JP2005229758A - System for managing energy consumption - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide an energy consumption managing system for suppressing energy consumption, without impairing comfortableness. <P>SOLUTION: The energy consumption managing system is provided with a management server 10 for producing a control plan for energy consuming devices, such as an air conditioner 22; and a control server 12 for collecting information from the devices, transmitting the information to the management server, and controlling the devices, based on the control plan. The management server periodically transmits plan information within a predetermined period to the control server. The management server predicts the degree of busyness, from data indicating the degree of busy pressure and regional weather data, and creates the control plan from predicted values, measured values such as the temperature, setting values of the devices, and weather report data. The system accurately produces the control plan, and suppresses energy consumption to a minimum, without impairing comfort. <P>COPYRIGHT: (C)2005,JPO&NCIPI

Description

  The present invention relates to an energy consumption management system, and more particularly to an energy consumption management system that suppresses energy consumption without deteriorating comfort and required conditions by optimization based on statistical processing. .

Conventionally, a system for managing power consumption of an air conditioner or the like in a store or an office has been proposed. For example, Patent Document 1 below discloses a technology for controlling power saving of various devices in a store such as a convenience store by an independent distribution method.
JP 2002-27686 A

  The above-described conventional power control system has a problem that optimization by statistical processing based on past operation record data, weather data, or the like is not achieved. In addition, there is a problem that wasteful energy is consumed because the viewpoint of suppressing energy consumption is not considered while satisfying subjective comfort.

  The present invention aims to solve the above-described problems, and for this purpose, the energy consumption management system of the present invention is a management server that creates a control plan for energy consuming equipment based on at least information obtained from the control server means. And a control server means for collecting information from at least the energy consuming equipment and sending the information to the management server means to control the energy consuming equipment based on the control plan. Further, the management server means is characterized in that the plan information for a predetermined period is periodically transmitted to the control server means.

  Further, the control server means comprises busy degree data collecting means for collecting data indicating the busy degree and sending it to the management server, wherein the management server means comprises at least the data showing the busy degree and the weather data of the area. A point comprising busy degree predicting means for generating predicted data of busy degree, and control plan generating means for generating a control plan from the predicted data of busy degree, measured value and set value data of the control target device, and weather forecast data There are also features.

  The energy consumption management system of the present invention can create a highly accurate control plan by the above-described features, and can suppress energy consumption to the minimum without impairing comfort and required performance. There is an effect.

  The energy consumption management system according to the present invention uses a computer system to optimize energy demand prediction and automatically by minimizing energy consumption and keeping the target room temperature within an allowable range within the performance range of the control target device. This system is realized by controlled operation. In order to realize optimal control, the computer system processes the following data for each installation location of the device to be controlled.

(1) Time-series numerical data showing the degree of busyness For example, the following can be adopted. (A) In the case of store POS data: number of customers / number of items sold by item. (B) For reservation data: Number of reservation customers. (C) The count value of the human body passage sensor near the installation location. (D) Open / close count value of the door near the installation location. In addition, any time-series data can be used as long as it is an index showing the busyness of the application location.

(2) Time series history of measured value / set value of control device (a) Measured value of control target medium of control device. For example, room temperature for an air conditioner, interior temperature for a refrigerator, hot water temperature for a water heater, etc. (B) Operation history of the control device. For example, in the case of an air conditioner / refrigerator / water heater, the set temperature and its input time.

(3) Meteorological data of the corresponding area (a) Data of actual meteorological conditions (clear, cloudy, rain, etc.) encoded and time-historyed. (B) Time-series history of actual outside temperatures. (C) The weather condition (sunny, cloudy, rain, etc.) of the weather forecast is coded and displayed in time series. (D) A time series showing the outside temperature in the weather forecast.

  The above data is stored in a database for each device, and based on this, the most effective data analysis method is determined by statistical analysis of multivariate analysis, and this analysis method generates forecast data for the degree of busyness of the desired date and time. Combine this with the relevant weather forecast data to simulate the operation results of the control device and generate an optimal device control plan.

  This system consists of target data accumulation and statistical analysis (management server), and target device control operation / operation history acquisition / control device control target medium measurement value acquisition part (control server = gateway server). It consists of two basic functions.

