JP2006079426A - Apparatus and method for diagnosing energy consumption - Google Patents

Apparatus and method for diagnosing energy consumption Download PDF

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JP2006079426A
JP2006079426A JP2004263884A JP2004263884A JP2006079426A JP 2006079426 A JP2006079426 A JP 2006079426A JP 2004263884 A JP2004263884 A JP 2004263884A JP 2004263884 A JP2004263884 A JP 2004263884A JP 2006079426 A JP2006079426 A JP 2006079426A
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energy consumption
building
regression equation
data
regression
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Hidekazu Sawada
英一 沢田
Mitsugi Kawamura
貢 河村
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Shimizu Construction Co Ltd
Shimizu Corp
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Shimizu Corp
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Abstract

<P>PROBLEM TO BE SOLVED: To provide an energy consumption diagnosis apparatus capable of reducing necessary costs without requiring much labor and time. <P>SOLUTION: The energy consumption diagnosis apparatus is provided with: a building database in which energy consumption data in a building and attribute data estimated to exert influence on the energy consumption are prestored; a regression expression identification means for setting the energy consumption data as an explained variable by performing regression analysis on the basis of the data stored in the building database and identifying a regression expression using the attribute data estimated to exert influence on the energy consumption as an explanation variable; a regression expression storage means for storing the regression expression identified by the regression expression identification means; an input means for inputting the values of the explained variable and the explanation variable; and an energy consumption diagnosing means for diagnosing the energy consumption of the building to be diagnosed from the regression expression stored in the regression expression storage means and the values inputted from the input means. <P>COPYRIGHT: (C)2006,JPO&NCIPI

Description

本発明は、建物のエネルギー消費量が適正な値であるかを診断するエネルギー消費量診断装置及びエネルギー消費量診断方法に関する。   The present invention relates to an energy consumption diagnosis device and an energy consumption diagnosis method for diagnosing whether an energy consumption amount of a building is an appropriate value.

従来のエネルギー消費量の診断法として、診断する建物にセンサを取り付け、エネルギー使用状況を計測し、得られた計測結果を、類似した建物の使用量と比較し、診断対象の建物のエネルギー使用量が多いか、少ないかを判定するものが知られている。
また、経済的な負担をかけることなく、最適な省エネ処理を行わせることができる省エネルギー診断方法が知られている（例えば、特許文献１参照）。
特開２００２−３１２４５７号公報 As a conventional method of diagnosing energy consumption, a sensor is attached to the building to be diagnosed, the energy usage is measured, and the measurement results obtained are compared with the usage of similar buildings, and the energy usage of the building to be diagnosed What determines whether there are many or few is known.
In addition, an energy saving diagnosis method that can perform an optimum energy saving process without placing an economical burden is known (see, for example, Patent Document 1).
JP 2002-31457 A

しかしながら、特許文献１に示す方法のように、計測機器を使用して、エネルギーの消費量を実測するには費用と時間がかかるという問題がある。また、この実測した計測値を比較するための類似した建物の実測データを予め用意しなければならないが、比較対象とする建物を抽出して、実測データを用意するのは困難であるという問題もある。   However, like the method shown in Patent Document 1, there is a problem that it takes cost and time to actually measure energy consumption using a measuring device. In addition, actual measurement data of similar buildings for comparing the actual measurement values must be prepared in advance, but there is a problem that it is difficult to prepare the measurement data by extracting the buildings to be compared. is there.

本発明は、このような事情に鑑みてなされたもので、手間と時間をかけることなく、必要とする費用も低減することができるエネルギー消費量診断装置及びエネルギー消費量診断方法を提供することを目的とする。   The present invention has been made in view of such circumstances, and provides an energy consumption diagnosis device and an energy consumption diagnosis method capable of reducing the necessary cost without taking time and effort. Objective.

