JP2003344555A - Method and apparatus for estimation of carbon dioxide discharge amount - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To make the CO<SB>2</SB>discharge amount from a general household of tomorrow or the like simply estimable. <P>SOLUTION: Multipliers 12 to 16, secular-change correction devices 18 to 22 and conversion devices 26 to 30 convert a consumption amount of electricity, gas and kerosene in the past into the CO<SB>2</SB>discharge amount, they correct its increase or decrease in every year, and they convert the consumption amount into a consumption amount per person so as to be input to a correlation analyze 32. The analyzer 32 finds a correlation between the discharge amount and sensible temperature data in the same period, and a correlation coefficient between a summer period and periods other than the summer period is stored in a storage device 34. An estimated sensible temperature 36 of tomorrow or the like based on a weather estimation and seasonal data 38 indicating the summer period or the periods other than the summer period are input to a CO<SB>2</SB>-discharge-amount estimation apparatus 40. The estimation apparatus 40 reads out the correlation coefficient for the summer period in the case of June to September and the correlation coefficient for a non-summer period in the case of October to May from a recording device 34. On the basis of the read-out correlation coefficients and the estimated sensible temperature 36, the CO<SB>2</SB>discharge amount caused by the electricity, the gas and the kerosene is calculated so as to output its sum total. A multiplier 42 multiplies an output of the apparatus 40 by the number of household members. <P>COPYRIGHT: (C)2004,JPO

Description

Detailed Description of the Invention

[0001]

FIELD OF THE INVENTION The present invention relates to a method and apparatus for predicting carbon dioxide content.

[0002]

2. Description of the Related Art Carbon dioxide is one of the causes of global warming, and it is necessary to suppress carbon dioxide emissions. As a premise, it is necessary to calculate or predict the carbon dioxide emission amount.

The Ministry of the Environment has decided a method of calculating CO ₂ emissions, which is to calculate the annual CO ₂ emissions of Japan as a whole from the total domestic energy consumption.

[0004] Japanese Unexamined Patent Publication No. 2002-4403 discloses a method of predicting heating / cooling cost and CO2 emission amount based on predicted heating load or cooling load in an individual building. In the conventional example described in this publication, an individual building heating load is obtained from the heat insulation performance (Q value) of each building,
Obtain the cooling load from the information (μ value) of the real solar radiation amount obtained inside the building, and calculate the heating and cooling costs by electricity for the year from the heating and cooling load, the equipment rate and the amount of money per unit energy. The annual CO ₂ emission amount is calculated from the heating and cooling load, the equipment manpower, and the CO ₂ emission coefficient. CO
_2. The specific content of the emission factor is not described.

In an image forming apparatus, a so-called copier, a method of calculating carbon dioxide emission from power consumption and consumption of consumables is described in Japanese Patent Laid-Open No. 2002-6696.

A power consumption indicator having a function of calculating a provisional carbon dioxide emission amount together with an electricity rate is disclosed in Japanese Patent Laid-Open No. 11-
No. 295358.

A method of calculating the carbon dioxide emission amount from the energy consumption amount is described in Japanese Patent Laid-Open No. 2001-183186.

[0008]

[Patent Document 1] Japanese Patent Application Laid-Open No. 2002-440
Conventionally, the actual living conditions have not been taken into consideration at all in the prior art described in Japanese Patent Laid-Open No. It merely determines whether or not CO ₂ emissions are high, as a mutual comparison of the basic performance of individual buildings. It does not motivate actions such as how to reduce CO ₂ emission in daily life.

Another conventional example predicts CO2 emission from the actual consumption of fossil fuels such as petroleum and coal, so to speak, it merely evaluates past behavior.

In the conventional example, the amount of CO ₂ emission is macroscopically grasped, and CO based on the energy consumption behavior in the carbon dioxide emission unit of general households and various business establishments.
₂ Emissions are not calculated or estimated.

In order to reduce the CO2 emission, it is important to make efforts to reduce the CO2 emission in each general household, and for that purpose, it is necessary to present some easy-to-understand index that motivates the suppression of energy consumption. In other words, for example, it is necessary to present an index that can estimate the CO2 emission amount of tomorrow and take action to suppress the CO2 emission amount.

