CN113819581A - Summer air conditioner load capacity and temperature sensitivity analysis method thereof - Google Patents
Summer air conditioner load capacity and temperature sensitivity analysis method thereof Download PDFInfo
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/30—Control or safety arrangements for purposes related to the operation of the system, e.g. for safety or monitoring
- F24F11/46—Improving electric energy efficiency or saving
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/62—Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
- F24F11/63—Electronic processing
- F24F11/64—Electronic processing using pre-stored data
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/70—Control systems characterised by their outputs; Constructional details thereof
- F24F11/80—Control systems characterised by their outputs; Constructional details thereof for controlling the temperature of the supplied air
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F11/00—Control or safety arrangements
- F24F11/88—Electrical aspects, e.g. circuits
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F24—HEATING; RANGES; VENTILATING
- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
- F24F2110/00—Control inputs relating to air properties
- F24F2110/10—Temperature
Abstract
The invention discloses a summer air conditioner load capacity and air temperature sensitivity analysis method, which relates to the field of power load prediction analysis, and comprises the steps of performing correlation analysis on historical load data and temperature data in summer to determine that the data with the maximum correlation is the highest load and the highest temperature, and modeling the highest load and the highest temperature data to obtain a highest temperature and highest load model; optimizing the highest temperature and highest load model by removing the highest load and highest temperature data with larger deviation to obtain a highest temperature and highest load optimal model; and obtaining a highest load prediction result by inputting the highest temperature and highest load optimal model according to the historical load data and temperature data in summer, and carrying out temperature sensitivity analysis on the highest load prediction result to obtain summer load temperature sensitivity, namely summer air conditioner load temperature sensitivity.
Description
Technical Field
The invention belongs to the field of power load prediction analysis, and particularly relates to a summer air conditioner load capacity and air temperature sensitivity analysis method.
Background
The commonly applied method of the existing load analysis technical scheme comprises the following steps: a maximum load comparison method, a reference load comparison method, a maximum temperature difference method, an equipment capacity and air conditioner quantity deduction algorithm and the like. For details, see the literature: qiu Qinjin, Zhoufeng, Liu liter, etc. Fujian province year maximum cooling load calculation analysis and research [ J ] Huadong power, 2005, 33 (20): 86-90.
The traditional maximum load comparison method is to obtain an air conditioner load curve by subtracting the average maximum load daily load curve in spring and autumn from the annual maximum load daily load curve. The analysis of the air conditioning load in summer needs to be carried out in summer of the year for guiding the development of the work of meeting the peak and summer of the year. If the traditional maximum load comparison method is adopted for analysis, the analysis of the air-conditioning load in summer in the year can be carried out after the autumn load data in the year is obtained after the autumn in the year passes, the guiding significance of the post analysis of the air-conditioning load in summer after the summer passes is necessarily reduced, and the traditional maximum load comparison method has hysteresis.
In the conventional method for predicting the air conditioning load, the load is not converted into a uniform temperature reference surface, the temperature has a significant influence on the air conditioning load, and the air conditioning load prediction without temperature correction tends to generate a large error.
Disclosure of Invention
The invention aims to provide a summer air conditioner load capacity and air temperature sensitivity analysis method, thereby overcoming the defect of prediction hysteresis of the existing air conditioner load.
In order to achieve the aim, the invention provides a summer air conditioner load capacity and air temperature sensitivity analysis method, which comprises the following steps:
acquiring historical load data in summer and temperature data corresponding to the historical load data;
performing correlation analysis on the historical load data and the temperature data, and determining data with the maximum correlation, wherein the data with the maximum correlation are the highest load and the highest temperature;
modeling the highest load and highest temperature data to obtain a highest temperature and highest load model;
optimizing the highest temperature and highest load model by removing data with larger deviation and holiday data to obtain an optimal model of the highest temperature and the highest load;
and inputting the historical load data and the temperature data in summer into the maximum temperature and maximum load optimal model to obtain a maximum load prediction result, and carrying out temperature sensitivity analysis on the maximum load prediction result to obtain the summer load temperature sensitivity.
Preferably, a pearson correlation coefficient analysis method is adopted to perform correlation analysis on the historical load data and the temperature data.
Preferably, modeling the maximum load and temperature data comprises: fitting the highest load and the highest temperature in the historical data date range through a quadratic function to obtain the maximum load fitting values under different temperature conditions, and displaying the maximum load fitting values by adopting a curve graph to obtain a highest temperature and highest load model.