本発明は、建物内のエネルギー消費量データと、該エネルギー消費量に影響を与えると推測される属性のデータが予め記憶された建物データベースと、前記建物データベースに記憶されている前記データに基づいて、回帰分析を行うことにより前記エネルギー消費量データを被説明変数とし、該エネルギー消費量に影響を与えると推測される属性のデータを説明変数とする回帰式を同定する回帰式同定手段と、前記回帰式同定手段により同定された回帰式を記憶する回帰式記憶手段と、診断対象の建物に関して、前記被説明変数と前記説明変数の値を入力する入力手段と、前記回帰式記憶手段に記憶されている前記回帰式と、前記入力手段から入力された値から診断対象の建物のエネルギー消費量の診断を行うエネルギー消費量診断手段とを備えたことを特徴とする。   The present invention is based on a building database in which energy consumption data in a building, data of attributes presumed to affect the energy consumption are stored in advance, and the data stored in the building database. A regression equation identifying means for identifying a regression equation using the data of an attribute estimated to affect the energy consumption as an explanatory variable by performing the regression analysis as the energy consumption data, Stored in the regression equation storage unit, the regression equation storage unit that stores the regression equation identified by the regression equation identification unit, the input unit that inputs the value of the explained variable and the explanatory variable regarding the building to be diagnosed, and the regression equation storage unit The regression equation and energy consumption diagnosis means for diagnosing the energy consumption of the building to be diagnosed from the value input from the input means. And it said that there were pictures.

本発明は、前記建物データベースには、病院の建物に関するデータが記憶され、少なくとも病院の種別または診療科の種別のデータが含まれることを特徴とする。   The present invention is characterized in that the building database stores data relating to a hospital building and includes at least data of a hospital type or a department type.

本発明は、建物内のエネルギー消費量データと、該エネルギー消費量に影響を与えると推測される属性のデータが予め記憶された建物データベースと、回帰式を同定する回帰式同定手段と、前記回帰式を記憶する回帰式記憶手段と、被説明変数と説明変数の値を入力する入力手段と、診断対象の建物のエネルギー消費量の診断を行うエネルギー消費量診断手段とを備えたエネルギー消費量診断装置において、診断対象の建物のエネルギー消費量を診断するエネルギー消費量診断方法であって、前記回帰式同定手段が、前記建物データベースに記憶されている前記データに基づいて、回帰分析を行うことにより前記エネルギー消費量データを被説明変数とし、該エネルギー消費量に影響を与えると推測される属性のデータを説明変数とする回帰式を同定し、同定された前記回帰式を前記回帰式記憶手段に記憶する過程と、前記入力手段が、診断対象の建物に関して、前記被説明変数と前記説明変数の値を入力する過程と、エネルギー消費量診断手段が、前記回帰式記憶手段に記憶されている前記回帰式と、前記入力された値から診断対象の建物のエネルギー消費量の診断を行う過程とを有することを特徴とする。   The present invention includes a building database in which energy consumption data in a building, data of an attribute presumed to affect the energy consumption are stored in advance, a regression equation identifying means for identifying a regression equation, and the regression Energy consumption diagnosis comprising regression equation storage means for storing an equation, input means for inputting the value of the explained variable and the explanatory variable, and energy consumption diagnosis means for diagnosing the energy consumption of the building to be diagnosed An apparatus for diagnosing energy consumption of a building to be diagnosed in an apparatus, wherein the regression identification means performs a regression analysis based on the data stored in the building database. Regression with the energy consumption data as the explained variable and the attribute data estimated to affect the energy consumption as the explanatory variable And storing the identified regression equation in the regression equation storage unit, the step of inputting the explained variable and the value of the explanatory variable for the building to be diagnosed, and energy The consumption diagnosis unit includes the regression equation stored in the regression equation storage unit and a process of diagnosing the energy consumption of the building to be diagnosed from the input value.