  These two basic functions can be constructed on the LAN (local area network) where the target device is installed, or the functions for storing the target data and performing statistical analysis are provided outside the device installation location. It is also possible to realize this by exchanging data with The communication means when the basic function is divided into the device installation location and the outside enables the following. (B) Point-to-point communication using public telephone lines. (B) Communication using the Internet / TCP / IP protocol.

  The following data analysis is realized by accumulating the analysis target data in the database in time series. (B) Seasonal interpretation from outside temperature (spring, summer, autumn and winter) (b) Characteristics due to differences in equipment capacity. (C) Changes in operating conditions due to changes over time. (D) Changes in the degree of busyness. As an example, if the device installation location is a restaurant business, grasp the fashion of the store.

  When using a combination of multiple devices to be controlled, for example, air conditioners installed in the same space can be correlated with each other during operation by analyzing simultaneous data for each device. An efficient control plan by the device group is generated.

  Using the time series resolution of the control plan as a parameter, an appropriate value is set according to the type of the target device. For example, a hot water storage type commercial water heater is set at intervals of 3 hours, and an air conditioner is set at intervals of 5 minutes. In addition, by category, select a category that can statistically analyze the same tendency of time-series data and use it.

  If the degree of busyness of the device installation location and the time series history of the measured value / set value of the device can be acquired and the device can be controlled (for example, ON / OFF of driving, strength of driving state, etc.), The type of the control target device is not particularly limited, and efficient operation control is possible for business equipment used in a wide variety of business categories.

Hereinafter, the case of controlling the air conditioner will be described. Factors of comfort in air conditioning harmony include internal factors such as motivation and satisfaction, and external factors such as atmosphere and environmental factors (temperature, humidity, airflow). Controls factors (temperature, airflow). And the environmental factor is changed positively with the intention of the following sensory organ reaction.
(B) Psychological reaction (subjective data): for example, temperature of sensation, mental sweating (physiological reaction), subjective evaluation, etc.
(B) Physiological response (physiological data): body temperature, heart rate, muscle activity, sweating, blood pressure, metabolism, etc.

  Actively intervene in the temperature of the psychological reaction for individuals with high metabolism. For individuals with low metabolism, adjust environmental factors to physiological responses appropriately (such as absorbing differences between men with high metabolism / women with low metabolism).

  In order to realize the control of the sensory temperature of the psychological reaction, the change of the sensory temperature is realized by the generation of airflow changes during cooling. As a method for changing the airflow, for example, the set temperature of the air conditioner is changed. There are methods such as changing the air volume of the air conditioner.

  Next, set temperature control in the system of the present invention will be described. The conventional temperature control of the air conditioner independent control senses the difference between the target temperature and the inlet temperature sensor value, and controls the thermo ON / OFF within a range of plus or minus 1 degree. Therefore, the temperature change is small and the effective temperature difference is not realized in this system.

  On the other hand, the preset temperature control in the system of the present invention predicts the suction port temperature sensor value after 5 minutes in advance, and the set of 2 frames of the last 10 minutes (high temperature 5 minutes, low temperature 5 minutes) at this point. An appropriate set temperature is estimated in advance and is controlled to the set temperature. During the latest 10 minutes, aggressive control of the set temperature change (average 2 ° C. width) is performed for 5 minutes at high temperature and 5 minutes at low temperature. By performing control that realizes the temperature change width at short-term intervals, the subjective evaluation is maximized. During cooling, it induces a physiological cycle of sweating → evaporation, leading to a comfortable cool feeling.

  Next, control of the amount of blown air will be described. In the case of conventional air conditioner self-sustained control, the value initially set manually is continued. In this system, the strongest designation of the air volume is performed when the operating state is blowing. In addition, the strongest air volume is specified when the temperature range is specified as a low temperature by specifying the set temperature change during cooling. By this control, the subjective evaluation of airflow change is maximally induced. During heating, since it feels uncomfortable that a change in the flow of warm air directly affects the human body, the amount of blown air is not controlled.

  As for the function to keep the room temperature at the specified temperature state, the control of this system is the same as the thermo ON / OFF for the preset temperature that is the conventional air conditioner independent control method, but the time to bring the room temperature to the specified temperature. The following intention is given to the control of the air flow change of the air, and a satisfactory air conditioning harmonious space is realized even though the physical room temperature is controlled by the eyes compared to the case of the normal control.

