JPH06174285A - Air conditioning heat load prediction method - Google Patents
Air conditioning heat load prediction methodInfo
- Publication number
- JPH06174285A JPH06174285A JP4350176A JP35017692A JPH06174285A JP H06174285 A JPH06174285 A JP H06174285A JP 4350176 A JP4350176 A JP 4350176A JP 35017692 A JP35017692 A JP 35017692A JP H06174285 A JPH06174285 A JP H06174285A
- Authority
- JP
- Japan
- Prior art keywords
- air conditioning
- heat load
- air
- conditioning heat
- conditioning equipment
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Landscapes
- Other Air-Conditioning Systems (AREA)
- Air Conditioning Control Device (AREA)
Abstract
(57)【要約】
【目的】 空調設備の熱負荷を予測し空調機器の運転の
最適化を図る。
【構成】 制御される空調設備の空調熱負荷及びその空
調設備の設置環境の外気条件を所定期間にわたり実測
し、その結果取得された空調熱負荷実測データ及び外気
条件実測データをそれぞれ重回帰分析し、その結果から
その空調設備の設置環境に固有の上記空調熱負荷と上記
外気条件との重相関式を求め、その重相関式に予測した
い予測期間の標準外気条件を適用することにより、その
予測期間にわたる平均空調熱負荷を予測する。この予測
データを利用することにより、空調機器の運転制御の最
適化を図ることができる。
(57) [Summary] [Purpose] To optimize the operation of air conditioning equipment by predicting the heat load of air conditioning equipment. [Structure] The air-conditioning heat load of the air-conditioning equipment to be controlled and the outside air condition of the installation environment of the air-conditioning equipment are actually measured for a predetermined period, and the obtained air-conditioning heat load measurement data and outside air condition measurement data are subjected to multiple regression analysis. From the result, the multiple correlation equation between the above-mentioned air conditioning heat load and the above outside air condition specific to the installation environment of the air conditioning equipment is obtained, and the standard outside air condition of the forecast period to be predicted is applied to that multiple correlation equation, and the prediction is performed. Predict the average air conditioning heat load over the period. By using this prediction data, it is possible to optimize the operation control of the air conditioning equipment.
Description
【０００１】[0001]
【産業上の利用分野】本発明は空調設備の制御方法に関
し、特に当月の運転実績に基づき翌月の空調熱負荷の予
測を行うための空調熱負荷予測方法に関する。BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an air conditioning facility control method, and more particularly to an air conditioning heat load predicting method for predicting the air conditioning heat load for the next month based on the operating results of the current month.
【０００２】[0002]
【従来の技術】従来より、ビル空調には、例えば、水熱
源空調システムのような蓄熱システムが採用されてい
る。かかる蓄熱システムは、周知のように、建物の地階
に熱源水を蓄えるための蓄熱槽を設置し、この蓄熱槽内
の熱源水を各階の空調機、例えばファンコイルユニット
やヒートポンプユニットのコイルや水側熱交換器に通水
して冷暖房を行うものであり、冷温水の製造のための熱
源機器が付設されている。この熱源機器としては、冷水
製造のためには冷凍機、温水製造のためにはボイラやヒ
ートポンプが使用されるのが通常であり、これらの熱源
機器群を稼働させることにより蓄熱運転が行われる。2. Description of the Related Art Conventionally, a heat storage system such as a water heat source air conditioning system has been used for building air conditioning. Such a heat storage system, as is well known, installs a heat storage tank for storing heat source water in the basement of the building, and the heat source water in this heat storage tank is used as an air conditioner on each floor, for example, a coil or water of a fan coil unit or a heat pump unit. Water is passed through the side heat exchanger for heating and cooling, and a heat source device for producing cold and hot water is attached. As this heat source device, a refrigerator is usually used for producing cold water, and a boiler or a heat pump is used for producing hot water, and heat storage operation is performed by operating these heat source device groups.
【０００３】冷房運転の場合を考えてみると、省エネル
ギー・省ランニングコスト運転を実施するためには、安
価な夜間電力を利用して冷凍機を稼働して蓄熱槽に冷水
を蓄え、これを翌日の空調運転当日に使用するのが最も
一般的である。また、運転当日に蓄熱槽内の冷水が不足
した場合には（熱源水温度が上昇すると）、冷房運転と
蓄熱運転（冷水製造運転）とを併用することも行われて
いる。Considering the case of cooling operation, in order to carry out energy-saving and running cost-saving operation, the refrigerator is operated by using inexpensive nighttime electric power to store cold water in the heat storage tank, and the next day It is most commonly used on the day of air conditioning operation. Further, when the cold water in the heat storage tank is insufficient on the day of operation (when the temperature of the heat source water rises), the cooling operation and the heat storage operation (cool water production operation) are also used together.
【０００４】以上のように、省エネルギー・省コスト運
転を行うためには、空調設備の熱源機器の運転条件（熱
源機器群の運転順位、発停時刻等）を最適に制御してや
ることが重要である。これまでは、このような空調設備
の運転制御パラメータは、空調設備の設計時点において
予測される空調熱負荷に応じて予め設定され、それに基
づいて実際の運転制御が実施されていた。As described above, in order to perform energy-saving and cost-saving operation, it is important to optimally control the operating conditions of the heat source equipment of the air conditioning equipment (the operating order of the heat source equipment group, start / stop times, etc.). . Heretofore, such operation control parameters of the air conditioning equipment have been preset according to the air conditioning heat load predicted at the time of designing the air conditioning equipment, and the actual operation control has been performed based on this.
