Analysis device

Abstract

To provide a device for easily analyzing the priority of measures having an energy saving effect.SOLUTION: An analysis device 100 for analyzing consumption information including a plurality of parameters related to energy consumed by an analysis target includes a storage unit 102 for storing the plurality of pieces of consumption information, and a classification unit 103 for classifying the consumption information into a plurality of groups on the basis of similarity of a relationship value for each of the plurality of parameters. And an importance level unit 104 configured to calculate an importance level that is a ratio of the parameter in the group, a deviation unit 105 configured to calculate a deviation between an overall average that is an average of the consumption information and a categorized average that is an average of the consumption information in the group, and a priority unit 106 configured to calculate a priority of an energy-saving measure for reducing consumed energy corresponding to the consumption information based on the deviation and the importance level.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to an analytical device. [Background technology]

[0002] In the construction industry, there is growing interest in reducing energy consumption during the operation of buildings. To reduce energy consumption in buildings, it is necessary to analyze energy consumption information, propose measures that contribute to energy reduction, and then implement them.

[0003] Furthermore, when analyzing the energy consumption information of a building, energy conservation measures have been implemented using a method for comprehensively implementing energy management of the building (for example, Patent Document 1). [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Patent No. 6584720 Summary of the Invention [Problem to be solved by the invention]

[0005] When carrying out the above-mentioned analysis, a specialized engineer must carry out the analysis work over a long period of time, which is time-consuming and costly. The present invention has been made to solve the above-mentioned problems, and has an object to provide a device for analyzing the priority of measures that have an energy-saving effect. [Means for solving the problem]

[0006] In order to achieve the above object, the present invention provides the following means. An analysis device according to one aspect of the present invention is an analysis device that analyzes consumption information including a plurality of parameters related to energy consumed by an analysis target, and is characterized by comprising: a memory unit that stores the plurality of pieces of consumption information; a classification unit that classifies the consumption information into a plurality of groups based on the similarity of relational values ​​for each of the plurality of parameters; an importance unit that calculates an importance level, which is the proportion of the parameter within the group, based on the consumption information; a deviation unit that calculates the deviation between an overall average, which is the average of the consumption information, and a classified average, which is the average of the consumption information in the group; and a priority unit that calculates a priority of an energy-saving measure to reduce the energy consumed corresponding to the consumption information, based on the deviation and the importance level.

[0007] According to the analysis device of the first aspect of the present invention, consumption information is categorized into groups based on the similarity of relational values ​​of parameters. The importance of parameters in each categorized group and the average of consumption information within the group are calculated. The deviation between the consumption information within the group and the average of all consumption information is further calculated. The importance of energy-saving measures is extracted based on the importance and the deviation.

[0008] The parameters are information included in the consumption information and are energy consumption items that explain the energy usage status of the building. The energy consumption items are preferably determined in advance.

[0009] The importance indicates the proportion of each parameter in the total of all predetermined parameters for energy consumption items. In the first aspect of the above invention, it is preferable that the memory unit further stores a predetermined evaluation value based on the energy-saving measures and the consumption information, and the priority unit calculates a priority based on the deviation, the importance, and the evaluation value.

[0010] In this way, the priority is calculated based on at least the evaluation value. By calculating the priority based on at least the evaluation value, the priority can be calculated with higher accuracy than if the priority were calculated without being based on the evaluation value.

[0011] In the first aspect of the above invention, it is preferable that the memory unit further stores information regarding the building, and the priority unit calculates the priority of the energy-saving measure based on the deviation, the importance, and the information regarding the building.

[0012] In this way, the priorities of the energy-saving measures are determined based on at least information about the building. The priorities of the energy-saving measures may also be calculated taking into account information other than the energy consumption information, such as facility information.

[0013] The facility information is part of the information about the building, and preferably includes the type of building, the structure of the floor that is part of the building, and information about the equipment installed in the building. In the first aspect of the invention, it is preferable that the analysis device further includes a selection unit that selects the energy-saving measure based on at least the priority.