The present invention is applicable to general households and various business establishments.
CO emitted in units of carbon oxide emissions _TwoEasier emissions
A method and device for predicting carbon dioxide emissions that can be predicted
The purpose is to do.

[0013]

A carbon dioxide emission predicting method according to the present invention analyzes a correlation between a carbon dioxide emission in a predetermined period based on energy use and a predetermined meteorological element, and obtains a correlation coefficient. , A weather prediction step for predicting the value of the predetermined weather element at a predetermined future time, and the predetermined coefficient predicted in the weather prediction step for the correlation coefficient obtained in the correlation analysis step. An emission amount calculation step of applying the predicted value of the meteorological element and calculating the carbon dioxide emission amount at the predetermined time.

With this configuration, it is possible to easily predict the amount of carbon dioxide emission from general households such as tomorrow. This allows
We can support efforts to reduce carbon dioxide in ordinary households.

[0015] Preferably, the predetermined meteorological element is a sensible temperature. As a result, the energy consumption can be estimated based on the actual feeling, and the carbon dioxide emission amount can be predicted.

Preferably, the energy use comprises the use of electricity, gas and kerosene. As a result, it is possible to cover the energy consumption that becomes a carbon dioxide emission source in general households.

Preferably, the correlation analysis step extrapolates the carbon dioxide emission amount at the predetermined time from the carbon dioxide emission amount within the predetermined period, and the extrapolated value obtained by the correction step and the predetermined amount. And an analysis step of analyzing the correlation with the meteorological element. Thereby, the carbon dioxide emission amount can be predicted based on the more accurate energy consumption prediction.

Preferably, in the correlation analysis step, one of the extrapolated value obtained in the correction step (18, 20, 22) and the correlation coefficient obtained in the analysis step is set to a value per person. A conversion step (26, 28, 30) for converting is provided. This makes it possible to obtain the final result per desired number of people.

The method according to the present invention further comprises a multiplication step (42) for multiplying the carbon dioxide emission amount calculated by the emission amount calculation step (40) by the number of persons. This makes it possible to calculate the carbon dioxide emission amount according to the number of constituent members.

Preferably, the correlation analysis step is 1
The correlation with a predetermined meteorological element is analyzed for each of a plurality of periods that divide the year, and the correlation coefficient is output. This makes it possible to estimate the carbon dioxide emission according to the time. For example, the plurality of periods that divide the year include a period including summer and a period other than that.

The carbon dioxide emission predicting apparatus according to the present invention comprises a correlation analysis means for analyzing a correlation between a carbon dioxide emission quantity within a predetermined period based on energy use and a predetermined meteorological element, and the correlation analysis means. And a storage unit for storing the obtained correlation coefficient and an emission amount calculation unit for calculating the carbon dioxide emission amount at the predetermined time from the predicted value of the predetermined weather element at the future future time and the correlation coefficient. It is characterized by

With this configuration, it is possible to easily predict the amount of carbon dioxide emission from general households such as tomorrow. This allows
We can support efforts to reduce carbon dioxide in ordinary households.

Preferably, the predetermined meteorological element is a sensible temperature. As a result, the energy consumption can be estimated based on the actual feeling, and the carbon dioxide emission amount can be predicted.

Preferably, the energy use comprises the use of electricity, gas and kerosene. As a result, it is possible to cover the energy consumption that becomes a carbon dioxide emission source in general households.

Preferably, the correlation analysis means extrapolates the carbon dioxide emission amount at the predetermined time from the carbon dioxide emission amount within the predetermined period, and the extrapolated value obtained by the correction means and the predetermined value. And an analysis device for analyzing the correlation with meteorological elements.

Preferably, the correlation analysis means further includes a conversion means for converting one of the extrapolated value obtained by the correction means and the correlation coefficient obtained by the analysis device into a value per person. This makes it possible to obtain the final result per desired number of people.