Preferably, the maximum load prediction result is a curve of a function curve of the maximum temperature and maximum load optimal model extending out of historical data dates.
Preferably, the method further comprises analyzing the current summer air conditioning load for load air temperature sensitivity using an improved maximum load comparison method.
Preferably, the analysis of the current summer air conditioning load for load air temperature sensitivity using the improved maximum load comparison method comprises the steps of:
acquiring a maximum air conditioner load curve in the past year;
carrying out temperature sensitivity-based temperature correction on the historical maximum air conditioner load curve according to the summer load temperature sensitivity, and uniformly correcting the temperature sensitivity-based temperature correction to a load value under the same temperature;
and predicting the maximum daily load data of the predicted month in the year by performing curve fitting on the corrected data, thereby obtaining a maximum air conditioner load prediction curve and air conditioner capacity analysis in summer in the year.
Preferably, the air-conditioning load of the whole year is obtained by processing through a maximum load comparison method, and an air-conditioning load curve is obtained by fitting.
Preferably, the obtaining of the annual maximum air conditioning load curve by the maximum load comparison method includes: and selecting a reference load curve, and comparing the daily load curve of the maximum load day with the reference load curve to obtain the maximum air-conditioning load curve of the past year.
Compared with the prior art, the invention has the following beneficial effects:
1. according to the summer air conditioner load capacity and air temperature sensitivity analysis method provided by the invention, correlation analysis is carried out on historical load data and temperature data in summer, the data with the maximum correlation is determined to be the highest load and the highest temperature, and modeling is carried out on the highest load and the highest temperature data to obtain a highest temperature and highest load model; optimizing the highest temperature and highest load model by removing the highest load and highest temperature data with larger deviation to obtain a highest temperature and highest load optimal model, wherein the data processed by the highest temperature and highest load optimal model is more accurate; the method comprises the steps of inputting historical load data and temperature data in summer into a maximum temperature and maximum load optimal model to obtain a maximum load prediction result, carrying out temperature sensitivity analysis on the maximum load prediction result to obtain summer load temperature sensitivity, namely summer air conditioner load temperature sensitivity, wherein the weather sensitivity is a quantitative analysis result of a power index change rule along with weather indexes, the maximum load prediction result can be visually obtained through the summer load temperature sensitivity, and the summer air conditioner load temperature sensitivity analysis result also serves as a summer air conditioner load temperature sensitivity analysis result, so that more accurate data support is provided for follow-up power supply, prediction and the like.
2. In the summer air conditioner load capacity and air temperature sensitivity analysis method provided by the invention, the current summer air conditioner load is analyzed for the summer load air temperature sensitivity by adopting an improved maximum load comparison method, and a summer maximum air conditioner load prediction curve and air conditioner capacity analysis in the current year can be obtained, so that real-time prediction analysis is carried out, and the analysis can be carried out without waiting until the summer is over.
Drawings
In order to more clearly illustrate the technical solution of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only one embodiment of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on the drawings without creative efforts.
FIG. 1 is a flow chart of a summer air conditioning load capacity and air temperature sensitivity analysis method of the present invention;
FIG. 2 is a maximum temperature versus maximum load fit of normal data according to the present invention;
FIG. 3 is a plot of the highest temperature versus highest load fit for the present invention with more skewed data culling;
FIG. 4 is a plot of maximum temperature versus maximum load fit for the elimination of holiday data in accordance with the present invention;
FIG. 5 is a plot of maximum temperature versus maximum load fit for the invention with holiday data culled and data with large deviations;
fig. 6 shows a summer maximum air conditioning load curve from 2017 to 2020 and a summer maximum air conditioning load prediction curve from 2021 according to the present invention.