・ Energy saving effect purpose:
Since the setting change of the average temperature of 2 ° C. is performed, the operation of the compressor may be stopped when a high temperature is specified, and as a result, intermittent operation of the device is induced. In the peak period of power consumption, when the set temperature is high, a change to the air blowing mode in which the compressor operation is surely stopped can be applied, and the peak cut control by the intermittent operation can be selected. At this time, coordinated control of multiple devices is performed, intermittent operation ON / OFF is alternately performed between devices, temporarily halving the number of operating air conditioners, minimizing the decrease in comfort level, and the entire device Control is also performed to reduce the total power consumption.

・ Comfort level:
It is possible to incorporate a factor for improving the comfort level by controlling the airflow change during the time to bring the room temperature to the specified temperature by controlling the airflow change such as 1 / f fluctuation by the chaos theory. In addition, humans enjoy a cool feeling as the sweat evaporates due to the physiological reaction of the human body. During cooling operation, by setting the time when the indoor temperature rises due to the strength of the air conditioner's set temperature and intermittent cooling operation, it gives a period to encourage sweating, and induces repeated sweating → evaporation by self-sustaining nerves. A sense of coolness.

Time series analysis block classification automatic creation processing function by industry type:
In order to obtain highly accurate simulation results for each business category, a template creation function for the data processing method for each business category is implemented.
FIG. 8 is an explanatory diagram showing a method for dividing an analysis block. First, classification is performed for changes in the outside air temperature state. In other words, the time zone is divided according to the difference in the outside temperature throughout the day in the corresponding area. The daytime from sunrise to sunset is divided into the nighttime zone after sunset when the outside air temperature drops significantly.

  Next, classification is performed according to the degree of busyness (number of customers in POS data and number of items sold by item). In other words, for each combination of outside temperature factor and busyness factor such as lunchtime, temporary closing time, dinnertime business hours, etc. in restaurants, the energy usage correlation by control device is investigated, and a highly accurate time zone Determine the divisions that will yield different correlations.

  In FIG. 8, it is divided into 8 blocks of closing, daytime, lunch, temporary closing, daytime, dinner, nighttime, closing. And for each analysis block of daytime, lunch, temporary suspension, nighttime, dinner, for each element of busyness data (number of sales and customers by item of POS data, in the case of restaurant POS, the number of orders and the corresponding at the time of ordering The number of seats) is subjected to multiple regression analysis, and an optimal multiple correlation equation that is a combination having a large multiple correlation coefficient is selected.

  FIG. 9 is an explanatory diagram showing a block sorting method based on the number of items sold. For each analysis block, a multiple correlation formula is calculated for each sales item. If the correlation is higher than other data, the number of sashimi sales, the number of pots sold, etc. are selected.

  The above is a time-series analysis pattern template for each business type that can accurately calculate the business operation for each business type and the energy usage correlation of the device. It is a pattern that takes into account the business operation status of business. This pattern template is used as an initial pattern for the same industry. An embodiment for controlling an air conditioner in a store will be described below.

  FIG. 1 is a block diagram showing a system configuration of an embodiment of the energy management system of the present invention. The Internet 11 can provide the management server 10 that collects data and creates a control plan, the gateway (control) server 12 that controls air-conditioning equipment and the like based on the control plan in the store 14, and the like, and weather information in any region A known weather information providing system 13 or the like is connected.

  The gateway server 12 is an interface circuit for a known monitoring control network 23 such as LonWorks (registered trademark), a known interface circuit for LAN 41 such as Ethernet (registered trademark), and a known communication line such as RS-232C. An interface circuit is provided. The hardware of the management server 10 and the gateway server 12 can be realized by attaching an interface circuit to a commercially available server as necessary.

  The plurality of air conditioners 22 are connected to the interface (I / F) adapter 20 by a known air conditioner centralized remote control network 24. The gateway server 12 can monitor and control each air conditioner 22 via the monitoring control network 23 and the I / F adapter 20.

