JP2011034484A - Device, system and program for predicting energy-saving effect for building - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide an energy-saving effect prediction device, energy-saving effect prediction system and energy-saving effect prediction program for a building which can predict energy consumption with high accuracy even with only a little input information with high accuracy and can improve motivation for energy-saving. <P>SOLUTION: The energy-saving effect prediction device 100 for a building is configured to be for a building for predicting energy saving amount in a facility provided in a building, and includes: a basic information inputting part 212 for inputting basic information; a real result information inputting part 214 for inputting real result information: a category classifying part 220 for classifying a building to a category; a measures table 230 for associating measures with a category to store an energy-saving rate; a measures inputting part 216 for inputting measure selection; an energy saving amount predicting part 240 for predicting energy saving amount; and an outputting part 250 for outputting the energy saving amount. <P>COPYRIGHT: (C)2011,JPO&INPIT

Description

  The present invention relates to a building energy saving effect prediction device, an energy saving effect prediction system, and an energy saving effect prediction program for predicting an energy saving amount, which is a reduction amount of energy consumption in equipment provided in a building.

  Conventionally, promotion of energy saving (energy saving) has been complained from the viewpoints of conservation of global resources and economic efficiency. For this reason, it is required to reduce the energy consumption of facilities and devices themselves and to reduce the energy consumption in terms of operation.

  The “Law Concerning Rational Use of Energy (hereinafter referred to as the Energy Conservation Law)” enacted to promote such energy conservation was revised in 2008. As a result of this amendment, the duty of energy management (management of energy usage), which had been imposed on large-scale factories and business establishments of a certain size or more, was imposed on a business unit basis. In this way, the scale of energy management has changed from the unit of each facility (unit of business) to the unit of business (enterprise unit), so regulations have been strengthened. It becomes a target.

  In order to manage the energy consumption (consumption) in a building, it is necessary to measure or calculate the energy consumption and grasp this. As a method for calculating energy consumption, for example, Patent Document 1 discloses an energy calculation method, an energy calculation device, an energy calculation system, and the like for calculating consumption energy such as electricity and gas consumed in a building (building). It is disclosed. According to this, the specifications (total floor area, heat source method, air-conditioning method, etc.) and energy consumption of all buildings are stored in a database in advance, and if you enter the specifications and energy consumption of the building to be predicted, Is compared with building data under similar conditions to calculate the calculated energy value. Therefore, it is supposed that the calculated energy value (energy consumption) of a building can be predicted and evaluated with high accuracy.

  Non-Patent Document 1 discloses a basic unit management tool “use integrated version” ESUM / ECCJ (Esam) for calculating energy consumption of a building by simulation. According to this, if data such as the specifications of the building and the number of facilities provided in the building are input, it is possible to calculate the energy consumption in the building without using the actual value of energy consumption. Become.

JP 2003-122815 A

http://www.eccj.or.jp/audit/esumt2/index.html Energy Conservation Center Basic Unit Management Tool website

  However, the energy calculation method described in Patent Document 1 has a problem that the energy consumption cannot be accurately calculated if the amount of building data stored in the database is small (if the number of populations is not sufficient). there were. Although it is not impossible to calculate just because the population is small, in order to obtain accuracy that can withstand practical use, data on several tens to hundreds of buildings as the population is required, and energy management is performed on a per-business basis. It is unsuitable for the use which performs.

  In addition, the basic unit management tool described in Non-Patent Document 1 needs to input the shape, structure and material of a building, facilities such as lighting and air conditioning, weather data, and the like with the details that are almost equal to the design information. For this reason, there has been a problem that the input information to the tool becomes extremely large, and much labor and time are required for the input work. In particular, businesses that have a large number of buildings require a great deal of labor to input information on all buildings, including not only large-scale buildings but also small and medium-sized buildings. is there. Therefore, for operators with large numbers of buildings, it was difficult to say that such a tool would be useful (not efficient).

  Furthermore, the basic unit management tool described in Non-Patent Document 1 has a function for comparing actual values and calculated values, and a function for comparing prediction results of a plurality of buildings, but the number of buildings to be compared is limited to five. Has been. There is also a problem that a program for aggregating the prediction results of a plurality of buildings is not incorporated, and it takes time and effort to manually aggregate the results calculated for each building.

  In addition, in the basic unit management tool described in Non-Patent Document 1, it is possible to calculate the energy consumption before and after performing the energy saving measure by changing some of the enormous and detailed parameters. However, although an operator skilled in this tool can grasp a certain level of feeling, it is difficult for the individual staff resident in each building to grasp the meaning of the parameters. For this reason, it is difficult for individual staff members to listen only to results from skilled operators and to use this tool directly. As a result, it was difficult to consider energy saving measures voluntarily in each building, and it was difficult to improve the motivation for energy saving of individual staff members in the building.

  In view of such problems, the present invention can predict energy consumption with high accuracy even with a small amount of input information, and can improve motivation for energy saving by implementing energy saving measures. It aims at providing the energy-saving effect prediction apparatus of a building, an energy-saving effect prediction system, and an energy-saving effect prediction program.