Detailed Description
The technical solutions in the present invention are clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 shows a summer air conditioner load capacity and air temperature sensitivity analysis method thereof, which comprises the following steps:
s1, acquiring historical load data in summer and temperature data corresponding to the historical load data;
s2, carrying out correlation analysis on the historical load data and the temperature data, and determining the data with the maximum correlation, wherein the data with the maximum correlation are the highest load and the highest temperature;
s3, modeling the highest load and the highest temperature data to obtain a highest temperature and highest load model;
s4, optimizing the highest temperature and highest load model by removing data with larger deviation and holiday data to obtain a highest temperature and highest load optimal model;
and S5, inputting the historical load data and temperature data in summer into the maximum temperature and maximum load optimal model to obtain a maximum load prediction result, and carrying out temperature sensitivity analysis on the maximum load prediction result to obtain summer load temperature sensitivity, namely summer air conditioner load temperature sensitivity. The historical load data is total load, but in terms of temperature sensitivity, the temperature sensitivity of the total load is the temperature sensitivity of the air conditioning load, and only the air conditioning load in the total load can change along with the temperature, so the summer load temperature sensitivity obtained through analysis is the summer air conditioning reddening temperature sensitivity.
According to the summer air conditioner load capacity and air temperature sensitivity analysis method, correlation analysis is carried out on historical load data and temperature data in summer, the data with the maximum correlation is determined to be the highest load and the highest temperature, modeling is carried out on the highest load and the highest temperature data, and a highest temperature and highest load model is obtained; optimizing the highest temperature and highest load model by removing the highest load and highest temperature data with larger deviation to obtain a highest temperature and highest load optimal model, wherein the data processed by the highest temperature and highest load optimal model is more accurate; the method comprises the steps of inputting the maximum temperature and maximum load optimal model of historical load data and temperature data in summer to obtain a maximum load prediction result, carrying out temperature sensitivity analysis on the maximum load prediction result to obtain summer load temperature sensitivity, namely summer air conditioner load temperature sensitivity, wherein the meteorological sensitivity is a quantitative analysis result of a power index change rule along with meteorological indexes, and the maximum load prediction result can be visually obtained through the summer load temperature sensitivity, so that more accurate data support is provided for follow-up power supply, prediction and the like.
In one embodiment, in step S2, a pearson correlation coefficient analysis method is used to perform correlation analysis on the historical load data and the temperature data.
In one embodiment, the step S3, the modeling the highest load and highest temperature data includes: fitting the highest load and the highest temperature in the historical data date range through a quadratic function to obtain the maximum load fitting values under different temperature conditions, and displaying the maximum load fitting values by adopting a curve graph to obtain a highest temperature and highest load model.
In one embodiment, in step S5, the highest load prediction result is a curve of the highest temperature and the highest load optimization model extended by the historical data date.
Specifically, the temperature sensitivity analysis is carried out on the highest load prediction result to obtain the summer load temperature sensitivity as follows: and obtaining the change interval of the highest load in corresponding different temperature interval ranges according to the curve extending out of the historical data date, and obtaining the summer load change amount when the temperature rises by 1 ℃ according to the change interval of the highest load in the different temperature interval ranges, namely the summer air-conditioning load temperature sensitivity.
In one embodiment, the method for analyzing summer air conditioning load capacity and air temperature sensitivity further comprises, S6, analyzing the current summer air conditioning load with respect to summer load air temperature sensitivity using an improved maximum load comparison method.
Specifically, the method for analyzing the current summer air-conditioning load by adopting the sensitivity of the improved maximum load comparison method to the summer load air temperature comprises the following steps:
s61, obtaining a historical maximum air conditioner load curve by adopting a maximum load comparison method;
s62, carrying out temperature sensitivity-based temperature correction on the maximum air conditioner load curve in the past year according to summer load temperature sensitivity, and uniformly correcting the maximum air conditioner load curve to a load value under the same temperature;
and S63, predicting the maximum daily load data of the predicted month in the year by fitting the data curve corrected in the step S62, so as to obtain a prediction curve of the maximum air-conditioning load in summer in the year and air-conditioning capacity analysis.
Specifically, the step S61 of obtaining the maximum air conditioning load curve over the years by using the maximum load comparison method includes: and selecting a reference load curve, and comparing the daily load curve of the maximum load day with the reference load curve to obtain the maximum air-conditioning load curve of the past year.
The reference load curve is the average of the daily load curves of the maximum load days of months 3 and 11.
The daily load curve of the maximum load day is the load curve of the maximum load day in summer.