  A plurality of refrigerators 31 are connected to the gateway server 12 via a monitoring control bus 32 such as RS-485, a known controller 30, and a communication line 33 such as RS-232C. The gateway server 12 is connected to each refrigerator. 31 can be monitored and controlled.

  Further, a plurality of POSs 40 are connected to the gateway server 12 via the known LAN 41, and the gateway server 12 can collect information such as the number of customers and the number of items sold from each POS 40.

  FIG. 2 is a flowchart showing the contents of the management server process of the present invention. In S10, it is determined whether or not there is a request for control plan information from the gateway server 12. If the determination result is affirmative, the process proceeds to S11. In S11, the control plan data is transmitted to the gateway server 12. In S12, measurement value information such as operation history and temperature is received from the gateway server, and operation history and measurement value data are stored.

  In S13, it is determined whether or not the weather information acquisition time has come. If the determination result is affirmative, the process proceeds to S14. In S14, the weather result data of the area where the store is located is acquired from the well-known weather information providing system 13 and stored as weather result data. In S15, the weather forecast data for the corresponding area is acquired in the same manner. Then, time series data of weather forecast data is created.

  That is, from the acquired weather forecast data, the lowest temperature of the day is 5:00 am, the highest temperature of the day is 2:00 pm, and the lowest temperature of the next day is 5:00 am Based on the interpretation of the time, temperature values in the middle of the time series are calculated every 5 minutes by spline interpolation and stored as weather forecast data.

  In S16, it is determined whether or not the process is the first process on the corresponding day. If the determination result is affirmative, the process proceeds to S17. In S17, the busy degree record data is acquired from the gateway server 12, and stored as busy record data. In S18, busy degree prediction data is generated from past busy record data and stored as busy prediction data.

  Here, calculation of the expected value of the busyness value will be described. First, in the case of the POS data, the number of customers per specific time, the number of sales of specific items, and the restaurant POS system, the number of customers per time at the time of ordering, the number of sales of specific items, and sales activities are performed at the time of ordering. In non-existent spaces (city hall reception desks, etc.), accumulating numerical values that correlate with the degree of busyness or temperature of the target space, such as the count value of the human body passage sensor per hour and the number of times of opening and closing the entrance door, are accumulated in advance as history data deep.

  If linkage with a system that can obtain digitized data on the degree of busyness is not possible, some measure to express the degree of busyness (in the case of equipment installation where sales activities are involved, the amount of sales, etc. ) Is manually input from the data entry screen of the gateway server 12, for example, every day.

  The grouping range of the quantification coefficient for the degree of busyness is a grouping of spaces that have an influence on the operation result of the target device, such as a hierarchy, in the same building. (In the case of a merchandise store, when accounting at the POS is for each floor, it is related to the equipment for each sales floor.) Then, data for each installation floor of the target equipment for the past 13 weeks from the target date is acquired. The time series resolution is set to 30 minutes, for example, and the obtained result data is collected.

  Next, a multiple regression analysis of the following elements is performed. In other words, the dependent variable is “the value of busyness between 30 minutes after the target time”, the explanatory variables are “the ISO code of the day of the day” and “the meteorological state code registration”, and from the multiple correlation equation Based on the weather condition of the weather forecast and the day of the week, the predicted value of the busyness value is calculated. When the data accumulation for the past 13 weeks is insufficient and the multiple correlation coefficient of the calculation result is 0.6 or less, the correlation result is obtained only by the busyness value and the weather condition.

  When the multiple correlation coefficient is 0.6 or less, the geometric mean value for each day code and each weather code is calculated for each sampling time zone and used as an expected value. When the reservation system is in operation at a beauty salon or the like, the number of reservation customers at the target date and time is used as it is as a coefficient of busyness. Note that the predicted value every 30 minutes is further decomposed every 5 minutes, and is adjusted to the resolution of data used for correlation calculation thereafter.

  In S19, the data correlation is statistically analyzed for each apparatus by the process described later, and stored as correlation analysis data. In S20, it is determined whether or not the weather forecast has been changed. If the determination result is affirmative, the process proceeds to S21. In S21, control plan data is generated by processing to be described later based on various achievements and predicted data, and stored as control plan data.