The apparatus according to the present invention further comprises a multiplier (42) for multiplying the carbon dioxide emission amount output from the emission amount calculating means (40) by the number of persons. This makes it possible to calculate the carbon dioxide emission amount according to the number of constituent members.

Preferably, the correlation analysis means outputs the correlation coefficient for each of a plurality of periods dividing one year. This makes it possible to estimate the carbon dioxide emission according to the time. For example, the plurality of periods that divide the year include a period including summer and a period other than that.

The carbon dioxide emission predicting method according to the present invention comprises a predictive value input step of inputting a predictive value of a predetermined weather element at a predetermined future time, a carbon dioxide emission amount based on energy use, and the predetermined weather element. Correlation coefficient (a
_i , b _i ) from the storage means for storing the correlation coefficient corresponding to the predetermined time, and the predetermined value input in the predicted value input step to the read correlation coefficient. Apply the forecast value of the weather element,
And an emission amount calculation step of calculating the carbon dioxide emission amount at the predetermined time.

With this configuration, it is possible to easily and quickly predict the amount of carbon dioxide emission from general households such as tomorrow. This will support the efforts of households to reduce carbon dioxide.

Preferably, the predetermined meteorological element is a sensible temperature. As a result, the energy consumption can be estimated based on the actual feeling, and the carbon dioxide emission amount can be predicted.

Preferably, the energy use comprises the use of electricity, gas and kerosene. As a result, it is possible to cover the energy consumption that becomes a carbon dioxide emission source in general households.

The method according to the present invention further comprises a multiplication step of multiplying the carbon dioxide emission amount calculated by the emission amount calculating step by the number of persons, and the correlation coefficient (a _i , a _i , b _i ) is a coefficient per person. This makes it possible to calculate the carbon dioxide emission amount according to the number of constituent members.

Preferably, the storage means has correlation coefficients (a _i , b) for each of a plurality of periods that divide one year.
_i ) is stored. This makes it possible to estimate the carbon dioxide emission according to the time. For example, the plurality of periods that divide the year include a period including summer and a period other than that.

The carbon dioxide emission predicting apparatus according to the present invention includes a predictive value input means for inputting a predictive value of a predetermined weather element at a predetermined future time, a carbon dioxide emission amount based on energy use, and the predetermined weather element. Correlation coefficient between (a _i ,
b _i ) and a storage means for storing the correlation coefficient corresponding to the predetermined time from the storage means, and the predetermined weather element input by the predicted value input means to the read correlation coefficient. And an emission amount calculation means for calculating the carbon dioxide emission amount at the predetermined time.

With this configuration, it is possible to easily and quickly predict the amount of carbon dioxide emission from general households such as tomorrow. This will support the efforts of households to reduce carbon dioxide.

Preferably, the predetermined meteorological element is the sensible temperature. As a result, the energy consumption can be estimated based on the actual feeling, and the carbon dioxide emission amount can be predicted.

Preferably, the energy use comprises the use of electricity, gas and kerosene. As a result, it is possible to cover the energy consumption that becomes a carbon dioxide emission source in general households.

The apparatus according to the present invention further comprises a multiplier (42) for multiplying the carbon dioxide emission amount calculated by the emission amount calculating means (40) by the number of persons, and the storage means (3).
The correlation coefficient (a _i , b _i ) stored in 4) is a coefficient per person. This makes it possible to calculate the carbon dioxide emission amount according to the number of constituent members.

Preferably, the storage means stores the correlation coefficient (a _i , b) for each of a plurality of periods dividing one year.
_i ) is stored. This makes it possible to estimate the carbon dioxide emission according to the time. For example, the plurality of periods that divide the year include a period including summer and a period other than that.

[0041]

Embodiments of the present invention will now be described in detail with reference to the drawings.

FIG. 1 shows a schematic block diagram of an apparatus which is an embodiment of the present invention, and FIG. 2 shows the relationship between data flow and processing in this embodiment. The main part of this embodiment is
It is realized by a computer program.