The embodiments of the method for analyzing the load capacity and the temperature sensitivity of the summer air conditioner are explained in detail to make the technicians in the field understand the invention more:
s1, acquiring load data and temperature data corresponding to the load data from 1/5/6/4/2021;
s2, analyzing the correlation between the daily maximum load, the daily maximum temperature, the daily minimum temperature and the average temperature in the date range (in the data of the step S1) by adopting a Pearson correlation coefficient, wherein the correlation result is shown in Table 1;
TABLE 1 correlation of peak load with temperature
| Correlation coefficient | Maximum temperature | Minimum temperature | Mean temperature |
| Maximum load | 0.8034 | 0.5906 | 0.7888 |
As can be seen from the results in table 1, the correlation between the highest load and the highest temperature was the greatest between 5 months 1 and 6 months 4 in 2021, and therefore the data with the highest load and the highest temperature as the correlation was used as the data basis for the fitting modeling.
S3, fitting the highest load and the highest temperature in the date range through a quadratic function to obtain the maximum load fitting values under different temperature conditions, namely a highest temperature and highest load model;
and S4, obtaining four models according to whether the data with larger deviation is removed or not and whether holidays are removed or not.
Model one is a model of maximum temperature and maximum load of normal data, namely, the maximum load is fitted with the maximum temperature, and the fitting function is obtained by taking y as 1.5703x255.5117x +2364.3723, the results of the corresponding highest temperature and highest load model are a fit, as shown in FIG. 2.
The model II is a high-temperature and highest-load model for eliminating data with large deviation, and the obtained fitting function is as follows: 2.1624x293.6269x +2989.8787, the results of the high temperature and highest load model corresponding to the higher culled bias data are a fit, as shown in FIG. 3.
The model III is a high-temperature and highest-load model for eliminating data in the holiday period, and the obtained fitting function is as follows: 3.4214x2168.6705x +4113.8697, the results of the high temperature and highest load model for the corresponding culled holiday data are a fit as shown in FIG. 4.
The model IV is a model for eliminating the highest load and the highest temperature of data with large deviation and holiday data, and the obtained fitting function is as follows: 3.3454x2162.3391x +3994.7421, the results of the highest load and highest temperature model corresponding to culling data with large deviations and holiday data are a fit, as shown in FIG. 5.
The fitting function and the actual electric quantity have certain difference, namely prediction error. Therefore, the accuracy of the prediction result in four cases is measured by using a relative error e, and the expression of the relative error e is as follows:
in the formula (1), Y is an actual value,
is a predicted value.
The actual temperatures of 5 months, 1 day to 6 months, 4 days are respectively substituted into the four models, the predicted highest load and the actual highest load are compared to calculate the relative error, the precision of the obtained four models is shown in the following table 2,
TABLE 25 MORE 1-MORE 6-MORE 4 highest load prediction accuracy
According to model precision analysis, the trend that the highest load changes along with the temperature in summer working day of the year is predicted, the accuracy of the model four is highest, therefore, the model four is adopted for prediction, the highest temperature and the highest load model are optimized by removing the highest load and the highest temperature data which deviate from large data and holiday data, and the model four which is the optimal model of the highest temperature and the highest load is obtained.
And S5, inputting the summer historical load data and temperature data to be analyzed through a maximum temperature and maximum load optimal model to obtain a maximum load prediction result, and performing temperature sensitivity analysis on the maximum load prediction result to obtain summer load temperature sensitivity.
The meteorological sensitivity is a quantitative analysis result of the change rule of the power index along with the meteorological index, namely the slope of the curve of the change of the power index data along with the meteorological index data. The model four is adopted to predict the highest load of the summer working day of the year, the obtained highest load prediction result is shown in table 3,
TABLE 3 prediction of the highest load
And the highest load prediction result is predicted by outwards extending a function curve of the model IV, and the change interval of the highest load in different temperature interval ranges is obtained. Table 3 is analyzed to obtain the variation of the summer load (i.e., the variation of the summer air conditioning load) for each increase of 1 ℃ in the air temperature, i.e., the sensitivity of the summer air conditioning load to the air temperature is shown in table 4. The sensitivity of the air conditioning load to the air temperature refers to the change of the air conditioning load when the air temperature rises by 1 ℃ in summer.
TABLE 4 summer load change per 1 ℃ rise
S6, because it is difficult to directly obtain the measured data of the air-conditioning load, and the change of the power grid load in summer is not only related to the air-conditioning load, but also needs to comprehensively consider other factors, the air-conditioning load is generally calculated by adopting a maximum load comparison method, and the traditional maximum load comparison method utilizes the annual maximum load daily load curve to subtract the average maximum load daily load curve in spring and autumn, thereby obtaining the air-conditioning load curve.