  In order to solve the above problems, a typical configuration of a building energy saving effect prediction apparatus according to the present invention predicts an energy saving effect of a building that predicts an energy saving amount that is a reduction amount of energy consumption in equipment provided in the building. Basic information input unit for inputting basic information including at least the total floor area of the building, actual information input unit for inputting actual information including energy consumption in facilities provided in the building, and basic information A category classification unit that classifies buildings into categories based on two or more elements selected from the group consisting of energy consumption per floor area, energy consumption fluctuation rate, and energy consumption Associate categories with measures to reduce energy consumption, energy saving rates of energy consumption reduced by implementing measures, and categories From the measure table to be paid, the measure input unit to input measure selection, the energy consumption of the building to be predicted, and the energy saving rate of the selected measure in the building to be predicted, An energy saving amount prediction unit that predicts an energy saving amount and an output unit that outputs the predicted energy saving amount are provided.

  According to the above configuration, the energy saving amount can be calculated by multiplying the energy saving rate for the selected measure by the energy consumption amount of the building. The fluctuation rate of the energy consumption can be obtained by, for example, the degree of fluctuation of the monthly energy consumption. The energy saving rate can be calculated as the ratio of the energy saving amount to the energy consumption when the measure is not implemented, when the amount of energy consumption reduced in the building by implementing the measure is the energy saving amount. . Therefore, the only information that needs to be entered is basic information such as the total floor area of the building, building usage, etc., actual information such as energy consumption in the facility, and selection of measures. Time can be greatly reduced.

  In addition, since measures for energy saving are specifically presented as measures (building operation improvement, operation adjustment, facility renewal), people in the building (hereinafter referred to as users) are specifically It is possible to easily recognize what action should be taken. Thereby, it becomes possible to increase the motivation for the user to save energy. Then, the buildings are classified into categories using the above information, energy intensity, and energy consumption fluctuation rate, and the energy consumption of the building to be predicted and the building where measures are selected as energy saving measures By using the energy saving rate in the category to be used, it is possible to predict the energy saving amount with high accuracy.

  Note that the above measure table may be divided into a measure name table and an effect table, and an energy saving rate (effect) for each of a plurality of categories may be associated with one measure. The measure name table can include the name of the measure and the details of the specific measure. In addition, instead of the energy saving rate, the effect table may store the energy saving basic unit by implementing the measure. The energy saving unit is the amount of energy saved per total floor area.

  The basic information may further include a use of the building, and the category classification unit may use at least the use of the building as an element for classifying the building. According to such a configuration, the building classification by the category classification unit reflects the usage of the building, that is, the energy consumption by the building usage, and the fluctuation of the energy consumption, so that the building can be classified more accurately. It becomes possible. Therefore, for example, even an office building can set a more detailed category.

  When a plurality of measures are stored in the above measure table, and the selection of two or more measures among the plurality of measures is input in the measure input unit, the energy saving amount prediction unit saves energy when all the measures are implemented. It is preferable to correct the energy saving rate when two or more measures are implemented using the energy saving rate at the time of implementation of all the measures, and to predict the energy saving amount of the building to be predicted using the corrected energy saving rate.

  For example, if measures to reduce the energy required for lighting are implemented, the energy consumed by the lighting is reduced and the heat generated from the lighting is reduced, so the energy required for air conditioning (cooling) is naturally reduced. . At this time, if the measures to reduce the energy required for air conditioning are performed at the same time, the energy required for air conditioning has already been reduced by the measures for lighting. Less. In this way, there is energy interaction between measures, so two measures, for example, a measure for reducing the energy required for lighting and a measure for reducing the energy required for air conditioning (cooling) at the same time. Even if it is carried out, the energy saving amount at that time is not necessarily the sum of the energy saving amounts when these measures are carried out alone.

  Therefore, when two or more measures are selected as in the above configuration, the energy saving rate at the time of implementation of all measures, that is, the energy saving rate reflecting all the interactions when all of the plurality of measures are implemented, is used. Use to correct the energy saving rate when two or more measures are implemented. Thereby, the error of the predicted energy saving amount can be reduced, and the prediction accuracy can be increased.

  The above measure table further stores the cost required for implementing the measure in association with the measure, and the energy saving amount prediction unit further increases the cost effectiveness using the predicted energy saving amount and the cost required for the selected measure. The output unit may predict and output the selected measures in order of cost effectiveness.

  According to such a configuration, the user can know the cost-effectiveness for each measure, and can easily recognize which measure is most beneficial. Therefore, it is possible to easily determine the priority when selecting a measure.

  In order to solve the above-mentioned problems, a typical configuration of a building energy-saving effect prediction system according to the present invention includes a plurality of building energy-saving effect prediction devices, and a plurality of energy-saving effect prediction devices are connected via a communication network. , A building energy-saving effect prediction system comprising a collection device that collects data sets including at least basic information, performance information, and energy-saving amount, and the collection device is connected to each of a plurality of energy-saving effect prediction devices to save energy An information receiving unit that receives a data set for each building provided with an effect prediction device, an information aggregating unit that collects the received data set for each building, and transmits the aggregated data set to each of the plurality of energy saving effect prediction devices An energy transmission effect prediction device that transmits a data set of a building in which the energy conservation effect prediction device is provided to the collection device, One collection and terminal communication unit that receives a data set aggregated from the device, and having a comparison unit for comparing energy savings of multiple buildings using the received data set.

  According to the above configuration, each of the energy saving effect prediction devices provided in a plurality of buildings transmits a data set including basic information, performance information, and energy saving amount to the collecting device, and the plurality of buildings aggregated in the collecting device. Are transmitted to each energy saving effect prediction apparatus. Therefore, the user of the energy saving effect prediction apparatus can obtain not only the data set of the building in which he is present but also the data set of other buildings. Therefore, it is possible to objectively compare the amount of energy saved in the building where you are and the amount of energy saved in other buildings, so that the competition principle can be applied to each building, and motivation for energy saving can be improved. .