As shown in FIG. 2, the present embodiment has four basic steps of calculation of energy consumption, calculation of CO ₂ emission amount, preparation of a CO ₂ emission amount prediction formula, and prediction of CO ₂ emission amount. To have. The steps of calculating the energy consumption, calculating the CO ₂ emission amount, and creating the CO ₂ emission amount prediction formula analyze the correlation between the weather element (here, the sensible temperature) and the past CO ₂ emission amount from the past data, A CO ₂ emission predicting formula is created from the correlation result. After creating the formula for predicting CO ₂ emissions, use the C
It is only applied to the O ₂ emission prediction formula. Once CO ₂
If an emission prediction formula is created, each of the calculation of energy consumption, the calculation of CO ₂ emission, and the creation of the CO ₂ emission prediction formula will be performed except for the modification of the prediction formula by updating / adding past data. The steps need not be performed.

The operation of the configuration shown in FIG. 1 will be described in detail.
As the basic information, the electricity usage fee, the gas usage fee, the kerosene usage amount, the transition of the number of household personnel, and the meteorological data in the same fixed period in the past are accumulated in the storage device 10. These data are, for example, data from 1990 to 2001.

For example, the amount of electricity, gas and kerosene used is
It consists of data for each household in each region for each month of each year.
Actually, each usage amount of electricity, gas and kerosene is calculated from statistical data of electricity bill, gas bill and kerosene bill per household and their respective charges. Of course, if there is data on the usage statistics, it can be used as it is. For example, regarding Tokyo, the amount of electricity, gas, and kerosene used per household is described on a monthly basis in the Tokyo Metropolitan Livelihood Analysis Survey Report “Tomin no Kurashimuki” published by Tokyo Metropolitan Government.

The transition of the number of household members can be obtained over a longer period of time, but for example, the transition data on an annual basis for Tokyo can be obtained.

The meteorological data are meteorological elements in the present invention, and in this embodiment, they are sensible temperature data. As is well known, the sensible temperature is determined by the temperature, the humidity, the wind speed, the amount of sunshine, the amount of metabolism and the amount of clothing. The average sensible temperature of the day is stored in the storage device 10 as an average of the temperatures per month. In this example, since the data on the amounts of electricity, gas, and kerosene used are on a monthly basis, the sensible temperature data is also an average value on a monthly basis.

The multipliers 12, 14 and 16 are used for the storage device 10.
The amount of electricity used, the amount of gas used, and the amount of kerosene read from are multiplied by the corresponding CO ₂ emission coefficient. The CO ₂ emission coefficient for consumption of electricity, gas and kerosene has already been calculated, and the numerical value may be used.

The aging correction devices 18, 20, 22 extrapolate the output values of the multipliers 12, 14, 16 respectively to calculate the present (in this embodiment, 2002) CO ₂ emission amount. This is to take into account that the consumption of electricity, etc. per household changes year by year. For example, the aging correction device 18 temporarily stores the CO ₂ emission amount of each year and each month output from the multiplier 12, and based on these values, the current CO ₂ emission amount is obtained.
₂ Emissions are calculated by extrapolation. Accordingly, CO ₂ emissions in the past due to the stored electricity in the storage device 10, will have been converted to CO ₂ emissions consumer behavior in the current. Similarly, the aging correction devices 20 and 22 are used to reduce CO ₂ caused by the past gas usage amount and kerosene usage amount.
Emissions are converted to CO ₂ emissions from gas and kerosene in consumer behavior in the current.

The aged correction device 24 extrapolates the transition of the number of household members stored in the storage device 10 and estimates the current number of households (in this embodiment, 2002). Converter 2
6, 28, 30 are the aging correction devices 18, 20, respectively.
The output of 22 is divided by the output of the aging correction device 24 to convert the CO ₂ emission amount per household into the CO ₂ emission amount per person. The outputs of the conversion devices 26, 28 and 30 are applied to the correlation analysis device 32. Further, past sensible temperature data is read from the storage device 10 and applied to the correlation analysis device 32.