The analysis of the air conditioning load in summer needs to be carried out in summer of the year for guiding the development of the work of meeting the peak and summer of the year. If the traditional maximum load comparison method is adopted for analysis, the analysis of the air-conditioning load in summer in the year can be carried out after the autumn load data in the year is obtained after the autumn in the year passes, the guiding significance of the post analysis of the air-conditioning load in summer after the summer passes is necessarily reduced, and the traditional maximum load comparison method has hysteresis.
Therefore, analyzing the current summer air conditioning load for summer load air temperature sensitivity using an improved maximum load comparison based method comprises the steps of:
at S61, since the temperature is suitable for 3 months (spring) and 11 months (autumn), and there is almost no cooling load, the average value of the daily load curves of the maximum load days of 3 months and 11 months is selected as the reference load curve. And selecting a daily load curve of a maximum load day from 6 months to 8 months (summer) to be compared with the obtained reference load curve to obtain a maximum air-conditioning load curve of the past year.
S62, using the summer load temperature sensitivity analysis result obtained in step S5, the obtained maximum air conditioning load of the past year is corrected to load value under the same temperature (such as 36 ℃) by temperature based on temperature sensitivity.
S63, predicting and obtaining the maximum daily load data of 11 months in the year in a curve fitting mode, and further obtaining the air conditioning load and the development trend of the air conditioning load in the past year.
As shown in fig. 6, fig. 6 shows a curve of the maximum air conditioning load in summer from 2017 to 2020 and a curve of the prediction of the maximum air conditioning load in summer from 2021. The air conditioning load and the development tendency thereof over the years, which are obtained from the summer maximum air conditioning load prediction curve of fig. 5, are shown in table 5,
TABLE 52017 to 2021 years air conditioning load and development trend
In summary, the summer air conditioner load capacity and air temperature sensitivity analysis method can predict the summer maximum air conditioner load and analyze the air conditioner capacity in the current year in time through the data of the current year, provide data support for power supply of a power grid, and analyze and plan the summer power consumption.
The above disclosure is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of changes or modifications within the technical scope of the present invention, and shall be covered by the scope of the present invention.
Claims (8)
1. A summer air conditioner load capacity and temperature sensitivity analysis method is characterized by comprising the following steps:
acquiring historical load data in summer and temperature data corresponding to the historical load data;
performing correlation analysis on the historical load data and the temperature data, and determining data with the maximum correlation, wherein the data with the maximum correlation are the highest load and the highest temperature;
modeling the highest load and highest temperature data to obtain a highest temperature and highest load model;
optimizing the highest temperature and highest load model by removing data with larger deviation and holiday data to obtain an optimal model of the highest temperature and the highest load;
and inputting the historical load data and the temperature data in summer into the maximum temperature and maximum load optimal model to obtain a maximum load prediction result, and carrying out temperature sensitivity analysis on the maximum load prediction result to obtain the summer load temperature sensitivity.
2. The summer air conditioning load capacity and air temperature sensitivity analysis method according to claim 1, characterized in that correlation analysis is performed on the historical load data and the temperature data using a pearson correlation coefficient analysis method.
3. The summer air conditioner load capacity and air temperature sensitivity analysis method according to claim 1, wherein modeling the maximum load and temperature data includes: fitting the highest load and the highest temperature in the historical data date range through a quadratic function to obtain the maximum load fitting values under different temperature conditions, and displaying the maximum load fitting values by adopting a curve graph to obtain a highest temperature and highest load model.
4. The summer air conditioning load capacity and air temperature sensitivity analysis method according to claim 1, wherein the maximum load prediction result is a curve of the maximum temperature versus maximum load optimization model as a function of extended historical data dates.
5. The summer air conditioning load capacity and air temperature sensitivity analysis method according to claim 1, further comprising analyzing the current summer air conditioning load for load air temperature sensitivity using an improved maximum load comparison method.
6. The summer air conditioning load capacity and air temperature sensitivity analysis method according to claim 5, wherein analyzing the current summer air conditioning load for load air temperature sensitivity using the improved maximum load comparison method comprises the steps of:
acquiring a maximum air conditioner load curve in the past year;
carrying out temperature sensitivity-based temperature correction on the historical maximum air conditioner load curve according to the summer load temperature sensitivity, and uniformly correcting the temperature sensitivity-based temperature correction to a load value under the same temperature;
and predicting the maximum daily load data of the predicted month in the year by performing curve fitting on the corrected data, thereby obtaining a maximum air conditioner load prediction curve and air conditioner capacity analysis in summer in the year.