  In order to solve the above-described problems, a typical configuration of a building energy saving effect prediction program according to the present invention predicts an energy saving effect of a building that predicts an energy saving amount that is a reduction amount of energy consumption in equipment provided in the building. A basic information input unit for inputting basic information including at least the total floor area of the building, and a result information input unit for inputting actual information including energy consumption in equipment provided in the building; A category classification unit that classifies buildings into categories based on two or more elements selected from the group consisting of basic information, energy intensity per unit of floor area, and the rate of change in energy consumption, Measures to reduce energy consumption and energy saving rate of energy consumption reduced by implementing the measures From the measure table that associates and stores the category, the measure input unit that inputs the choice of measure, the energy consumption of the building to be predicted, and the energy saving rate of the selected measure in the building to be predicted, It is made to function as an energy-saving-amount prediction part which estimates the energy-saving amount of the building used as prediction object, and an output part which outputs the estimated energy-saving amount.

  The component corresponding to the technical idea in the building energy saving effect prediction apparatus described above and the description thereof can be applied to the energy saving effect prediction system and the energy saving effect prediction program for the building.

  According to the present invention, even with a small amount of input information, it is possible to predict energy consumption with high accuracy, and by realizing energy saving measures, it is easy to understand energy saving and improve each person's motivation. It is possible to provide a building energy saving effect prediction apparatus, an energy saving effect prediction system, and an energy saving effect prediction program.

It is a block diagram which shows schematic structure of the energy saving effect prediction apparatus of a building concerning this embodiment, and an energy saving effect prediction program. It is a figure explaining the category classification of a building. It is a figure explaining the detail of a measure table. It is a figure which shows the example of the output by an output part. It is a flowchart explaining the outline of the energy-saving effect prediction process of a building. It is a block diagram explaining the data flow of an energy saving effect prediction apparatus. It is a block diagram which shows schematic structure of the energy-saving effect prediction system concerning this embodiment. It is a sequence diagram explaining operation | movement of the prediction apparatus and collection device in the prediction system concerning this embodiment. It is a figure which shows the example of the comparison of the energy-saving effect in a comparison part.

  Hereinafter, preferred embodiments of the present invention will be described in detail with reference to the accompanying drawings. The dimensions, materials, and other specific numerical values shown in the embodiment are merely examples for facilitating understanding of the invention, and do not limit the present invention unless otherwise specified. In the present specification and drawings, elements having substantially the same function and configuration are denoted by the same reference numerals, and redundant description is omitted, and elements not directly related to the present invention are not illustrated. To do.

(Building energy saving effect prediction device and energy saving effect prediction program)
A building energy saving effect prediction apparatus and an energy saving effect prediction program according to the present embodiment will be described. FIG. 1 is a block diagram illustrating a schematic configuration of a building energy saving effect prediction apparatus and an energy saving effect prediction program according to the present embodiment.

  As shown in FIG. 1, a building energy saving effect prediction apparatus 100 (hereinafter simply referred to as a prediction apparatus 100) according to the present embodiment includes an input unit 102, an output unit 104, and a computer 110. In this embodiment, the prediction apparatus 100 is embodied by causing the computer 110 to execute an energy saving effect prediction program 200 (hereinafter simply referred to as a prediction program 200) described later.

  The input unit 102 includes a keyboard and a pointing device such as a mouse. In the present embodiment, the keyboard is exemplified. This allows the user of the prediction device 100 to input information and commands. Information and commands may be input from a remote client computer (not shown) via a network such as the Internet. In this case, the network interface corresponds to the input means 102.

  The output unit 104 is exemplified by a monitor in the present embodiment, and displays various operation screens and information. As the output means 104, in addition to a monitor directly connected to the computer 110, a printer may be used as long as the information display is limited. Further, when accessing the prediction apparatus 100 from a remote location via a network, an operation screen and information can be displayed using a web page.

  The computer 110 has a general configuration, and includes a CPU that performs various processes and operations, a storage medium such as a hard disk that stores programs and data, and a RAM that is an area for executing programs.

  Next, a functional configuration of the prediction device 100 will be described. This is almost equal to the configuration of the prediction program 200 at the same time. As shown in FIG. 1, the prediction program 200 includes an input unit 210, a category classification unit 220, a measure table 230 (measure name table 232, effect table 234), an energy saving amount prediction unit 240, and an output unit 250. Prepare.

  The input unit 210 receives information and commands input by the user via the input unit 102 (information from the user is input). In the present embodiment, the input unit 210 also functions as a basic information input unit 212, a record information input unit 214, and a measure input unit 216.

  The basic information input unit 212 receives basic information including at least the total floor area of the building. Such basic information is broadly classified into building information that is information relating to buildings, equipment information that is information relating to equipment provided in the buildings, and operation information that is information relating to the operation of buildings and equipment. In detail, the building information includes the total floor area, floor space of special rooms, the use of the building, the number of floors, etc., and the facility information includes the transformer capacity, the cooling source capacity, the heating source capacity, and the type of the heat storage system. As the operation information, a cooling period, a cooling time, a cooling set temperature, a heating period, a heating time, a heating set temperature, and the like can be exemplified.