The correlation analysis device 32 includes electricity, gas and kerosene.
CO for each month of each year_TwoThe amount of emissions
Correlation with sensible temperature data at the same time, and first-order correlation
Correlation coefficient a_i, B_iTo calculate. For example,
CO in T_TwoWhen the emission amount is y, y = a_i× T + b_i Is approximated by. a₁, B₁Is CO due to power consumption_Two
Correlation coefficient for emission amount, a_Two, B_TwoTurn off the gas
CO due to expenses_TwoCorrelation coefficient for emission amount, a
_Three, B_ThreeIs CO due to kerosene consumption_TwoFor emissions
It is a correlation coefficient.

The correlation coefficients a _i and b _i may be calculated, for example, on a monthly basis, but as a result of a comparative examination of the data for the past 10 years or more, the inventor of the present application has roughly calculated one year to be 6 The correlation with the sensible temperature was significantly different between the summer period from March to September and the other periods from October to May, and it was confirmed that this kind of seasonal classification is sufficient for practical use.

3 to 8 show correlation diagrams between the sensible temperature and the CO ₂ emission amount. 3 to 8, the horizontal axis represents the sensible temperature, and the vertical axis represents the CO ₂ emission amount (Kg per person and per day).
C / person / day). 3 and 4 show the correlation between the CO ₂ emission amount due to power consumption and the sensible temperature, and FIGS. 5 and 6 show the correlation between the CO ₂ emission amount due to gas consumption and the sensible temperature, 7 and 8 show C caused by kerosene consumption.
The correlation between the amount of discharged O ₂ and the sensible temperature is shown. 3, 5 and 7 show the correlation in the summer period from June to September, and FIGS. 4, 6 and 8 show the correlation in the non-summer period from October to May.

For example, when the data of power consumption for the past 10 years is available on a monthly basis, for example, for the summer period, 10 (years) × 4 (months) = 40 CO ₂ emission data are obtained. These CO ₂ emission data will be compared with the sensible temperature at each time.

These results, for example, 2002 in Tokyo
The formula for predicting CO ₂ emissions under the condition of a year was as follows. That is, for the summer period, electricity: y = 0.0145 x (sensible temperature) + 0.1527 gas: y = -0.0136 x (sensible temperature) + 0.635
6 Kerosene: y = -0.0005x (sensed temperature) +0.025
8 Total: y = 0.00040 × (sensed temperature) +0.814
1, and for other periods (October to May), electricity: y = -0.0154 × (sensed temperature) +0.746
3 gas: y = −0.0202 × (sensed temperature) +0.732
0 Kerosene: y = -0.0543x (sensed temperature) +0.389
1 Total: y = −0.0899 × (sensible temperature) +1.867
It was 4. The unit of the CO ₂ emission amount y is kgC / person / day.

The correlation coefficients a _i and b _i (i = 1 to 3) calculated by the correlation analysis device 32 are stored in the storage device 34 for table lookup. Each correlation coefficient a _i , b _i
There are a summer period (June to September) and a non-summer period (October to May) in (i = 1 to 3), and there are 12 coefficients in total.

In this embodiment, as will be described later, 1
Since the CO ₂ emission amount per person is converted into the CO ₂ emission amount per household and output, the number of household members in the year for predicting the CO ₂ emission amount (output of the aged correction device 24) is stored in the storage device 34. Keep it.

Now, it is ready to predict the amount of CO ₂ emission tomorrow, and thereafter, the correlation coefficient a stored in the storage device 32 is based on the predicted temperature prediction by the weather forecast.
_The CO ₂ emission amount is predicted with reference to _i and b _i (i = 1 to 3).