[0014] In this way, by providing a selection unit, high-priority energy-saving measures can be automatically extracted and implemented. By automatically extracting energy-saving measures, it becomes possible to identify energy-saving measures that are expected to be highly effective without relying on the tacit knowledge of experts. [Effects of the Invention]

[0015] According to the analysis device of the present invention, planning for implementing energy conservation measures, which has traditionally been done manually and requires a lot of time, can now be easily carried out in an extremely short time. [Brief explanation of the drawings]

[0016] [Figure 1] 1 is a block diagram illustrating the configuration of an analyzer according to a first embodiment of the present invention. [Figure 2] 10 is a table showing evaluation values. [Figure 3] 10 is a graph showing importance. [Figure 4] 10 is a graph illustrating the overall average and the categorized average. [Figure 5] 10 is a flowchart illustrating a process for calculating the priority of an analysis device. DETAILED DESCRIPTION OF THE INVENTION

[0017] [First embodiment] An analysis device 100 according to a first embodiment of the present invention will be described with reference to Fig. 1 to Fig. 5. The analysis device 100 of this embodiment is a device that analyzes energy consumption and calculates the priority of energy-saving measures.

[0018] The analysis target in this embodiment is consumption information. Note that in this embodiment, analysis results for multiple buildings may be acquired in advance to confirm which typology group the building to be analyzed belongs to.

[0019] The consumption information preferably represents the amount of energy consumed in the building. The consumption information preferably includes the amount of energy consumed related to predetermined parameters. In this embodiment, the consumption information is preferably obtained by analyzing consumption information for multiple buildings.

[0020] Buildings refer to commercial buildings, office buildings, etc. It is preferable that buildings are equipped with devices that consume energy. Furthermore, it is preferable that buildings are equipped with devices that can measure the energy consumption of the entire building on an hourly basis. It is preferable that buildings are equipped with an energy management system (hereinafter also referred to as "BEMS") that can measure energy consumption in detail.

[0021] The predetermined parameters are basically assumed to be 11 in total: lighting operation time, night lighting use ratio, air conditioning fan power in summer, cold heat use ratio in summer, hot heat use ratio in summer, air conditioning operation time, night air conditioning use ratio, air conditioning fan power in winter, cold heat use ratio in winter, hot heat use ratio in winter, and lighting use ratio. Note that the parameters may be parameters other than those mentioned above, and may be more or less than 11.

[0022] Lighting operating hours refer to the total number of hours that lighting is in operation per year. In other words, lighting operating hours are the amount of time that lighting is expected to be in operation in a year. The nighttime lighting usage rate refers to the percentage of lighting operation time during the night compared to the total lighting operation time. Nighttime preferably refers to the period between 8:00 PM and 8:00 AM. However, nighttime may be a different time period depending on the type of building. Nighttime may also be a time period predetermined by a professional engineer, or may be a different time period depending on the type of building.

[0023] The air conditioning fan power in summer refers to the percentage of the annual power consumption of air conditioning fans that is consumed in summer. In this embodiment, summer is preferably the period from June 1 to September 30. However, summer may also be a time period predetermined by a professional engineer.

[0024] The summer cooling and heat usage ratio indicates the proportion of summer cooling and heat demand in the building's annual cooling and heat demand. The summer heat usage rate indicates the percentage of summer heat demand in the building's annual heat demand. Air conditioning operating hours refer to the cumulative annual operating hours of an air conditioner. In other words, air conditioning operating hours are the predicted hours that an air conditioner is operating in a year.

[0025] The nighttime air conditioning usage rate indicates the percentage of air conditioning operating time during the night in the total daily electricity consumption related to air conditioning. The air conditioning fan power consumption in winter refers to the percentage of the power consumed by air conditioning fans in winter in the total annual power consumption of air conditioning fans. In this embodiment, winter is preferably the period from December 1st to March 31st. Note that winter may also be a time period predetermined by a professional engineer.

[0026] The proportion of cold energy use in winter indicates the proportion of cold energy demand in winter to the building's annual cold energy demand. The proportion of winter heating use indicates the proportion of winter heating demand in the building's annual heating demand. The lighting usage rate is the amount of energy consumed by lighting in the total annual energy consumption.

[0027] The expert engineer is an engineer who is knowledgeable about energy conservation measures. The expert engineer may calculate an evaluation value, which will be described later. The energy-saving measures are measures that have the potential to reduce the amount of energy consumed. In this embodiment, the energy-saving measures are measures that are input in advance by a professional engineer and are expected to have an energy-saving effect.