  In the present embodiment, the basic information further includes the use of the building in addition to the total floor area of the building. Then, by using the use of the building and the total floor area for the building classification in the category classification unit 220 described later, it is possible to reflect the fluctuation rate of the energy consumption due to the use of the building.

  The record information input unit 214 receives record information (in this embodiment, monthly energy consumption of the entire building) including at least energy consumption in the equipment provided in the building. This makes it possible to calculate monthly energy intensity and the rate of change in energy consumption. Preferably, the energy consumption is subdivided into, for example, a heat source, air conditioning, lighting, etc. for each energy consumption destination. As a result, the energy intensity can be calculated more accurately.

  The measure input unit 216 receives a measure selected by the user among measures stored in a measure table 230 described later, that is, a measure selection.

  The category classification unit 220 includes basic information (building use, total floor area, etc.), energy consumption per unit of total floor area (value obtained by dividing energy consumption by total floor area), energy consumption The buildings are classified into categories based on two or more elements selected from the group consisting of the rate of change.

  FIG. 2 is a diagram for explaining the category classification of buildings. FIG. 2A is a diagram for explaining details of building category classification, and FIG. 2B is a diagram for explaining classification of energy consumption trends. In the present embodiment, the case where the category classification unit 220 classifies a building into a category based on the use and total floor area (basic information) of the building and the energy consumption tendency will be described as an example.

  As shown in FIG. 2A, the buildings are first classified according to their use, that is, whether the building is a branch office or a sales office. As described above, the use of the building is used as an element for classifying the building, so that the fluctuation of the energy consumption due to the operation mode is reflected, so that the building can be classified more accurately. And the category classification | category part 220 further classifies the building classified by the use based on the total floor area. The large, medium and small sections of the total floor area shown in FIG. 2A can be set as appropriate. In addition, as shown in FIG. 2 (a), the use of the building is classified into two uses of “branch office” and “sales office” in the present embodiment, but the present invention is not limited to this. Other applications can be added.

  Next, the category classification unit 220 further classifies the buildings classified by the total floor area based on the energy consumption tendency, and assigns category numbers. As shown in FIG. 2B, the energy consumption tendency is classified using the energy intensity and the fluctuation rate (result information) of the energy consumption. Specifically, the energy consumption trend is calculated based on the average value of each of the buildings divided by use and total floor area, where the horizontal axis is the rate of change in energy consumption and the vertical axis is the energy consumption rate. It is divided into four quadrants I to IV according to the line segment passing through the (average value). That is, the four quadrants are classified according to the energy consumption tendency of the building. Therefore, for example, if attention is paid to the rate of change on the horizontal axis, buildings corresponding to quadrants I and IV with a large amount of this are buildings with a large demand for air conditioning, and buildings corresponding to quadrants II and III with a small amount of this are other than the demand for air conditioning. It can be inferred that this is a building (such as a server room) where there is a lot of constant demand from the equipment.

  The fluctuation rate of the energy consumption is only required to represent the degree of fluctuation, and can be calculated, for example, as (monthly energy consumption maximum value−monthly energy consumption minimum value) / annual energy consumption. This makes it possible to compare the degree of fluctuation regardless of the size of the building. Further, categorization may be performed using the fluctuation rate of energy intensity instead of energy consumption. In this case, the energy consumption is divided by the total floor area in advance, so the influence of the building scale can be eliminated, and the rate of change can be expressed by the degree of dispersion (standard deviation, average deviation, variance, unbiased variance, etc.) .

  The measure table 230 stores a measure for reducing energy consumption, an energy saving rate of energy consumption reduced by implementing the measure, and a category in association with each other. The energy saving rate is the ratio of the energy saving amount to the energy consumption amount when the measure is not implemented, when the reduction amount of the energy consumption reduced in the building by implementing the measure is the energy saving amount. In this way, by associating the measure and the energy saving rate for each category, it is possible to predict the energy saving amount with high accuracy while reducing the input items.

  In the present embodiment, the measure table 230 includes a measure name table 232 and an effect table 234. FIG. 3 is a diagram for explaining the details of the measure table 230. As shown in FIG. 3, the measure name table 232 stores the measure name and specific measure contents (reference) as details of the measure. Thereby, since the means for energy saving is actualized, when selecting a measure, the contents can be easily grasped. Also, when implementing, the user can easily recognize what action he / she should perform, and the user's motivation for energy saving can be enhanced.

  The measure name table 232 further associates the above measures with classifications such as building operation improvement, operation adjustment, and facility renewal, and the type of equipment (for example, classification of lighting, cooling, etc.) for which the measure is to be implemented. May be stored. Further, the measures may be classified into levels such as level 1 (required implementation item), level 2 (recommended implementation item), level 3 (implementation optional item), etc., and stored in association with the level. In addition, the measure name table 232 may record a flag for determining whether or not the measure is an option.

  In the effect table 234, the energy saving rate is stored for each measure and each category. That is, in association with one measure stored in the measure name table 232, the effect table 234 is associated with a plurality of energy saving rates for each category. This energy saving rate is determined by setting a model building for each category, and dividing the difference in energy consumption before and after implementing the measures for the model building by the energy consumption before implementing the measures. Can be sought. The energy saving amount in the present embodiment can be calculated by multiplying the energy saving rate when the energy saving measure is performed in the model building by the energy consumption amount of the model building.