That is, a predictor such as tomorrow based on the weather prediction
Indicates the temperature sensitivity 36 and whether the target date is June-September or other times
CO seasonal data 38_TwoInput to the emission prediction device 40
It CO_TwoThe emission forecasting device 40 follows the seasonal data 38.
In the case of June-September, the correlation coefficient a during the summer period_i, B
_iIf (i = 1 to 3) is from October to May,
Correlation coefficient a_i, B_i(I = 1 to 3) from the recording device 34
read out. Then, the read correlation coefficient a_i, B_i(I
= 1 to 3) and the predicted sensible temperature 36,
CO caused by air, gas and kerosene_TwoCalculate emissions
Put out. That is, y₁= A₁× T_SET+ B₁ y_Two= A_Two× T_SET+ B_Two y_Three= A_Three× T_SET+ B_Three Where y₁Is CO caused by electricity_TwoEmissions, y_TwoIs mo
CO caused by_TwoEmissions, y_ThreeIs CO due to gas
_TwoEmission amount, T_SETIs the predicted sensible temperature. y₁
And y_TwoAnd y_ThreeIs the predicted CO_TwoTotal amount of emissions
become. CO_TwoThe emission prediction device 40 is y₁And y_TwoAnd y
_ThreeOutput the sum of.

Since the output of the CO ₂ emission predicting device 40 is the CO ₂ emission amount per person, in order to convert this into a household unit, the multiplier 42 uses the CO ₂ emission predicting device 4
Multiply the output of 0 by the number of household members. The number of household members to be multiplied here may be the average number of household members in the target year. In that case, the output of the aged correction device 24 is stored in the storage device 34 and read out as necessary. Or, take the latest household size data from another database and use the multiplier 4
You may enter in 2. By inputting the number of people under the virtual family structure to the multiplier, it becomes possible to predict the CO ₂ emission amount in an arbitrary family structure.

In the present embodiment, the correlation analyzer 32 analyzes the correlation with the meteorological data on the CO ₂ emission amount per person, but when the final output is per household, the CO ₂ emission amount per household is calculated. Clearly, the correlation may be analyzed. By analyzing the correlation of CO ₂ emission amount per person, it becomes possible to predict the CO ₂ emission amount according to the number of household members.

Although the embodiment using the correlation between the sensible temperature as the weather element and the CO ₂ emission amount has been described, the temperature and the CO _{2 are used.}
It has been confirmed that there is a correlation with the amount of emissions. Therefore, even if the ambient temperature is used instead of the sensible temperature, the CO ₂ emission amount can be predicted although the accuracy is worse than that in the above-described embodiment. However, the highest temperature is used during the summer period, and the lowest temperature is used during the non-emergency period. This is because cooling is generally used in the summer period and heating is used in the non-summer period. More specifically, average temperature and average humidity, maximum temperature and average humidity, minimum temperature and average humidity, average temperature and average wind speed, maximum temperature and average wind speed, minimum temperature and average humidity, average temperature and average humidity and average wind speed. In any combination of the maximum temperature, the average humidity and the average wind speed, and the minimum temperature, the average humidity and the average wind speed, the correlation with the CO ₂ emissions of electricity, gas and kerosene was confirmed. Meteorological elements are average temperature and average humidity, maximum temperature and average humidity, minimum temperature and average humidity, average temperature and average wind speed, maximum temperature and average wind speed, minimum temperature and average humidity, average temperature and average humidity and average wind speed, maximum Any combination of temperature, average humidity and average wind speed, and minimum temperature, average humidity and average wind speed may be used. Accuracy is improved by considering more factors.

In this embodiment, one year is divided into 6-September period and 10 months.
-Although it was divided into the May period, it is clear that other divisions may be appropriate depending on the region. For example, it is natural that the periods are different between Hokkaido and Okinawa. More generally, the correlation between CO ₂ emissions and meteorological elements is analyzed on a monthly basis, and the correlation coefficients a _i , b _i (i = 1 to 3) on a monthly basis are analyzed.
May be stored in the storage device 34.

Although the CO ₂ emission amount is converted to per person and the correlation with the meteorological data is analyzed, it is obvious that the amount per person may be converted after the correlation analysis with the meteorological data. . That is, the correlation analysis device 32 may be arranged before the conversion devices 26, 28, 30.

By setting the past energy use data stored in the storage device 10 as a store unit, a business unit or a factory unit, it is possible to predict a CO ₂ emission amount in a store unit, a business unit or a factory unit of a similar industry. .

[0066]

As can be easily understood from the above description, according to the present invention, the CO ₂ emission amount from general households such as tomorrow and the day after tomorrow can be estimated very easily. As a result, it becomes possible to motivate CO ₂ emissions in general households and the like, and it becomes easy to achieve the CO ₂ reduction target.