  FIG. 3 is a flowchart showing the contents of the gateway server process of the present invention. In S30, it is determined whether it is time to acquire control data. If the determination result is affirmative, the process proceeds to S31. In S31, control plan data is acquired from the management server. In S32, the measured values such as the operation history information and the temperature collected from the air conditioner are transmitted.

  In S33, it is determined whether or not a busy information collection cycle has arrived. If the determination result is affirmative, the process proceeds to S34. In S34, for example, busyness data such as the number of customers per predetermined time and the number of sales of specific items is collected from the POS 40, for example. In S35, the busy degree data is transmitted to the management server (actually, it is transmitted in response to a request from the management server).

  In S36, for example, it is determined whether or not an air conditioner on / off operation has been performed by a store employee. If the determination result is affirmative, the process proceeds to S37 to store on / off information for each device. . In the system of the present invention, only the control target device that is turned on is controlled.

  In S38, for example, it is determined whether or not an operation of changing the set values of the air conditioner, such as the set temperature and the set air volume, has been performed by a store employee. If the determination result is affirmative, the process proceeds to S39. In S39, the control plan is corrected by giving priority to a setting change instruction by manual operation for a certain period (for example, several tens of minutes) after the operation.

  In S40, it is determined whether or not the control cycle of the target device (for example, 5 minutes for an air conditioner) has arrived. If the determination result is affirmative, the flow proceeds to S41. In S41, operation history information is collected from the target device. In S42, for example, measured values of the control target (temperature) are collected from the air conditioner. In S43, the target device is controlled based on the control plan data. The control contents are items that can be set by the remote controller, such as the operation mode, set temperature, and set air volume, and each air conditioner operates autonomously so as to satisfy the set conditions. Thereafter, the process returns to S30 and the process is repeated.

  FIG. 4 is a flowchart showing the contents of the statistical analysis process (S19). In S50, the room temperature, the outside temperature, the set temperature, and the degree of busyness for each device in the past predetermined period (for example, the past 13 weeks), for each time series analysis block, and for each operation state (in the case of an air conditioner, cooling / heating / air blowing) Get the data. In S51, the defective data is removed. That is, in order to increase the correlation accuracy, the data when the load is higher than the capacity of the control device and is not functioning is discarded. For example, data when the air conditioner is operating in extreme heat and the room temperature does not reach the target temperature and the temperature rises conversely even if it is continuously operated at the set temperature.

  In S52, it is determined whether or not the number of data is equal to or greater than the maximum number. If the determination result is affirmative, the process proceeds to S53. In S53, the latest maximum number of data in the time series is acquired. In S54, multiple correlation coefficients are calculated for all combinations of explanatory variable elements. That is, a multiple regression analysis of the following elements is performed.

FIG. 10 is an explanatory diagram showing 15 types of explanatory variables for performing combination inspection.
(1) Dependent variable: (A) Room temperature 5 minutes after the target time.
(2) Explanatory variables: (A) Set temperature from 5 minutes before to the target time. (B) Outside temperature from 5 minutes before the target time. (C) Room temperature from 5 minutes before to the target time. (D) Temperature difference between the outside air temperature and the set temperature from 5 minutes before to the target time. (E) Temperature difference between room temperature and set temperature from 5 minutes before to the target time. (F) Temperature difference between outside temperature and room temperature from 5 minutes before to the target time. (G) Busy degree from 5 minutes before to the target time (number of POS data customers, number of items sold, etc.). (H) The set temperature from the target time until 5 minutes later. (Li) The outside temperature from the target time until 5 minutes later. (Nu) The temperature difference between the outside air temperature and the set temperature 5 minutes after the target time. (L) Temperature difference between the set temperature at the target time and the set temperature after 5 minutes. (Wo) The temperature difference between the outside air temperature at the target time and the outside air temperature after 5 minutes. (W) Busy degree value from 5 minutes after the target time. (F) The difference between the busyness value at the target time and the busyness value after 5 minutes. (Yo) A numerical code showing the weather condition of the day.