7. The summer air conditioner load capacity and air temperature sensitivity analysis method according to claim 6, characterized in that the air conditioner load over the years is obtained by processing with a maximum load comparison method, and an air conditioner load curve is obtained by fitting.
8. The summer air conditioning load capacity and air temperature sensitivity analysis method according to claim 6, wherein obtaining the historical maximum air conditioning load curve using a maximum load comparison method comprises: and selecting a reference load curve, and comparing the daily load curve of the maximum load day with the reference load curve to obtain the maximum air-conditioning load curve of the past year.
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Citations (23)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US4061185A (en) * | 1975-05-16 | 1977-12-06 | Canada Square Management Ltd. | Temperature control system |
| JP2001074291A (en) * | 1999-09-07 | 2001-03-23 | Mitsubishi Heavy Ind Ltd | Method for controlling operating number of refrigerating machine |
| JP2001227794A (en) * | 2000-02-16 | 2001-08-24 | Daikin Ind Ltd | Method of estimating air-conditioning load, and method of controlling regenerative air-conditioning system, and these devices |
| JP2002022239A (en) * | 2000-07-11 | 2002-01-23 | Mitsubishi Heavy Ind Ltd | Load prediction device |
| JP2006078009A (en) * | 2004-09-07 | 2006-03-23 | Shimizu Corp | Air conditioning load predicting method |
| JP2010145070A (en) * | 2008-12-22 | 2010-07-01 | Mitsubishi Electric Corp | Air conditioning management device |
| CN102521674A (en) * | 2011-12-07 | 2012-06-27 | 福建省电力有限公司 | Computer generating method for short-term load forecasting in summer in power consumer electricity-utilization information acquisition system |
| CN103307711A (en) * | 2013-06-07 | 2013-09-18 | 昆山市远视商用空调工程有限公司 | Air conditioner control system and control method |
| CN103617565A (en) * | 2013-12-06 | 2014-03-05 | 国网山东省电力公司 | Method for establishing relationship between city micro weather and electric power air conditioner loads |
| US20140081704A1 (en) * | 2012-09-15 | 2014-03-20 | Honeywell International Inc. | Decision support system based on energy markets |
| CN104156783A (en) * | 2014-07-29 | 2014-11-19 | 广西电网有限责任公司 | Maximum daily load prediction system and method of electric system considering meteorological accumulative effect |
| CN104156786A (en) * | 2014-08-18 | 2014-11-19 | 广西电网有限责任公司 | Non-working day maximum daily load forecasting system considering meteorological multi-factor influences |
| CN104881722A (en) * | 2015-06-11 | 2015-09-02 | 国网天津市电力公司 | Maximum load prediction method based on load decomposition |
| CN105701570A (en) * | 2016-01-11 | 2016-06-22 | 国网浙江省电力公司经济技术研究院 | Short-term electric power demand analysis method based on overall process technology improvement |
| CN105760947A (en) * | 2016-02-02 | 2016-07-13 | 国网江苏省电力公司 | Method for analyzing influence factors of meteorological environment on network load |
| CN105825294A (en) * | 2016-03-10 | 2016-08-03 | 国家电网公司 | Meteorological-factor-based grid power load prediction method and system |
| CN207799046U (en) * | 2018-01-23 | 2018-08-31 | 天津安消科技有限公司 | A kind of fire-fighting equipment Power Supply Monitoring sensor |
| CN108830415A (en) * | 2018-06-13 | 2018-11-16 | 国网江苏省电力有限公司电力科学研究院 | A kind of weather sensitive load power estimating method based on non-linear correlation model |
| CN110864414A (en) * | 2019-10-30 | 2020-03-06 | 郑州电力高等专科学校 | Air conditioner power utilization load intelligent control scheduling method based on big data analysis |
| CN111242805A (en) * | 2020-01-09 | 2020-06-05 | 南方电网科学研究院有限责任公司 | Method and device for calculating increase rate of power consumption |
| CN112149309A (en) * | 2020-09-30 | 2020-12-29 | 贵州电网有限责任公司 | Temperature-adjusting load curve fitting method considering temperature-load correlation |