[Brief description of drawings]

FIG. 1 is a schematic block diagram of an embodiment of the present invention.

FIG. 2 is a diagram showing a flow of processing of this embodiment.

FIG. 3 is a diagram showing a correlation between a sensible temperature of a CO ₂ emission amount derived from power consumption and a summer period.

FIG. 4 is a diagram showing a correlation between a sensible temperature of a CO ₂ emission amount derived from power consumption and a non-summer period.

FIG. 5 is a diagram showing the correlation between the sensible temperature of CO ₂ emissions originating from gas consumption and the summer period.

FIG. 6 is a diagram showing a correlation between CO ₂ emission amount originating from gas consumption and a sensible temperature during a non-summer period.

FIG. 7 is a diagram showing a correlation between a sensible temperature of a CO ₂ emission amount derived from kerosene consumption and a summer period.

FIG. 8 is a diagram showing a correlation between CO ₂ emission amount originating from kerosene consumption and a sensible temperature during a non-summer period.

[Explanation of symbols]

10: storage device 12, 14, 16: multiplier 18, 20, 22, 24: aging correction device 26, 28, 30: conversion device 32: correlation analysis device 34: storage device 36: predicted sensible temperature 38: seasonal data 40 : CO ₂ emission predicting device 42: Multiplier

Claims (28)

