
wherein x is min Indicating the minimum energy consumption level, x, of the air conditioner occurring in the process max Represents the maximum energy consumption level of the air conditioner occurring in the process; pi x (t) represents the probability distribution π for the energy consumption level x over the future time period [ t, t + L ] x (t)。
Thus, the object to be achieved by the present invention can be described as: how to select proper air conditioning unit power H (t) to increase air conditioning load with probability P t+L The current working power of the unit can not be exceeded.
In order to better characterize the resource utilization of the air conditioning unit, an air conditioning performance probability P is defined QoS . Probability of air conditioning performance P QoS Set by the user, for example, that the set air conditioning power, after this adjustment, can satisfy the subsequent demand, P, with a probability of 99% QoS It is set to 99%. If it is desired to pass this adjustment, the set air conditioning power is certain to meet the subsequent demand, P QoS It is set to 100%.
1) If P t+L <P QoS The power of the currently started air conditioning unit meets the load increasing requirement, namely the performance probability P is met in the time slot QoS 。
2) If P t+L >P QoS Indicates null of current start-upThe power of the set of engines is insufficient, and at the moment, the set of engines can be adjusted according to the following measures:
H′(t)←H(t)+△
and delta represents the minimum power of the unit air conditioning unit capable of being started.
Thereby calculating whether P can be satisfied under the new air conditioning unit power H' (t) t+L <P QoS
Theoretically, the appropriate air conditioning operating power H' (t) can be calculated so that P is equal to P, from the load at each time in the [ t, t + L) period t+L <P QoS . However, in actual practice, assume that the current time is t, so that there is P in the subsequent [ t, t + L) time period t+L <P QoS The load of the air conditioner during the time period t, t + L) must be known, which is not possible at the present moment. For this purpose, P is calculated by a sliding window based processing method, i.e. the unknown I (t) (t =1,2, \ 8230;, L) is replaced by the past record I (t-L) (L =1,2, \ 8230;, L) t+L 。
However,. Pi. x Still unknown, the present invention proposes a sliding window observation based pi x The parameter estimation method comprises the following steps:
at time t-1, a sliding window Win (t-1) = [ I (t-L), I (t-L + 1), \8230; I (t-1) ] is defined, which contains measured air conditioning load information for L times. Time is sequentially pushed backwards, at the time t, the sliding window contains Win (t) = [ I (t-L + 1), I (t-L + 2), \ 8230; I (t) ] latest data, and if in a certain time slot, I (t) = x, a characteristic function is defined:
thereby obtaining the number L of times that a certain energy consumption level x appears in the time window [ t-L +1, t ] x Comprises the following steps:
thus in the time window [ t-L +1, t)Frequency of occurrence of internal, energy consumption class xComprises the following steps:
by frequencyReplacing the probability pi x Errors and jitter will occur, so a smoothing factor ρ (0 ≦ ρ ≦ 1) is introduced to smooth the estimate π x ：
When the size L of the sliding window is sufficiently large, the frequency converges on the probability. However, when the size L of the sliding window is sufficiently large, a large amount of data needs to be processed, the amount of calculation increases as the amount of memory increases, and for the detection of the air conditioning load, L =2000 or so is general, and the forgetting factor ρ is set to a value between 0.7 and 0.9.
The method of the invention thus obtained is in particular:
step 1: setting the energy consumption level of an air conditioner, setting the minimum unit power delta of an air conditioning unit, setting the power adjustment period T of the air conditioner, detecting to obtain the power V (T) of the current air conditioning unit, and setting the theoretical power H (T) of the air conditioning unit;
step 2: calculating an average power increment a (t, L) available in L slots of a future [ t, t + L) time period according to equation (2);
and step 3: counting the occurrence frequency of each energy consumption level according to historical data, and then calculating pi by using formulas (5) and (6) x (t)；
And 4, step 4: calculating logM (theta) according to a formula (4), and then calculating the overflow probability P of the newly-added air conditioning load exceeding the current air conditioning unit power according to a formula (3) t+L ；
Step (ii) of5: judging whether P exists t+L <P QoS If yes, adjusting the air conditioner starting power from V (t) to H (t); if not, H' (t) ← H (t) +. DELTA, return to step 2 for recalculation;
and 6: and starting judgment of the next time period, T ← T + T.