本発明は、前記建物データベースには、病院の建物に関するデータが記憶され、少なくとも病院の種別または診療科の種別のデータが含まれることを特徴とする。   The present invention is characterized in that the building database stores data relating to a hospital building and includes at least data of a hospital type or a department type.

本発明によれば、計測機器等を使用して、エネルギー使用状況を計測する必要がないため、必要とする費用が少なく、時間もかかることなく、診断対象の建物のエネルギー消費量を診断することができるという効果が得られる。また、回帰式を同定し、信頼区間を設定して診断を行うようにしたため、診断結果のあいまいさが少なく、客観的に診断できるとともに、様々な要因を設定し、回帰分析を行うため、高い精度で信頼区間を設定できるという効果が得られる。   According to the present invention, since it is not necessary to measure the energy usage status using a measuring device or the like, the energy consumption of the building to be diagnosed can be diagnosed with less cost and less time. The effect of being able to be obtained. In addition, since the regression equation is identified and the confidence interval is set for diagnosis, the diagnosis result is less ambiguous and can be objectively diagnosed, and various factors are set and regression analysis is performed. The effect that the confidence interval can be set with accuracy is obtained.

以下、本発明の一実施形態によるエネルギー消費量診断装置を図面を参照して説明する。図１は同実施形態の構成を示すブロック図である。この図において、符号１は、使用者がデータ入力するためのキーボードやマウス等で構成される入力部である。符号２は、診断結果等のデータを表示するためのディスプレイ装置等で構成される表示部である。符号３は、建物内のエネルギー消費量データと、このエネルギー消費量に影響を与えると推測される属性のデータが記憶される建物データベースである。符号４は、建物データベース３を構築するデータベース構築部である。符号５は、建物データベース３に記憶されているデータを読み出し、読み出したデータの回帰分析を行い、回帰式を同定するデータ解析部である。符号６は、データ解析部５において同定した回帰式を記憶する回帰式記憶部である。符号７は、入力部１から入力される建物に関する属性データと回帰式記憶部６に記憶されている回帰式とに基づいて、診断対象の建物のエネルギー消費量の診断を行うエネルギー消費量診断部である。   Hereinafter, an energy consumption diagnosis apparatus according to an embodiment of the present invention will be described with reference to the drawings. FIG. 1 is a block diagram showing the configuration of the embodiment. In this figure, reference numeral 1 denotes an input unit composed of a keyboard, a mouse and the like for a user to input data. Reference numeral 2 denotes a display unit including a display device for displaying data such as diagnosis results. Reference numeral 3 is a building database in which energy consumption data in a building and data of an attribute estimated to affect the energy consumption are stored. Reference numeral 4 denotes a database construction unit for constructing the building database 3. Reference numeral 5 denotes a data analysis unit that reads data stored in the building database 3, performs regression analysis of the read data, and identifies a regression equation. Reference numeral 6 denotes a regression equation storage unit that stores the regression equation identified by the data analysis unit 5. Reference numeral 7 denotes an energy consumption diagnosis unit that diagnoses the energy consumption of the building to be diagnosed based on the attribute data regarding the building input from the input unit 1 and the regression equation stored in the regression equation storage unit 6. It is.

ここで、図４を参照して、データベース構築部４が構築する建物データベース３のテーブル構造を説明する。この例の建物データベース３は、入力部１から入力した病院の建物に関する情報が記憶されており、「地域」、「延床面積」、「病床数」、「年間エネルギー消費量」、「上水使用量」のフィールドを有している。ここでは、６つの項目のデータについて示したが、７項目以上のデータが記憶されるようにしてもよい。例えば、病院種別の下位情報として、診療科の項目を設け、「延床面積」、「病床数」、「年間エネルギー消費量」、「上水使用量」の情報は、病院全体の値と診療科毎の値とに分けて記憶するようにしてもよい。
なお、建物データベース３は、様々な団体が公開したエネルギーデータを使用して構築するようにしてもよい。 Here, the table structure of the building database 3 constructed by the database construction unit 4 will be described with reference to FIG. The building database 3 in this example stores information about the hospital building input from the input unit 1, and includes “region”, “total floor area”, “number of beds”, “annual energy consumption”, “water supply” It has a field “Usage”. Here, six items of data are shown, but data of seven items or more may be stored. For example, clinical department items are provided as subordinate information by hospital type, and the information on “total floor area”, “number of beds”, “annual energy consumption”, and “water consumption” is the value of the entire hospital and You may make it memorize | store and divide into the value for every department.
The building database 3 may be constructed using energy data released by various organizations.