| CN112734135A (en) * | 2021-01-26 | 2021-04-30 | 吉林大学 | Power load prediction method, intelligent terminal and computer readable storage medium |
| CN112923529A (en) * | 2021-01-25 | 2021-06-08 | 浙江理工大学 | Heating air conditioner temperature regulation and control method and system based on thermal history |
-
2021
- 2021-08-23 CN CN202110968929.3A patent/CN113819581B/en active Active
Patent Citations (23)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US4061185A (en) * | 1975-05-16 | 1977-12-06 | Canada Square Management Ltd. | Temperature control system |
| JP2001074291A (en) * | 1999-09-07 | 2001-03-23 | Mitsubishi Heavy Ind Ltd | Method for controlling operating number of refrigerating machine |
| JP2001227794A (en) * | 2000-02-16 | 2001-08-24 | Daikin Ind Ltd | Method of estimating air-conditioning load, and method of controlling regenerative air-conditioning system, and these devices |
| JP2002022239A (en) * | 2000-07-11 | 2002-01-23 | Mitsubishi Heavy Ind Ltd | Load prediction device |
| JP2006078009A (en) * | 2004-09-07 | 2006-03-23 | Shimizu Corp | Air conditioning load predicting method |
| JP2010145070A (en) * | 2008-12-22 | 2010-07-01 | Mitsubishi Electric Corp | Air conditioning management device |
| CN102521674A (en) * | 2011-12-07 | 2012-06-27 | 福建省电力有限公司 | Computer generating method for short-term load forecasting in summer in power consumer electricity-utilization information acquisition system |
| US20140081704A1 (en) * | 2012-09-15 | 2014-03-20 | Honeywell International Inc. | Decision support system based on energy markets |
| CN103307711A (en) * | 2013-06-07 | 2013-09-18 | 昆山市远视商用空调工程有限公司 | Air conditioner control system and control method |
| CN103617565A (en) * | 2013-12-06 | 2014-03-05 | 国网山东省电力公司 | Method for establishing relationship between city micro weather and electric power air conditioner loads |
| CN104156783A (en) * | 2014-07-29 | 2014-11-19 | 广西电网有限责任公司 | Maximum daily load prediction system and method of electric system considering meteorological accumulative effect |
| CN104156786A (en) * | 2014-08-18 | 2014-11-19 | 广西电网有限责任公司 | Non-working day maximum daily load forecasting system considering meteorological multi-factor influences |
| CN104881722A (en) * | 2015-06-11 | 2015-09-02 | 国网天津市电力公司 | Maximum load prediction method based on load decomposition |
| CN105701570A (en) * | 2016-01-11 | 2016-06-22 | 国网浙江省电力公司经济技术研究院 | Short-term electric power demand analysis method based on overall process technology improvement |
| CN105760947A (en) * | 2016-02-02 | 2016-07-13 | 国网江苏省电力公司 | Method for analyzing influence factors of meteorological environment on network load |
| CN105825294A (en) * | 2016-03-10 | 2016-08-03 | 国家电网公司 | Meteorological-factor-based grid power load prediction method and system |
| CN207799046U (en) * | 2018-01-23 | 2018-08-31 | 天津安消科技有限公司 | A kind of fire-fighting equipment Power Supply Monitoring sensor |
| CN108830415A (en) * | 2018-06-13 | 2018-11-16 | 国网江苏省电力有限公司电力科学研究院 | A kind of weather sensitive load power estimating method based on non-linear correlation model |
| CN110864414A (en) * | 2019-10-30 | 2020-03-06 | 郑州电力高等专科学校 | Air conditioner power utilization load intelligent control scheduling method based on big data analysis |
| CN111242805A (en) * | 2020-01-09 | 2020-06-05 | 南方电网科学研究院有限责任公司 | Method and device for calculating increase rate of power consumption |
| CN112149309A (en) * | 2020-09-30 | 2020-12-29 | 贵州电网有限责任公司 | Temperature-adjusting load curve fitting method considering temperature-load correlation |
| CN112923529A (en) * | 2021-01-25 | 2021-06-08 | 浙江理工大学 | Heating air conditioner temperature regulation and control method and system based on thermal history |
| CN112734135A (en) * | 2021-01-26 | 2021-04-30 | 吉林大学 | Power load prediction method, intelligent terminal and computer readable storage medium |
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