  The effect table 234 may store an energy saving basic unit by implementing measures instead of the energy saving rate. The energy saving basic unit is the energy saving amount per total floor area, and conversely, when obtaining the energy saving amount, it can be calculated by multiplying the energy saving basic unit by the total floor area.

  In the present embodiment, since the energy saving rate is obtained in advance and stored in the effect table 234 in association with the measure and the category, elements for calculating the energy saving rate in the prediction device 100 and the prediction program 200 or No means are provided. However, the present invention is not limited to this, and when the energy saving rate is obtained in the prediction device 100 and the prediction program 200, an element or means for calculating the energy saving rate may be provided.

  Further, as a means for calculating the energy saving rate, a basic unit management tool described in Non-Patent Document 1 may be used. This is because, as a result of the inventors examining the calculation of the energy saving rate using the basic unit management tool, the energy saving rate for the measure showed the same tendency if it is a building in the same category.

  Referring to FIG. 3 again, in the present embodiment, the effect table 234 further stores the cost required for implementing the measures for each category. Thereby, the energy saving amount prediction unit 240 described later can predict the cost-effectiveness using the cost required for the measure and the energy saving amount reduced by executing the measure.

  The energy saving amount prediction unit 240 predicts the energy saving amount of the building from the energy consumption amount of the building to be predicted and the total value of the energy saving rates of the selected measures in the building. Thereby, it is possible to predict the energy saving amount with high accuracy while the information to be input to the prediction device by the user is an extremely small number of information including only basic information, performance information and measure selection. Therefore, it is possible to greatly reduce labor and time required for input work, and consequently labor costs required for the work.

  Further, in the present embodiment, the energy saving amount prediction unit 240 further predicts cost effectiveness using the predicted energy saving amount and the cost required for the measures stored in the effect table 234. As a result, the user can grasp the cost-effectiveness of each measure, and can easily recognize which measure is most beneficial, so it is easy to determine the priority when selecting the measure. It becomes possible.

  Further, in the present embodiment, the energy saving amount prediction unit 240 calculates when a plurality of measures are stored in the measure table 230, and selection of two or more measures among the plurality of measures is input in the measure input unit 216. The energy saving rate is corrected, and the energy saving amount of the building to be predicted is predicted using the corrected energy saving rate. Thereby, the error of the predicted energy saving amount can be reduced, and the prediction accuracy can be increased.

  Specifically, when two or more measures are selected from among a plurality of measures, there is an energy interaction between the measures, so the energy saving rate when implementing two or more measures is not necessarily two or more measures. It is not the sum of energy saving rates. Therefore, an error occurs in the energy saving rate used for calculating the energy saving amount, and if the energy saving amount is predicted using the energy saving rate in which the error has occurred, the accuracy is reduced. For this reason, when two or more measures are selected among a plurality, it is necessary to correct the energy saving rate.

  For the correction of the above-mentioned energy saving rate, the energy saving rate at the time of implementation of all measures, which is the energy saving rate when all the measures are implemented, is used. Thereby, since an interaction can be reflected in correction | amendment, it becomes possible to raise the precision of correction | amendment. In addition, the energy saving rate at the time of implementation of all measures can be obtained in the same manner as the calculation of the energy saving rate described above.

  In the energy saving rate correction process by the energy saving amount prediction unit 240, first, the sum of the energy saving rates when all the measures are taken individually and the energy saving rate at the time of all the measures when all the measures are taken simultaneously are used. The compression rate is calculated. The compression rate can be calculated from the formula “energy saving rate when all measures are implemented / sum of energy saving rates when all measures are individually performed”. Then, the corrected compression rate can be obtained by multiplying the calculated compression rate by the energy saving rate of two or more selected measures.

  The above correction process will be described in more detail by exemplifying the case where the measures 1, 2 and 3 are implemented in the category 1 building shown in FIG. 3. The measure 1 is a measure for lighting, and the measures 2 and 3 are This is a measure for air conditioning. As shown in FIG. 3, the energy saving rate of measure 1 is 3%, and the energy saving rates of measures 2 and 3 are 2%. Assuming that the energy saving rate at the time of implementation of all the measures when implementing all the measures stored in the measure table 230 is X%, the sum of the energy saving rates when all the measures are performed individually is Y% (X <Y) Then, the compression ratio is X / Y (<1).

  In the above case, if the energy consumption of the lighting is reduced by implementing the measure 1, the energy related to the air conditioning is also reduced, so that energy interaction occurs between the measures. Therefore, the energy saving rate when all of the measures 1, 2 and 3 are implemented is not the sum of 7%, and an error occurs. Therefore, by multiplying the compression rate (X / Y) by the energy saving rate of measures 1, 2 and 3, the corrected energy saving rate of measure 1 (3X / Y%) and the corrected energy saving rate of measure 2 ( 2X / Y%), the corrected energy saving rate of measure 3 (2X / Y%) is required.

  The output unit 250 outputs the energy saving amount predicted by the energy saving amount prediction unit 240 to the output unit 104. Further, it may be output to the storage unit 230 together with the output to the output unit 104, and the storage unit may record and accumulate the integrated result. FIG. 4 is a diagram showing an example of output by the output unit 250. FIG. 4A is a graph showing the energy saving rate for each measure, which is selected by the user by changing the display color of the selected measure. Measures can be identified. For example, as illustrated in FIG. 4A, the output unit 250 can output the predicted energy saving amounts for each measure. In addition, the data may be rearranged in descending order of energy saving amount.