次に、図２を参照して、図１に示すデータ解析部５の動作を説明する。まず、データ解析部５は、建物データベース３に記憶されている建物データを読み込む（ステップＳ１）。続いて、データ解析部５は、変数変換を施した後（ステップＳ２）、回帰分析を行うことにより（ステップＳ３）、エネルギー消費量を被説明変数、エネルギー消費量に影響を与える要因（この例では、延床面積や病院種別など）を説明変数として（ステップＳ４）、回帰式を同定する（ステップＳ５）。ステップＳ２〜Ｓ５の処理においては、公知の回帰分析手法を用いて（１）式を同定すればよいため、回帰式の同定処理の詳細な説明は省略する。この処理によって同定した回帰式を（１）式に示す。データ解析部５は、ここで同定した回帰式（（１）式）を回帰式記憶部６へ記憶する（ステップＳ６）。   Next, the operation of the data analysis unit 5 shown in FIG. 1 will be described with reference to FIG. First, the data analysis unit 5 reads building data stored in the building database 3 (step S1). Subsequently, after performing variable conversion (step S2), the data analysis unit 5 performs regression analysis (step S3), thereby changing the energy consumption to the explained variable and the factor that affects the energy consumption (this example Then, the regression equation is identified (step S5) using the total floor area, hospital type, etc.) as explanatory variables (step S4). In the processing of steps S2 to S5, since the equation (1) may be identified using a known regression analysis method, detailed description of the regression equation identification processing is omitted. The regression equation identified by this processing is shown in equation (1). The data analysis unit 5 stores the regression equation (Equation (1)) identified here in the regression equation storage unit 6 (step S6).

log（一次エネルギー消費量）＝−0.054＋（1.205−0.021×Ｘ１）×log（延床面積）
−0.275（（冷暖房期の消費量／中間期の消費量））・・・・・・（１）
ここに、Ｘ１＝１（精神病院の場合）
Ｘ１＝０（精神病院以外の病院の場合） log (primary energy consumption) = -0.054 + (1.205-0.021 x X1) x log (total floor area)
-0.275 ((Consumption during the heating / cooling period / Consumption during the interim period)) (1)
Here, X1 = 1 (in the case of mental hospital)
X1 = 0 (in hospitals other than mental hospitals)

（１）式で用いられている説明変数は、５％で有意となったものであり、延床面積でだけでなく、病院種別、冷暖房期（冷暖房を使用する期間）の消費量と中間期（冷暖房を使用しない期間）の消費量との比も一次エネルギー消費量に影響を与えている。図５に、（１）式より求めた予測値の９５％信頼区間を示す。   The explanatory variables used in the formula (1) became significant at 5%, not only the total floor area, but also the hospital type, consumption during the heating / cooling period (period during which air conditioning is used) and the intermediate period The ratio with the consumption of (period when air conditioning is not used) also affects the primary energy consumption. FIG. 5 shows a 95% confidence interval of the predicted value obtained from the equation (1).