[0028] The analysis device 100 is a device that analyzes consumption information. The analysis device 100 is an information processing device such as a server that has a CPU (Central Processing Unit), ROM, RAM, an input / output interface, and the like.

[0029] The analysis device 100 is communicably connected to an information processing device that stores consumption information of a building to be analyzed. In this embodiment, the analysis device 100 is connected to the BEMS of the building in a state where consumption information can be communicated.

[0030] The analysis device 100 may acquire consumption information from a BEMS or from a device other than a BEMS. For example, the analysis device 100 may acquire consumption information by manual import, from a BAS (Building Automation System: central monitoring device), or from another company's cloud.

[0031] Manual importing includes methods such as using a portable storage medium to transfer consumption information from a building management device to the analysis device 100, or entering consumption information into the analysis device 100 by typing the consumption information into a keyboard.

[0032] Obtaining consumption information from another company's cloud includes obtaining consumption information for a building managed by a company other than the company that owns the analysis device 100, and includes methods such as obtaining consumption information from a BEMS or BAS owned by another company, or using a portable storage medium to transfer the information from a management device managed by another company to the analysis device 100.

[0033] A BEMS may be provided for each building, or one BEMS may be provided for multiple buildings. In this embodiment, an example in which one BEMS is provided for one building will be described. Also, an example in which the analysis device 100 is connected to the BEMS provided in each building so that consumption information can be communicated will be described.

[0034] The program stored in the storage device such as the ROM described above causes the CPU, ROM, RAM, and input / output interface to cooperate with each other to function as at least an acquisition unit 101, a storage unit 102, a classification unit 103, an importance unit 104, a deviation unit 105, a priority unit 106, and a selection unit 107, as shown in FIG. 1.

[0035] The acquisition unit 101 has a configuration for acquiring consumption information and information relating to a building. In this embodiment, it is preferable to acquire the consumption information and information relating to a building using a BEMS.

[0036] The information about the building preferably includes information about the floors that make up the building to be analyzed and about the energy-consuming equipment installed in the building. Information about the equipment will be described later.

[0037] The storage unit 102 is an information storage medium configured to store various types of information. The various types of information stored preferably include consumption information, information about the building, energy-saving measures, and evaluation values. The storage unit 102 may be a flash memory such as an SD memory card, or may be a recording medium of another type.

[0038] The evaluation value is an evaluation coefficient that is input in advance by a specialist engineer knowledgeable in energy conservation measures, and more specifically, the evaluation value is a coefficient used in calculating the priority, which will be described later. In this embodiment, a method for calculating an evaluation value will be described with reference to Fig. 2. The vertical axis on the left side of Fig. 2 represents energy-saving measures, the horizontal axis on the top represents predetermined parameters, and the vertical axis on the right side represents evaluation values. Preferably, there are 11 predetermined parameters.

[0039] If implementing an energy-saving measure is likely to improve the energy-saving effect related to a parameter, a 1 is entered in the column where that energy-saving measure intersects with that parameter. If the expected energy-saving effect related to a parameter is extremely low, nothing is entered in the column where that energy-saving measure intersects with that parameter. When the above-mentioned work has been completed in all columns where the energy-saving measure intersects with parameters, an evaluation value for each energy-saving measure is calculated.

[0040] The method for calculating the evaluation value will be explained using measure A in Figure 2 as an example. By implementing measure A, 1 is entered in the columns for parameters 1 and 2, which are likely to improve energy-saving effects. Nothing is entered in the columns for parameters 3 to 11, which are unlikely to improve energy-saving effects. After that, 2, the sum of the values ​​on the horizontal axis, is entered in the column for the evaluation value of measure A. The same input is made for measures B and onwards. Note that the evaluation value calculated is a common value for all groups, but different values ​​are calculated for the importance and deviation, which will be described later, for each group.

[0041] The classification unit 103 is configured to classify data into a plurality of groups based on the similarity of relational values ​​for at least each predetermined parameter. Specifically, the classification unit 103 classifies data based on the similarity of the proportion of each parameter that occupies the total of the predetermined parameters. In this embodiment, the classification unit 103 classifies data into four groups. The number of groups to be classified may be more or less than four.