[Claims] 1. A correlation analysis step (10-3) of analyzing a correlation between a carbon dioxide emission amount within a predetermined period based on energy use and a predetermined meteorological element and outputting a correlation coefficient.
4) and the value (3) of the predetermined meteorological element at a predetermined future time (38).
6) The weather forecasting step (36) and the correlation coefficient obtained in the correlation analysis step (10-34) are applied with the forecasted value of the predetermined weather element forecasted in the weather forecasting step, An emission amount calculation step (40) for calculating the amount of carbon dioxide emission at the predetermined time, the method for predicting carbon dioxide emission. 2. The method for predicting carbon dioxide emissions according to claim 1, wherein the predetermined meteorological element is a sensible temperature. 3. The method for predicting carbon dioxide emissions according to claim 1, wherein the energy use includes use of electricity, gas and kerosene. 4. A correction step (18, 20, 2) in which the correlation analysis step extrapolates a carbon dioxide emission amount in the predetermined period from a carbon dioxide emission amount in the predetermined period.
2) and an analysis step (32) for analyzing the correlation between the extrapolated value obtained by the correction step and the predetermined meteorological element
The method for predicting carbon dioxide emission according to claim 1, comprising: 5. The correlation analysis step further converts one of the extrapolated value obtained by the correction step (18, 20, 22) and the correlation coefficient obtained by the analysis step into a value per person. Conversion step (26,
28, 30), The carbon dioxide emission prediction method according to claim 1. 6. The step (40) of calculating the emission amount
6. The method for predicting carbon dioxide emission according to claim 5, comprising a multiplication step (42) for multiplying the carbon dioxide emission calculated by the method by the number of people. 7. The correlation analysis step analyzes the correlation with a predetermined meteorological element for each of a plurality of periods that divide one year, and outputs the correlation coefficient.
The method for predicting carbon dioxide emissions according to any one of 1. 8. The method for predicting carbon dioxide emission according to claim 7, wherein the plurality of periods delimiting the one year include a period including summer and a period other than the period. 9. Correlation analysis means (10-32) for analyzing the correlation between carbon dioxide emissions within a predetermined period based on energy use and predetermined meteorological elements, and the phase relationship obtained by the correlation analysis means. A storage means (34) for storing the number, and an emission amount calculation means for calculating a carbon dioxide emission amount at the predetermined time from the predicted value (36) of the predetermined meteorological element at the future predetermined time (38) and the correlation coefficient. (40) A carbon dioxide emission predicting device comprising: 10. The carbon dioxide emission predicting device according to claim 9, wherein the predetermined meteorological element is a sensible temperature. 11. The carbon dioxide emission predicting apparatus according to claim 9, wherein the energy use includes use of electricity, gas and kerosene. 12. The correction means (18, 20, 22), wherein the correlation analysis means extrapolates a carbon dioxide emission amount in the predetermined time period from a carbon dioxide emission amount in the predetermined time period,
The carbon dioxide emission prediction device according to claim 9, further comprising an analysis device (32) for analyzing a correlation between the extrapolated value obtained by the correction means and the predetermined meteorological element. 13. The conversion means (26, 28, 26) for the correlation analysis means to convert one of the extrapolated value obtained by the correction means and the correlation coefficient obtained by the analysis device into a value per person. 30) The carbon dioxide emission amount prediction device according to claim 1, which comprises 30). 14. The carbon dioxide emission prediction device according to claim 13, further comprising a multiplier (42) for multiplying the carbon dioxide emission output from the emission calculation means (40) by the number of persons. 15. The carbon dioxide emission predicting apparatus according to claim 9, wherein the correlation analysis unit outputs the correlation coefficient for each of a plurality of periods that divide one year. 16. The carbon dioxide emission prediction device according to claim 15, wherein the plurality of periods that divide the one year include a period including summer and a period other than the period. 17. A phase between a predicted value input step of inputting a predicted value (36) of a predetermined weather element at a predetermined future time (38), a carbon dioxide emission amount based on energy use, and the predetermined weather element. A coefficient reading step (40) for reading out the correlation coefficient corresponding to the predetermined time from the storage means (34) for storing the relation numbers (a _i , b _i ) and the read correlation coefficient A carbon dioxide emission amount, comprising: an emission amount calculation step (40) for applying the estimated value of the predetermined weather element input in the estimated value input step to calculate the carbon dioxide emission amount in the predetermined time period. Prediction method. 18. The carbon dioxide emission predicting method according to claim 17, wherein the predetermined meteorological element is a sensible temperature. 19. The method for predicting carbon dioxide emissions according to claim 17, wherein the energy use includes use of electricity, gas and kerosene. 20. Further, the emission amount calculating step (4)
0) comprising a multiplication step (42) for multiplying the carbon dioxide emission amount calculated by 0) by the number of persons, and the correlation coefficient (a _i , b _i ) stored in the storage means is a coefficient per person Item 17. The method for predicting carbon dioxide emission according to Item 17. 21. The carbon dioxide emission prediction method according to claim 17, wherein the storage means stores the correlation coefficients (a _i , b _i ) for each of a plurality of periods that divide one year. 22. The method for predicting carbon dioxide emission according to claim 17, wherein the plurality of periods that divide the one year include a period including summer and a period other than the period. 23. A predictive value input means for inputting a predictive value (36) of a predetermined weather element at a predetermined future time (38), a carbon dioxide emission amount based on energy use, and a phase between the predetermined weather element. The storage means (34) for storing the relation numbers (a _i , b _i ) and the correlation coefficient corresponding to the predetermined time from the storage means (34) are read out, and the correlation coefficient read out An emission amount calculating means (40) for applying the predicted value (36) of the predetermined meteorological element input by the predicted value input means and calculating the carbon dioxide emission amount at the predetermined time. Carbon emission forecasting device. 24. The carbon dioxide emission predicting device according to claim 23, wherein the predetermined meteorological element is a sensible temperature. 25. The carbon dioxide emission predicting apparatus according to claim 23, wherein the energy use includes use of electricity, gas and kerosene. 26. The phase relation stored in the storage means (34), further comprising a multiplier (42) for multiplying the carbon dioxide emission quantity calculated by the emission quantity calculation means (40) by the number of persons. The carbon dioxide emission predicting device according to claim 23, wherein the number (a _i , b _i ) is a coefficient per person. 27. Correlation coefficients (a _i , b) for each of a plurality of periods that divide one year are stored in the storage means (34).
The carbon dioxide emission predicting apparatus according to claim 23, wherein _i ) is stored. 28. The carbon dioxide emission predicting device according to claim 27, wherein the plurality of periods that divide the one year include a period including summer and a period other than that.