次に、図３を参照して、診断対象の建物のエネルギー消費量の診断を行う動作を説明する。まず、診断対象の建物に関して、回帰分析で有意となった要因に対応する属性を説明変数として入力部１より入力する（ステップＳ１１）。エネルギー消費量診断部７は、回帰式記憶部６に記憶されている回帰式を読み出し、この回帰式と入力された説明変数とに基づいて、信頼区間を算出する（ステップＳ１２）。そして、エネルギー消費量診断部７は、診断対象の建物のエネルギー消費量の実績値がこの信頼区間内に入るか否かによってこの建物のエネルギー消費量が多いか、少ないかを判定する（ステップＳ１３）。図６に判定する基準を示す。例えば、延床面積２０、０００ｍ２、冷暖房期の消費量／中間期の消費量＝１．４の一般病院の５０％および９５％信頼区間を求めると図７のようになる。仮に、診断対象の建物のエネルギー消費量が１０００００ＧＪの場合、この建物のエネルギー消費量は、「エネルギーを消費しすぎている」と判定する。エネルギー消費量診断部７は、この判定結果を表示部２へ表示する（ステップＳ１４）。 Next, with reference to FIG. 3, the operation | movement which diagnoses the energy consumption of the building for diagnosis is demonstrated. First, regarding the building to be diagnosed, an attribute corresponding to a factor that has become significant in the regression analysis is input from the input unit 1 as an explanatory variable (step S11). The energy consumption diagnosis unit 7 reads the regression equation stored in the regression equation storage unit 6, and calculates a confidence interval based on the regression equation and the input explanatory variable (step S12). Then, the energy consumption diagnosis unit 7 determines whether the energy consumption of this building is large or small depending on whether or not the actual value of the energy consumption of the building to be diagnosed falls within this confidence interval (step S13). ). FIG. 6 shows the criteria for determination. For example, FIG. 7 shows the 50% and 95% confidence intervals for a general hospital with a total floor area of 20,000 m 2 , consumption in the heating / cooling period / consumption in the intermediate period = 1.4. If the energy consumption of the building to be diagnosed is 100,000 GJ, it is determined that the energy consumption of this building is “too much energy is consumed”. The energy consumption diagnosis unit 7 displays the determination result on the display unit 2 (step S14).

このように、計測機器等を使用して、エネルギー使用状況を計測する必要がないため、必要とする費用が少なく、時間もかかることなく、診断対象の建物のエネルギー消費量を診断することができる。また、回帰式を同定し、信頼区間を設定して診断を行うようにしたため、診断結果のあいまいさが少なく、客観的に診断できるとともに、様々な要因を設定し、回帰分析を行うようにしたため、高い精度で信頼区間を設定できる。   In this way, since it is not necessary to measure the energy usage status using a measuring device or the like, it is possible to diagnose the energy consumption of the building to be diagnosed with less cost and less time. . In addition, since the regression equation is identified and the confidence interval is set for diagnosis, the diagnosis result is less ambiguous and can be objectively diagnosed, and various factors are set and regression analysis is performed. The confidence interval can be set with high accuracy.

なお、図１における処理部の機能を実現するためのプログラムをコンピュータ読み取り可能な記録媒体に記録して、この記録媒体に記録されたプログラムをコンピュータシステムに読み込ませ、実行することによりエネルギー消費量診断処理を行ってもよい。なお、ここでいう「コンピュータシステム」とは、ＯＳや周辺機器等のハードウェアを含むものとする。また、「コンピュータシステム」は、ホームページ提供環境（あるいは表示環境）を備えたＷＷＷシステムも含むものとする。また、「コンピュータ読み取り可能な記録媒体」とは、フレキシブルディスク、光磁気ディスク、ＲＯＭ、ＣＤ−ＲＯＭ等の可搬媒体、コンピュータシステムに内蔵されるハードディスク等の記憶装置のことをいう。さらに「コンピュータ読み取り可能な記録媒体」とは、インターネット等のネットワークや電話回線等の通信回線を介してプログラムが送信された場合のサーバやクライアントとなるコンピュータシステム内部の揮発性メモリ（ＲＡＭ）のように、一定時間プログラムを保持しているものも含むものとする。   Note that a program for realizing the functions of the processing unit in FIG. 1 is recorded on a computer-readable recording medium, and the program recorded on the recording medium is read into a computer system and executed to diagnose energy consumption. Processing may be performed. Here, the “computer system” includes an OS and hardware such as peripheral devices. The “computer system” includes a WWW system provided with a homepage providing environment (or display environment). The “computer-readable recording medium” refers to a portable medium such as a flexible disk, a magneto-optical disk, a ROM, and a CD-ROM, and a storage device such as a hard disk built in the computer system. Further, the “computer-readable recording medium” refers to a volatile memory (RAM) in a computer system that becomes a server or a client when a program is transmitted via a network such as the Internet or a communication line such as a telephone line. In addition, those holding programs for a certain period of time are also included.