  FIG. 4B is a diagram illustrating an example of output representing cost effectiveness for each measure. In the present embodiment, the output unit 250 can rearrange the selected measures in order of high cost effectiveness and output them to the output unit 104. As a result, the user can recognize at a glance which measure is most beneficial.

  Next, the energy-saving effect prediction process of the building performed by the prediction apparatus 100 demonstrated above is demonstrated. FIG. 5 is a flowchart for explaining an outline of a building energy saving effect prediction process, and FIG. 6 is a block diagram for explaining a data flow of the energy saving effect prediction apparatus. When the energy saving effect of a building is predicted using the prediction apparatus 100 according to the present embodiment, as shown in FIG. 5, first, the basic information input unit 212 receives basic information input by a user using an input unit (S300). ). Next, the record information input unit 214 receives record information input by the user using the input unit (S302). Furthermore, the measure input unit 216 receives selection of a measure input by the user using the input means (S304). The measure input unit 216 acquires a list of measures from the measure name table 232 and generates options. Note that a flag indicating whether or not to make the measure an option can be set in advance by the system administrator.

  That is, as shown in FIG. 6, data that is sequentially input when using the prediction device 100 includes selection of basic information, performance information, and measures. More specifically, it is possible to recalculate by changing the selection of the measure while maintaining the basic information and the result information. On the other hand, as data prepared (stored) in the prediction apparatus 100 in advance, measures as options to be stored in the above-described measure name table 232, energy saving rates and costs stored in the effect table 234, and category classification Etc.

  And the category classification | category part 220 classify | categorizes a building into a category based on two or more elements selected from the group which consists of basic information, an energy basic unit, and the fluctuation rate of energy consumption (S306). Next, the energy saving amount prediction unit 240 acquires the energy saving rate from the effect table 234 in the category corresponding to the building to be predicted of the selected measure (S308).

  The energy saving amount prediction unit 240 determines whether or not the selected measure input to the measure input unit 216 is 2 or more (S310), and when the measure is 2 or more (YES in S310), In this way, the compression rate is calculated (S312). Then, the energy saving amount prediction unit 240 corrects the energy saving rate using the calculated compression rate (S314).

  When the number of selected measures is one (NO in S310) and when the energy saving rate is corrected (S314), the energy saving amount prediction unit 240 displays the energy saving rate (if the number of selected measures is two or more). The energy saving amount of the building is predicted using the corrected energy saving rate) and the energy consumption amount of the building to be predicted (S316).

  Next, the energy saving amount prediction unit 240 acquires the cost required for the selected measure from the effect table 234 (S318), and further predicts the cost effectiveness using the acquired cost and the predicted energy saving amount (S320). ). Then, the output unit 250 rearranges the selected measures by the energy saving amount and the energy saving rate, or rearranges the selected measures in order of cost effectiveness (S322), and outputs them to the output unit 104 (S324). The above process is performed, and the energy saving effect prediction process for the building executed by the prediction device 100 is completed.

  As described above, according to the prediction device and the prediction program according to the present embodiment, the information to be input to the prediction device is only basic information, performance information, and measure selection. It is possible to greatly reduce the labor cost required for time and work. Buildings are categorized into categories using this information, energy intensity, and energy consumption fluctuation rate, and the amount of energy consumed in the building to be predicted and the building where measures are selected as energy-saving measures By using the energy saving rate in the category to be used, it is possible to predict the energy saving amount with high accuracy. In addition, since measures to save energy are embodied as measures, users can easily recognize what actions they should take and motivate users to save energy. Can be increased.

(Energy saving effect prediction system)
Next, the energy saving effect prediction system according to the present embodiment will be described. FIG. 7 is a block diagram illustrating a schematic configuration of the energy saving effect prediction system according to the present embodiment. As shown in FIG. 7, an energy saving effect prediction system 400 (hereinafter referred to as prediction system 400) according to the present embodiment includes a communication network 402 and a plurality of energy saving effect prediction devices 410 (hereinafter referred to as prediction devices 410). And a collecting device 420.

  The communication network 402 connects a plurality of prediction devices 410 and a collection device 420. As the communication network 402, the Internet or the like can be preferably used. The data set transmitted from the prediction device 410 may be collected by the collection device 420 through a data storage medium such as a CD-R.

  The prediction device 410 is a device that predicts an energy saving amount, which is a reduction amount of energy consumption in facilities provided in a building, as with the prediction device 100 described above. In the present embodiment, one prediction device 410 is installed in each of the buildings 404a, 404b, and 404c, and predicts the energy saving amount of the installed buildings. The prediction device 410 includes a control unit 412, a terminal communication unit 414, and a comparison unit 416 in addition to the configuration of the prediction device 100 described above. About the element which overlaps with the prediction apparatus 100, description is abbreviate | omitted by attaching | subjecting the code | symbol same as the element which comprises the prediction apparatus 100. FIG.

  The control unit 412 manages and controls functions of the entire prediction device 410 using a semiconductor integrated circuit including a central processing unit (CPU).

  The terminal communication unit 414 transmits and receives data to and from the collection device 420 described later via the communication network 402. Specifically, a data set including at least the basic information, the performance information, and the energy saving amount of the building 404a, 404b, or 404c in which the prediction device 410 is provided is transmitted to the collection device 420 and is collected in the collection device 420. A plurality of building data sets are received from such a collection device 420. In other words, the data set is a combination of input data and calculated data. Thereby, the user of the prediction device 410 can obtain not only the data set of the building 404a, 404b, or 404c in which the user is present but also the data set of another building.