And (3) wild value treatment: generally speaking, the load in a time window can not fluctuate violently, and if the load fluctuates violently, the judgment can be made according to whether a fault exists on the site or not. After the possibility of occurrence of the failure is eliminated, it is judged that the load detection data is disturbed by the noise and the measurement value is invalid. At this time, a new measurement value can be calculated to replace the invalid measurement value, and the specific algorithm is as follows:
1. adding the valid data in the time window to obtain an average value:
wherein M is the number of valid data ξ i A valid number;
2. calculating the variance of the effective data in the time window:
3. obtaining a distribution function of a normal distribution
4. According to distribution functionDetection data ξ' are randomly generated to replace invalid data.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and amendments can be made without departing from the principle of the present invention, and these modifications and amendments should also be considered as the protection scope of the present invention.
Claims (3)
1. An adaptive adjusting method for the power of an air conditioning unit is characterized by comprising the following steps:
step 1: setting the energy consumption level of an air conditioner, setting the minimum unit power delta of an air conditioning unit, setting the power adjustment period T of the air conditioner, detecting to obtain the power V (T) of the current air conditioning unit, and setting the theoretical power H (T) of the air conditioning unit;
and 2, step: calculating the average power increment a (t, L) available in L slots of the future [ t, t + L) time period as:
wherein t is the current moment;
and step 3: counting the frequency of the occurrence of each energy consumption level in the time period of [ t-L +1, t) according to historical data, and then calculating the frequency of the occurrence of the energy consumption level x of a certain air conditioner in the L time slotsComprises the following steps:
wherein L is x Is the number of times of occurrence of the air-conditioning energy consumption level x in the time period of [ t-L +1, t);
for the frequencySmoothing to obtain the probability distribution pi of the energy consumption level x in the future [ t, t + L) time period x (t) is:
where ρ is a smoothing factor, π x (t-1) probability distribution of energy consumption levels in the future L time slots obtained at the last moment;
and 4, step 4: calculating the overflow probability P of the newly added air conditioner load exceeding the current air conditioner set power t+L Comprises the following steps:
according to Crame's theorem in large deviation theory, there are:
wherein, the first and the second end of the pipe are connected with each other,is the total increment of the air-conditioning load in the future [ t, t + L) time period, and L is a time period independent variable; m (theta) is a finite moment generating function, M (theta) = Ee θI(t) E represents mathematical expectation, and theta is a undetermined parameter; defining the newly increased air conditioning load requirement as I (t) in the time slot [ t-1, t);
when I (t) (t =1,2, \ 8230;) is a markov process,
wherein x is min Indicating the minimum energy consumption level, x, of the air conditioner occurring in the process max Represents the maximum energy consumption level of the air conditioner occurring in the process;
and 5: judging whether P exists t+L <P QoS If yes, adjusting the air conditioner starting power from V (t) to H (t); if not, increasing the power H (t) by delta to obtain a new air conditioning unit power H' (t), and returning to the step 2 for recalculation; wherein, P QoS Is the air conditioner performance probability;
and 6: and starting judgment of the next time period, and modifying the time T to T + T.
2. The adaptive power adjustment method for the air conditioning unit according to claim 1, wherein in the step 3, the number L of occurrences of the energy consumption level x in the [ t-L +1, t) time period is counted according to historical data x The method comprises the following steps: at time t-1, a sliding window Win (t-1) = [ I (t-L), I (t-L + 1), \ 8230; I (t-1) is defined]It contains the measured air-conditioning load information at L moments; the time is pushed back in sequence, and at the moment t, the sliding window comprises Win (t) = [ I (t-L + 1), I (t-L + 2), \ 8230; I (t)]The latest data, if at a certain time slot, has I (t) = x, defines a characteristic function:
thereby obtaining the number L of times that the energy consumption level x appears in the time window [ t-L +1, t) ] x Comprises the following steps:
3. the adaptive power adjustment method for the air conditioning unit according to claim 1 or 2, wherein the value range of the smoothing factor p is 0.7-0.9.
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