また、上記プログラムは、このプログラムを記憶装置等に格納したコンピュータシステムから、伝送媒体を介して、あるいは、伝送媒体中の伝送波により他のコンピュータシステムに伝送されてもよい。ここで、プログラムを伝送する「伝送媒体」は、インターネット等のネットワーク（通信網）や電話回線等の通信回線（通信線）のように情報を伝送する機能を有する媒体のことをいう。また、上記プログラムは、前述した機能の一部を実現するためのものであっても良い。さらに、前述した機能をコンピュータシステムにすでに記録されているプログラムとの組み合わせで実現できるもの、いわゆる差分ファイル（差分プログラム）であっても良い。   The program may be transmitted from a computer system storing the program in a storage device or the like to another computer system via a transmission medium or by a transmission wave in the transmission medium. Here, the “transmission medium” for transmitting the program refers to a medium having a function of transmitting information, such as a network (communication network) such as the Internet or a communication line (communication line) such as a telephone line. The program may be for realizing a part of the functions described above. Furthermore, what can implement | achieve the function mentioned above in combination with the program already recorded on the computer system, and what is called a difference file (difference program) may be sufficient.

本発明の一実施形態の構成を示すブロック図である。It is a block diagram which shows the structure of one Embodiment of this invention. 図１に示す装置の動作を示すフローチャートである。It is a flowchart which shows operation | movement of the apparatus shown in FIG. 図１に示す装置の動作を示すフローチャートである。It is a flowchart which shows operation | movement of the apparatus shown in FIG. 図１に示す建物データベース３のテーブル構造を示す図である。It is a figure which shows the table structure of the building database 3 shown in FIG. 予測値の９５％信頼区間の一例を示す図である。It is a figure which shows an example of the 95% confidence interval of a predicted value. 判定基準の一例を示す図である。It is a figure which shows an example of the criterion. 一般病院の５０％および９５％信頼区間のエネルギー消費量の一例を示す図である。It is a figure which shows an example of the energy consumption of the 50% and 95% confidence interval of a general hospital.

符号の説明Explanation of symbols

１・・・入力部、２・・・表示部、３・・・建物データベース、４・・・データベース構築部、５・・・データ解析部、６・・・回帰式記憶部、７・・・エネルギー消費量診断部
DESCRIPTION OF SYMBOLS 1 ... Input part, 2 ... Display part, 3 ... Building database, 4 ... Database construction part, 5 ... Data analysis part, 6 ... Regression type | formula memory | storage part, 7 ... Energy consumption diagnosis department

Claims (4)