A correlation equation that obtains the room temperature of the predicted value five minutes after the target time is tried for all patterns of permutation combinations of the explanatory variables, and the equation that leads to the highest multiple correlation coefficient calculation is adopted.
The multiple regression analysis is well known as described in, for example, Yasuharu Okamoto, “Practical approach to statistical processing by programming, Delphi data analysis” published on March 20, 1998 by CQ Publishing Co., Ltd. Only explained.
In the multiple regression analysis, a multiple regression equation representing a dependent variable is used as follows using n sets of explanatory variables.
[Dependent variable (predicted value)] = [partial regression coefficient 1] × [explanatory variable 1] + [partial regression coefficient 2] × [explanatory variable 2] +... + [Partial regression coefficient n] × [explanatory variable n ] + [Residual (constant)]
Here, paying attention to [residual (constant)], [residual (constant)] = [dependent variable (predicted value)] − ([partial regression coefficient 1] × [explanatory variable 1] + [partial regression coefficient 2] ] × [explanatory variable 2] +... + [Partial regression coefficient n] × [explanatory variable n]), the partial regression coefficient is such that the residual sum of squares is obtained and the residual sum of squares is minimized. 1, partial regression coefficient 2... If a partial regression coefficient n is obtained, a multiple regression equation can be obtained.
When there are a plurality of explanatory variables, multiple regression equations are obtained for possible combinations of variables, compared, and the best combination of variables is examined brute-force. If the dependent variable is set to room temperature 5 minutes after the target time in this system, for example, control with an air conditioner, by performing a round robin inspection, depending on where the air conditioner is installed, Correlation of explanatory variables related to temperature becomes stronger, and when installed near the kitchen of the restaurant industry, correlation of explanatory variables related to the degree of busyness (number of POS data customers, number of items sold, etc.) becomes stronger, and performance changes due to secular changes Can find the best combination of variables unique to each device, such as the correlation between set temperature-related and room temperature-related explanatory variables is strong.
As a selection criterion, a multiple correlation coefficient that is the square root of the ratio of the total variation square sum and the residual sum of squares of the multiple regression equation is examined, and a combination of variables that maximizes the multiple correlation coefficient is adopted. The number of combinations of variables is [2 to the power of p−1] if there are p variables. Since this system targets the 15 explanatory variables described above, 32767 combinations are inspected.

  In S55, the highest correlation coefficient is 0.6 or more and the highest one is selected. In S56, the selected multiple correlation equation is registered for each device and each time series analysis block.

  FIG. 5 is a flowchart showing the contents of the control plan data generation process (S21). The control plan is, for example, a total of 288 frames (one day) from 5:05 am to 5 am the following day, with a processing unit of 5 minutes as one frame in accordance with a general change in outside air temperature. In addition, serial management numbers are assigned to control target devices installed in the same space, and registered in advance in the management master database for each device.

  In S60, a room temperature transition simulation assuming a blowing operation is performed. That is, based on the expected busyness data of the target date and the predicted outside air temperature from the weather forecast data, and for each target device, each time series analysis block, and the optimum multiple correlation formula of the air blow operation state, Expected values are calculated every 5 minutes.

  In S61, it is determined whether or not the expected value of the simulation is equal to or higher than the upper limit temperature. In addition, “upper limit temperature” and “lower limit temperature” are registered in advance as parameters in the management master database for each device. And when the room temperature simulation result at the time of ventilation becomes more than "the upper limit temperature", it is set as a cooling operation target location, and when the room temperature simulation result at the time of ventilation becomes "lower limit temperature" or less, it is set as a heating operation target location.

  In S62, the room temperature after 5 minutes in the cooling operation is predicted every set temperature within a predetermined temperature range. In S63, the set temperature with the smallest absolute value of the error between the assumed cooling temperature and the expected cooling temperature is selected as the set temperature for cooling operation. In S64, it is determined whether the predicted value of the simulation is equal to or lower than the lower limit temperature. If the determination result is affirmative, the process proceeds to S65. In S65, the room temperature after 5 minutes in the heating operation is predicted every set temperature within a predetermined temperature range. In S66, a preset temperature with the smallest absolute value of the error between the assumed heating temperature and the expected heating temperature is selected and set as the preset temperature for the heating operation.

  Therefore, “estimated cooling temperature” and “assumed heating temperature” are registered in advance in the management master database for each device as parameters. The predicted value is the temperature range 18 based on the predicted busy degree data of the target date and time, the predicted outside air temperature transition value from the weather forecast data, the target device, the time series analysis block, and the optimum multiple correlation equation for each cooling / heating operation state. The predicted room temperature at the target time is calculated for each set temperature of 1 ° C. within a temperature range of 0 ° C. to 29 ° C.