[0042] The importance unit 104 is configured to calculate the proportion of each parameter for each categorized group. In other words, the average proportion of each parameter within a group (hereinafter also referred to as the categorized average) is calculated for each parameter. Specifically, the proportion of each parameter is calculated as shown in FIG. 3.

[0043] Figure 3 is a diagram that helps understand the quantification of the proportions of parameters in one group. The vertical axis is the parameter, and the horizontal axis is the importance, which is a quantification of the parameter's influence. The sum of the importance of all parameters is 1. Note that Figure 3 shows the quantification of the proportions of parameters in one group, and the quantification of the proportions of parameters in other groups is calculated in the same way as in Figure 3.

[0044] The deviation unit 105 has a configuration for calculating the deviation between the overall average and the typified average for each parameter, as shown in Fig. 4. Fig. 4 is a diagram showing the calculation of the deviation between the overall average and the typified average for each parameter in one group. The vertical axis represents the parameter, and the horizontal axis represents the deviation from the median.

[0045] In Figure 4, points P1 to P11 are the overall average for each parameter. Points Q1 to Q11 are the typified average for each parameter. The deviation is the absolute value of the difference between points Pn and Qn.

[0046] The overall average is the average value for each parameter across all consumption information. In other words, it is the average value for each parameter before categorization. The categorization average is the average value for each parameter in consumption information within a categorized group.

[0047] The priority unit 106 is configured to calculate the priority of an energy-saving measure for reducing the energy consumed corresponding to the consumption information based on at least the deviation and the importance. In this embodiment, the priority unit 106 calculates the priority of the energy-saving measure based on the deviation, the importance, and the evaluation value.

[0048] Priority refers to the order in which energy-saving measures are recommended to be implemented. The priority unit 106 calculates a suitability index, which is a function calculated based on the deviation, importance, and evaluation value, for each energy-saving measure. Furthermore, the suitability index is calculated for each group. In this embodiment, the suitability index is a value obtained by multiplying the deviation, importance, and evaluation value. The suitability index may be calculated by a method other than multiplying the deviation, importance, and evaluation value.

[0049] The suitability index for measure A is calculated by multiplying the importance of measure A in group 1, the deviation of measure A in group 1, and the evaluation value of measure A common to groups 1 to 4. The suitability index for each measure is calculated using the same process for measure B and onwards. The suitability index for each measure is also calculated using the same process for groups 2 to 4. The calculated suitability indices are arranged in ascending order to indicate the priority for recommending the implementation of energy-saving measures.

[0050] When calculating the priority without using the evaluation value, a compatibility index, which is a value obtained by multiplying the deviation and the importance, is calculated for each energy-saving measure. Furthermore, the compatibility index is calculated for each group.

[0051] In addition, when measures with the same suitability index value occur, a sub-order can be set arbitrarily by the operator. By setting the sub-order, priorities can be set even for measures with the same suitability index value.

[0052] The selection unit 107 is configured to extract energy-saving measures to be recommended based on the priority. If there are no recommended energy-saving measures, the selection unit 107 does not need to extract any energy-saving measures. Furthermore, if there are multiple recommended energy-saving measures, the selection unit 107 may extract multiple energy-saving measures. In this embodiment, the selection unit 107 extracts energy-saving measures corresponding to priorities that exceed a predetermined threshold. The predetermined threshold may be set for each group or for each energy-saving measure.

[0053] Next, the operation of the analysis device 100 configured as described above will be described with reference to FIG. When the analysis device 100 is started, the acquisition unit 101 performs a process of acquiring consumption information (S1). The acquired consumption information is stored in the storage unit .

[0054] Once the consumption information is stored, the classification unit 103 performs a process of classifying the consumption information into four groups based on the relationship between the parameters (S2). The classified groups are stored in the storage unit 102.

[0055] When the categorized groups are stored, the importance unit 104 calculates the importance for each group, and further calculates the importance for each parameter (S3). The calculated importance is stored in the storage unit 102.

[0056] Furthermore, the deviation unit 105 calculates the deviation between the overall average and the categorized average for each group, and further calculates the deviation for each parameter (S4). The calculated deviations are stored in the storage unit 102.