建物内のエネルギー消費量データと、該エネルギー消費量に影響を与えると推測される属性のデータが予め記憶された建物データベースと、
前記建物データベースに記憶されている前記データに基づいて、回帰分析を行うことにより前記エネルギー消費量データを被説明変数とし、該エネルギー消費量に影響を与えると推測される属性のデータを説明変数とする回帰式を同定する回帰式同定手段と、
前記回帰式同定手段により同定された回帰式を記憶する回帰式記憶手段と、
診断対象の建物に関して、前記被説明変数と前記説明変数の値を入力する入力手段と、
前記回帰式記憶手段に記憶されている前記回帰式と、前記入力手段から入力された値から診断対象の建物のエネルギー消費量の診断を行うエネルギー消費量診断手段と
を備えたことを特徴とするエネルギー消費量診断装置。 A building database in which energy consumption data in the building and data of attributes presumed to affect the energy consumption are stored in advance;
Based on the data stored in the building database, by performing regression analysis, the energy consumption data is set as an explanatory variable, and attribute data estimated to affect the energy consumption is an explanatory variable. A regression equation identifying means for identifying a regression equation to be
Regression equation storage means for storing the regression equation identified by the regression equation identification means;
For the building to be diagnosed, input means for inputting the explained variable and the value of the explanatory variable;
The regression equation stored in the regression equation storage means, and energy consumption diagnosis means for diagnosing the energy consumption of the building to be diagnosed from the value input from the input means Energy consumption diagnostic device. 前記建物データベースには、病院の建物に関するデータが記憶され、少なくとも病院の種別または診療科の種別のデータが含まれることを特徴とする請求項１に記載のエネルギー消費量診断装置。   2. The energy consumption diagnosis apparatus according to claim 1, wherein the building database stores data related to a hospital building and includes at least data of a hospital type or a department type. 建物内のエネルギー消費量データと、該エネルギー消費量に影響を与えると推測される属性のデータが予め記憶された建物データベースと、回帰式を同定する回帰式同定手段と、前記回帰式を記憶する回帰式記憶手段と、被説明変数と説明変数の値を入力する入力手段と、診断対象の建物のエネルギー消費量の診断を行うエネルギー消費量診断手段とを備えたエネルギー消費量診断装置において、診断対象の建物のエネルギー消費量を診断するエネルギー消費量診断方法であって、
前記回帰式同定手段が、前記建物データベースに記憶されている前記データに基づいて、回帰分析を行うことにより前記エネルギー消費量データを被説明変数とし、該エネルギー消費量に影響を与えると推測される属性のデータを説明変数とする回帰式を同定し、同定された前記回帰式を前記回帰式記憶手段に記憶する過程と、
前記入力手段が、診断対象の建物に関して、前記被説明変数と前記説明変数の値を入力する過程と、
エネルギー消費量診断手段が、前記回帰式記憶手段に記憶されている前記回帰式と、前記入力された値から診断対象の建物のエネルギー消費量の診断を行う過程と
を有することを特徴とするエネルギー消費量診断方法。 A building database in which energy consumption data in a building and attribute data estimated to affect the energy consumption are stored in advance, regression equation identifying means for identifying a regression equation, and the regression equation are stored. Diagnosis in an energy consumption diagnosis device comprising: a regression equation storage means; an input means for inputting an explanatory variable and values of explanatory variables; and an energy consumption diagnosis means for diagnosing the energy consumption of a building to be diagnosed. An energy consumption diagnosis method for diagnosing the energy consumption of a target building,
The regression equation identifying means performs regression analysis based on the data stored in the building database, thereby making the energy consumption data the explained variable, and is assumed to affect the energy consumption. Identifying a regression equation having attribute data as explanatory variables, and storing the identified regression equation in the regression equation storage means;
A process in which the input means inputs the explained variable and the value of the explanatory variable for the building to be diagnosed;
Energy consumption diagnostic means comprises: the regression equation stored in the regression equation storage means; and a process of diagnosing energy consumption of a building to be diagnosed from the input value. Consumption diagnosis method. 前記建物データベースには、病院の建物に関するデータが記憶され、少なくとも病院の種別または診療科の種別のデータが含まれることを特徴とする請求項３に記載のエネルギー消費量診断方法。
4. The energy consumption diagnosis method according to claim 3, wherein the building database stores data relating to a hospital building and includes at least data of a hospital type or a department type.
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