In S67, the corresponding device management number is acquired for the fluctuation control determination. In S68, it is determined whether or not the cooling operation is performed. If the determination result is affirmative, the process proceeds to S69. In S69, when the cooling plans are arranged in time series in the same operation state, the fluctuation control process described later is performed. In S70, a peak cut process described later is performed. In S71, air volume determination is performed. That is, the strongest designation of the air volume is performed when the operating state is blowing. In addition, the strongest air volume is specified when the temperature range is specified as a low temperature by specifying the set temperature change during cooling.
In S72, 1 / f fluctuation control processing is performed. In other words, the setting temperature and the air volume are changed so as to change the fluctuation using the 1 / f fluctuation value due to the intermittent chaos by the mounting function. In S73, the control plan data is saved.

  FIG. 6 is a flowchart showing the contents of the fluctuation process (S69). When the cooling plans are arranged in time series in the same operation state, the following processing is performed. In S80, it is determined whether or not the device management number is an odd number. If the determination result is affirmative, the process proceeds to S81. If the determination result is negative, the process proceeds to S84. In S81, it is determined whether or not the number of control frames (count number of minimum time series unit) from the daily base point (for example, 5:05 am) is an odd number. If the determination result is affirmative, the process proceeds to S82. If the result is negative, the process proceeds to S83. In S82, the set temperature is increased by 1 ° C. from the planned control value. In S83, the set temperature is decreased by 1 ° C.

  In S84, it is determined whether or not the number of control frames is an odd number. If the determination result is affirmative, the process proceeds to S85, but if not, the process proceeds to S86. In S85, the set temperature is subtracted by 1 ° C. from the planned control value. In S86, the set temperature is increased by 1 ° C.

  FIG. 7 is a flowchart showing the contents of the peak cut processing (S70). For the demand peak cut intermittent operation control, a temperature value for performing demand control is registered in advance in the management master database for each installation location. Then, when the cooling plans are arranged in time series in the same operation state, the following processing is performed.

  In S90, it is determined whether or not the predicted outside air temperature at the same time exceeds the temperature value for which demand control is performed. If the determination result is affirmative, the process proceeds to S91. In S91, it is determined whether or not the management number is an odd number. If the determination result is affirmative, the process proceeds to S92. If the determination result is negative, the process proceeds to S95.

  In S92, it is determined whether or not the number of control frames is an odd number. If the determination result is affirmative, the process proceeds to S93, but if not, the process proceeds to S94. In S93, the operation state is changed to the air blowing operation. In S94, the set temperature is decreased by 1 ° C.

S95: It is determined whether the number of control frames is an odd number. If the determination result is affirmative, the process proceeds to S96, but if not, the process proceeds to S97. In S96, the set temperature is decreased by 1 ° C. In S97, the operation state is changed to the blowing operation.

Next, an actual application example of multiple regression analysis prediction will be described. When predicting the optimal set temperature value to reach room temperature after 5 minutes by multiple regression analysis with air conditioner control, as described above, the actual value of the data item used for the prediction is extracted from the storage database, A multiple regression analysis is performed to obtain a multiple regression equation. At this time, the combination of explanatory variables having the strongest correlation with the data item to be predicted is determined.
For example, it is assumed that a correlation equation in which four items of room temperature, set temperature, outside air temperature, and number of sales points are selected as the data items of the explanatory variable is optimal. FIG. 11 shows an example of data values of four items (for the latest 13 weeks) selected for the corresponding device. Then, FIG. 12 shows a graph of the result of creating an operation plan with the set temperature value predicted every 5 minutes from the above data and operating the air conditioner. The vertical and horizontal axes of the graph represent temperature or the number of customers and time, respectively.
In FIG. 12, the weather forecast outside temperature 63 is obtained from the weather forecast data in FIG. 2, and the predicted busyness (number of sales = number of customers) 64 is obtained from the busy forecast data in FIG. It is obtained by the processing room temperature transition simulation processing of S60 to S66 of FIG. As a result, a set temperature 61 that is operation plan data is generated. The room temperature 60 represents the actual transition of the room temperature as a result of operation according to this operation plan, and is almost coincident with the expected room temperature. The set temperature is wavy due to the fluctuation process of S69.