  The comparison unit 416 compares the energy intensity, energy saving rate, and energy saving rate of a plurality of buildings using a plurality of data sets received by the terminal communication unit. Thereby, the user can objectively compare the energy intensity, energy saving amount and energy saving rate of the building 404a, 404b or 404c in which he / she is present with the energy intensity, energy saving amount and energy saving rate of other buildings. Become. Accordingly, the competition principle can be applied to each building, and the motivation for the user to save energy can be improved.

  The collection device 420 is provided in the building 404c in the present embodiment, and collects data sets from the plurality of prediction devices 410 transmitted via the communication network 402. The collection device 420 includes a control unit 422, an information reception unit 424, an information transmission unit 426, and an information aggregation unit 428.

  The control unit 422 manages and controls the functions of the entire collection device 420 by a semiconductor integrated circuit including a central processing unit (CPU).

  The information receiving unit 424 is connected to each of the plurality of prediction devices 410 and receives a data set for each of the buildings 404a, 404b, or 404c in which the prediction devices 410 are provided. The information transmission unit 426 transmits the data sets of the plurality of buildings 404a, 404b, and 404c aggregated in the information aggregation unit 428 described later to each of the plurality of prediction devices 410.

  In the present embodiment, the information receiving unit 424 and the information transmitting unit 426 are described separately for easy understanding, but a transmitting / receiving unit that transmits and receives a data set may be provided.

  The information aggregating unit 428 aggregates the data sets for each of the buildings 404a, 404b, and 404c received by the information receiving unit 424. Aggregation may wait for all data sets to be aggregated (accumulated) before being sent to each prediction device 410, or each time a data set is received from any prediction device 410, another prediction device. The data set may be transmitted to 410.

  In the present embodiment, a configuration in which one prediction device is provided in each of three buildings and one collection device is provided in one building is illustrated, but the number and arrangement are not limited thereto.

  Next, operations of the prediction device 410 and the collection device 420 in the prediction system 400 described above will be described. FIG. 8 is a sequence diagram illustrating operations of the prediction device 410 and the collection device 420 in the prediction system 400 according to the present embodiment.

  In the prediction system, first, as shown in FIG. 8, the plurality of prediction devices perform energy-saving effect prediction processing for each of the buildings 404a, 404b, or 404c provided with the prediction devices (see S300 to S324: FIG. 5). . And the terminal communication part 414 of the prediction apparatus 410 provided in the building 404a transmits the data set of the building 404a to the collection apparatus 420 (S500). The terminal communication unit 414 of the prediction device 410 provided in the building 404b transmits the data set of the building 404b to the collection device 420 (S502). The terminal communication unit 414 of the prediction device 410 provided in the building 404c transmits the data set of the building 404c to the collection device 420 (S504).

  The collection device 420 receives the data sets of the buildings 404a, 404b, and 404c transmitted from the plurality of prediction devices 410 at the information reception unit 424, and aggregates these data sets at the information aggregation unit 428 (S506). Thereafter, the collection device 420 transmits the aggregated data set to the prediction device 410 provided in the buildings 404a, 404b, and 404c by the information transmission unit 426 (S508).

  The plurality of prediction devices 410 provided in the buildings 404a, 404b, or 404c receive the aggregated data sets transmitted from the collection device at the terminal communication unit, and the comparison unit 416 uses the received data sets to generate a plurality of data sets. The energy intensity, energy saving amount and energy saving rate of the buildings 404a, 404b and 404c are compared (S510).

  FIG. 9 is a diagram illustrating an example of comparison of energy saving effects in the comparison unit 416 and the information aggregation unit 428 of the collection device 420. 9A is a graph showing the energy saving rate for each building, FIG. 9B is a graph showing the energy intensity for each building, and FIG. 9C is a graph showing the energy consumption for each region. It is.

  For example, as illustrated in FIG. 9A, the comparison unit 416 rearranges the buildings in order of increasing energy saving rate (calculated from the energy saving amount) for each building, and the building provided with the prediction device 410 (FIG. 9A ) Can be shown in different display colors or the like. As a result, the user can determine at a glance how much the building (building E) where the user is located belongs to all the buildings. Moreover, as shown in FIG.9 (b), the comparison part 416 may represent not only the energy saving amount but the energy basic unit which is performance information as a graph for every building.

  Further, a plurality of building data sets aggregated in the information aggregating unit 428 of the collection device 420 may be grouped based on a predetermined condition such as a region to which the building belongs, and then output by the output unit 250. Thereby, for example, as shown in FIG. 9C, data for each region (set conditions) can be displayed. Further, other data included in the basic information, for example, data such as building use and scale, may be used for aggregation and rearrangement for display output.

  As described above, according to the prediction system according to the present embodiment, a data set including basic information, performance information, and an energy saving amount is stored in the collection device 420 from each of the energy saving effect prediction devices 410 provided in a plurality of buildings. Data sets of a plurality of buildings that are transmitted and aggregated in the collection device 420 are transmitted to each energy saving effect prediction device 410. Therefore, the user of the prediction apparatus 410 can obtain not only the data set of the building where the user is present but also the data set of the other building, and can compare the building where the user is present with the other building. . As a result, the competition principle can be applied to each building, and the motivation for energy saving can be improved.