[0057] After the importance and deviation are stored, the priority unit 106 calculates a priority for each energy-saving measure based on the importance, deviation, and evaluation value, and then performs a process of calculating a priority for each group (S5). The calculated priorities are stored in the storage unit 102.

[0058] Once the priorities are stored, the selection unit 107 performs a process of extracting energy-saving measures to be recommended for each group (S6). The extracted recommended energy-saving measures are stored in the storage unit 102. The stored recommended energy-saving measures may be transmitted to an information terminal managed by the worker. Note that the processes of S5 and S6 may be performed simultaneously.

[0059] The analysis device 100 configured as described above can categorize consumption information into groups based on the similarity of parameter relation values. The importance of parameters within each categorized group and the average of the consumption information within the group can be calculated. The deviation between the consumption information within the group and the average of all the consumption information is further calculated. The importance of energy-saving measures can be easily calculated based on the importance and deviation.

[0060] Furthermore, the storage unit 102 can further store an evaluation value that is predetermined based on the energy saving measures and the consumption information, and the priority unit 106 can calculate a priority based on the deviation, importance, and evaluation value.

[0061] Furthermore, the analysis device 100 may further include a selection unit 107 that extracts the energy-saving measures based on at least the priority. <Modification> The selection unit 107 of the analysis device 100 may be configured to extract energy-saving measures to be recommended for implementation based on the priority and information about the equipment. In this embodiment, the selection unit 107 first extracts energy-saving measures corresponding to priorities that exceed a predetermined threshold. Next, the selection unit 107 extracts feasible energy-saving measures from the energy-saving measures selected based on the information about the equipment.

[0062] Specifically, a case will be described in which a measure to introduce an energy-saving fan belt is selected by the selection unit 107. By replacing a normal fan belt with an energy-saving fan belt, the energy consumption of the air conditioner is reduced.

[0063] On the other hand, if the air conditioning equipment installed in the building is not equipped with a fan belt, the energy-saving effect of the energy-saving fan belt cannot be obtained. In other words, even if the priority exceeds the threshold, if the equipment is not compatible, the energy-saving effect cannot be improved. Furthermore, the system extracts energy-saving measures that satisfy the priority exceeding the threshold and information about the equipment.

[0064] The information about the facility equipment preferably includes information about the amount of energy consumed by the facility equipment and information about the parts that make up the facility equipment. According to the analysis device 100 having the above configuration, the priority is calculated based on at least information relating to the building. By calculating the priority based on at least information relating to the building, the priority of the energy-saving measures can be calculated based on information other than consumption information.

[0065] In this way, the priorities can be calculated based on facility information as well, so that the priorities of energy-saving measures can be calculated with high accuracy. The technical scope of the present invention is not limited to the above-described embodiments, and various modifications can be made without departing from the spirit of the present invention. For example, the present invention is not limited to applications of the above-described embodiments, and may be applied to embodiments in which these embodiments are appropriately combined, and is not particularly limited. [Explanation of symbols]

[0066] 100...Analyzer, 101...Acquisition section, 102...Storage section, 103...Type section, 104...Importance section, 105...Difference section, 106...Priority section, 107...Selection section

Claims

1. An analysis device that analyzes consumption information including a plurality of parameters related to energy consumed by an analysis object, a storage unit that stores a plurality of pieces of consumption information; a classification unit that classifies the consumption information into a plurality of groups based on the similarity of relational values ​​for each of the plurality of parameters; an importance unit that calculates an importance, which is a ratio of the parameter within the group, based on the consumption information; a deviation unit that calculates the deviation between an overall average, which is an average of the consumption information, and a categorized average, which is an average of the consumption information in the group; a priority unit that calculates a priority of an energy-saving measure for reducing the consumed energy corresponding to the consumption information based on the deviation and the importance; An analytical device comprising:

2. the storage unit further stores an evaluation value that is predetermined based on the energy-saving measures and the consumption information; 2. The analysis apparatus according to claim 1, wherein the priority unit calculates the priority based on the deviation, the importance, and the evaluation value.

3. The storage unit further stores information about the building, 2. The analysis device according to claim 1, wherein the priority unit calculates the priority of the energy-saving measure based on the deviation, the importance, and information about the building.

4. 4. The analysis device according to claim 1, further comprising a selection unit that selects the energy-saving measures based on at least the priority.

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