Although the embodiments have been described above, the present invention can also be applied to other industries / business conditions as follows.
(1) Optimal control of refrigerators and water heaters In the case of refrigerators and water heaters, since the object is goods and water, it has nothing to do with comfort, but by applying the control method of the present invention, Optimization can be realized. In addition to the number of customers in the store as described above, the equivalent to time-series numerical data showing the degree of busyness, for example, the inventory amount and turnover rate of products for refrigerators, the amount of hot water used for water heaters, etc. Can be adopted.

(2) Optimal control of aeration equipment for wastewater treatment facilities (activated sludge type) at livestock facilities, food factories, and sewage treatment facilities In wastewater treatment, the amount and quality of sewage that flows in varies seasonally and with time. If oxygen is supplied at the maximum capacity of the aeration apparatus during a period when the amount of water is small, excessive aeration occurs and wasteful energy is consumed. Achieving optimal optimization of energy use by performing appropriate operation prediction simulation of the aeration equipment that maintains the appropriateness of the sensor measurement value of DO (Dissolved Oxygen) value from the inflow water volume, inflow water quality, weather forecast, etc. Can do.

(3) Optimal water level control of receiving tanks When pumping up elevated tanks and well water receiving tanks, instead of additional pumping whenever a low water level is detected by the water level sensor in the receiving tank, a peak in daytime usage is obtained from demand prediction simulations. In advance, control is performed to increase the amount of stored water from a low water level to a full water level. Thereby, the time shift to the night electric power time zone and the efficiency improvement by continuous operation of the pump are realized, and the optimization of the energy use can be realized.

It is a block diagram which shows the system configuration | structure of the Example of the energy management system of this invention. It is a flowchart which shows the content of the management server process of this invention. It is a flowchart which shows the content of the gateway server process of this invention. It is a flowchart which shows the content of a statistical analysis process (S19). It is a flowchart which shows the content of a control plan data generation process (S21). It is a flowchart which shows the content of fluctuation processing (S69). It is a flowchart which shows the content of the peak cut process (S70). It is explanatory drawing which shows the division method of an analysis block. It is explanatory drawing which shows the block division method by the number of sales according to item. It is explanatory drawing which shows the example of 15 types of explanatory variables which perform a combination test | inspection. It is explanatory drawing which shows the data value example of 4 items of the latest 13 weeks of an applicable apparatus. It is a graph which shows the result of having created the operation plan with the predicted set temperature value and performing the air conditioner operation.

Explanation of symbols

DESCRIPTION OF SYMBOLS 10 Management server 11 Internet 12 Gateway server 13 Weather information provision system 14 Store 20 I / F adapter 21 Centralized remote control 22 Air conditioner 23 Monitoring control network 24 Air conditioning equipment centralized remote control network

Claims (5)

Management server means for creating a control plan for energy consuming equipment based on at least information obtained from the control server means;
Control server means for collecting information from at least energy consuming equipment and sending it to the management server means for controlling the energy consuming equipment based on the control plan;
An energy consumption management system characterized by comprising: And further comprising communication means for connecting the management server means and the control server means,
2. The energy consumption management system according to claim 1, wherein the management server means periodically transmits plan information for a predetermined period to the control server means. The control server means comprises busy degree data collecting means for collecting data representing the busy degree and sending it to the management server,
The management server means generates busyness prediction means for generating busyness prediction data from at least data indicating the busyness and weather data of the area, and
Control plan generating means for generating a control plan based on the forecast data of the busy degree, the measured value and set value data of the device to be controlled, and weather forecast data;
The energy consumption management system according to claim 1, further comprising:   The said control plan production | generation means is provided with the fluctuation control means which actively performs fluctuation control about a related control object apparatus group, and the peak cut means which reduces energy consumption, without impairing comfort so much. The energy consumption management system according to 1. 2. The energy consumption management system according to claim 1, wherein the control server means performs control only when the control target device is turned on, and changes the control plan only for a certain period based on manual setting change. .