  Although the preferred embodiments of the present invention have been described above with reference to the accompanying drawings, it goes without saying that the present invention is not limited to such examples. It will be apparent to those skilled in the art that various changes and modifications can be made within the scope of the claims, and these are naturally within the technical scope of the present invention. Understood.

  In addition, the energy-saving effect prediction process of a building performed by the prediction apparatus of this specification, and the process of the prediction apparatus and collection device in an energy-saving effect prediction system are not necessarily time-sequentially according to the order described as a flowchart or a sequence diagram. There is no need to perform processing, and processing in parallel or by a subroutine may be included.

  INDUSTRIAL APPLICABILITY The present invention can be used as a building energy saving effect prediction device, an energy saving effect prediction system, and an energy saving effect prediction program for predicting an energy saving amount that is a reduction amount of energy consumption in equipment provided in a building.

DESCRIPTION OF SYMBOLS 100 ... Prediction apparatus, 102 ... Input means, 104 ... Output means, 110 ... Computer, 200 ... Prediction program, 210 ... Input part, 212 ... Basic information input part, 214 ... Performance information input part, 216 ... Measure input part, 220 ... Category classification unit 230 ... Measure table 232 ... Measure name table 234 ... Effect table 240 ... Energy savings prediction unit 250 ... Output unit 400 ... Prediction system 402 ... Communication network 404a ... Building 404b ... Building , 404c ... Building, 410 ... Prediction device, 412 ... Control unit, 414 ... Terminal communication unit, 416 ... Comparison unit, 420 ... Collection device, 422 ... Control unit, 424 ... Information reception unit, 426 ... Information transmission unit, 428 ... Information aggregator

Claims (6)

An energy saving effect prediction device for a building that predicts an energy saving amount that is a reduction amount of energy consumption in equipment provided in a building,
A basic information input unit for inputting basic information including at least the total floor area of the building;
A track record information input unit for inputting track record information including at least the energy consumption in the equipment provided in the building;
A category in which the buildings are classified into categories based on two or more elements selected from the group consisting of the basic information, the energy consumption rate that is the energy consumption per total floor area, and the rate of change of the energy consumption. A classification section;
A measure table for associating and storing the measure for reducing the energy consumption, the energy saving rate of the energy consumption reduced by implementing the measure, and the category;
A measure input unit for inputting the selection of the measure;
An energy saving amount prediction unit for predicting the energy saving amount of the building to be predicted from the energy consumption amount of the building to be predicted and the energy saving rate of the selected measure in the building to be predicted;
An output unit for outputting the predicted energy saving amount;
A device for predicting an energy saving effect of a building, comprising: The basic information further includes the use of the building,
The said category classification | category part uses the use of the said building at least as an element which classifies the said building, The energy-saving effect prediction apparatus of the building of Claim 1 characterized by the above-mentioned. When a plurality of the measures are stored in the measure table, and selection of two or more measures among the plurality of measures is input in the measure input unit,
The energy saving amount prediction unit corrects the energy saving rate when the two or more measures are implemented by using the energy saving rate at the time of implementation of all the measures, which is the energy saving rate when the plurality of measures are all implemented, and the correction The building energy-saving effect prediction apparatus according to claim 1, wherein the energy-saving amount of the building to be predicted is predicted using the energy-saving rate. The measure table further stores the cost required to implement the measure in association with the measure,
The energy saving amount prediction unit further predicts cost effectiveness using the predicted energy saving amount and the cost required for the selected measure,
The building output energy saving effect prediction apparatus according to claim 1, wherein the output unit outputs the selected measures arranged in order of high cost effectiveness. A plurality of building energy-saving effect prediction devices according to any one of claims 1 to 4, wherein the basic information, the actual information, and the energy-saving amount are transmitted from the plurality of energy-saving effect prediction devices via a communication network. An energy-saving effect prediction system for a building comprising a collection device that collects a data set including at least
The collector is
An information receiving unit connected to each of the plurality of energy saving effect prediction devices and receiving the data set for each building in which the energy saving effect prediction device is provided;
An information aggregating unit for aggregating the received data sets for each building;
An information transmission unit for transmitting the aggregated data set to each of the plurality of energy saving effect prediction devices;
Have
The energy saving effect prediction device is
A terminal communication unit that transmits the data set of the building provided with the energy saving effect prediction device to the collection device and receives the aggregated data set from the collection device;
A comparison unit that compares energy saving amounts of the plurality of buildings using the received data set;
A system for predicting the energy saving effect of a building, characterized by comprising: An energy conservation effect prediction program for a building that predicts an energy conservation amount that is a reduction amount of energy consumption in equipment provided in a building,
Computer
A basic information input unit for inputting basic information including at least the total floor area of the building;
A track record information input unit for inputting track record information including at least the energy consumption in the equipment provided in the building;
A category in which the buildings are classified into categories based on two or more elements selected from the group consisting of the basic information, the energy consumption rate that is the energy consumption per total floor area, and the rate of change of the energy consumption. A classification section;
A measure table for associating and storing the measure for reducing the energy consumption, the energy saving rate of the energy consumption reduced by implementing the measure, and the category;
A measure input unit for inputting the selection of the measure;
An energy saving amount prediction unit for predicting the energy saving amount of the building to be predicted from the energy consumption amount of the building to be predicted and the energy saving rate of the selected measure in the building to be predicted;
A building energy-saving effect prediction program that functions as an output unit that outputs the predicted energy-saving amount.

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