【０００５】しかしながら、実建物が設計時点で想定さ
れた通りに運用されることは稀であり、この結果空調熱
負荷も設計時の値とは異なるものとなることが多かっ
た。省エネルギー・省コスト運転を行うためには、空調
熱負荷が設計値と異なる場合には、当然熱源機器の運転
方法も、その負荷の変更に応じて最適化してやることが
重要である。しかし現在のところ、空調設備の熱負荷
（空調負荷）を予測し、空調機器の運転の最適化を図る
運転制御技術は確立されていない。However, the actual building is rarely operated as expected at the time of designing, and as a result, the air conditioning heat load often differs from the value at the time of designing. In order to perform energy-saving and cost-saving operation, when the air-conditioning heat load is different from the design value, it is important to optimize the operation method of the heat source device according to the change of the load. However, at present, an operation control technology for predicting the heat load (air conditioning load) of the air conditioning equipment and optimizing the operation of the air conditioning equipment has not been established.
【０００６】[0006]
【発明が解決しようとする課題】従って、本発明の目的
は、従来の空調設備の運転制御に関する上記のような問
題点に鑑み、空調設備の熱負荷を予測し、空調機器の運
転の最適化を図ることが可能な新規かつ改良された空調
設備の運転制御方法を提供することである。SUMMARY OF THE INVENTION Therefore, in view of the above-mentioned problems relating to the operation control of the conventional air conditioning equipment, the object of the present invention is to predict the heat load of the air conditioning equipment and optimize the operation of the air conditioning equipment. It is to provide a new and improved operation control method for an air conditioning facility capable of achieving the above.
【０００７】さらに、本発明の別の目的は、空調負荷に
応じた空調機機の運転制御を行うことにより、空調機器
の省エネルギー・省ランニングコスト化を達成すること
が可能な新規かつ改良された空調設備の運転制御方法を
提供することである。Another object of the present invention is to provide a new and improved air conditioner capable of achieving energy saving and running cost saving by controlling the operation of the air conditioner according to the air conditioning load. An object of the present invention is to provide an operation control method for air conditioning equipment.
【０００８】[0008]
【課題を解決するための手段】上記課題を解決するため
に本発明によれば、制御される空調設備の空調熱負荷及
びその空調設備の設置環境の外気条件を所定期間にわた
り実測し、その結果取得された空調熱負荷実測データ及
び外気条件実測データに関して重回帰分析し、その結果
からその空調設備の設置環境に固有の上記空調熱負荷と
上記外気条件との重相関式を求め、その重相関式に予測
したい予測期間の標準外気条件を適用することにより、
その予測期間にわたる平均空調熱負荷を予測することを
特徴とする、空調熱負荷予測方法が提供される。In order to solve the above problems, according to the present invention, the air conditioning heat load of the air conditioning equipment to be controlled and the outside air condition of the installation environment of the air conditioning equipment are measured for a predetermined period, and the result is Multiple regression analysis is performed on the acquired air-conditioning heat load measurement data and outside air condition measurement data, and from the results, a multiple correlation equation between the above-mentioned air conditioning heat load and the outside air conditions unique to the installation environment of the air conditioning equipment is obtained, and the multiple correlation By applying the standard outside air condition of the forecast period you want to forecast to the formula,
There is provided an air conditioning heat load prediction method characterized by predicting an average air conditioning heat load over the prediction period.
【０００９】さらに、本発明によれば、上記空調設備が
複数の熱源機器群を含む場合には、予測された上記平均
空調熱負荷に基づいて、上記熱源機器群の最適な運転順
位及び各機器の発停時刻を決定することが好ましい。ま
た、上記空調設備が蓄熱槽を含む場合には、予測された
上記平均空調熱負荷に基づいて、上記蓄熱槽の最適な蓄
熱時刻及び放熱時刻とを決定することが好ましい。Further, according to the present invention, when the air conditioning equipment includes a plurality of heat source equipment groups, the optimum operating order and each equipment of the heat source equipment group are based on the predicted average air conditioning heat load. It is preferable to determine the start and stop times of. Further, when the air conditioning equipment includes a heat storage tank, it is preferable to determine the optimum heat storage time and heat release time of the heat storage tank based on the predicted average air conditioning heat load.
【００１０】[0010]
【作用】本発明は、空調熱負荷及び外気条件の所定期間
にわたる実測データを重回帰分析することにより、使用
される空調設備の設置環境に固有な空調熱負荷と外気条
件との間の重相関式を算出するように構成されている。
この重相関式により、その設置環境に固有な空調熱負荷
と外気条件が精度よく関係づけられる。また、その重相
関式における偏回帰係数は、その空調設備が設置される
建物などの設置環境に固有なものなので、その重相関式
に予測したい予測期間にわたる当該地域の標準外気条件
を代入してやることことで、予測期間中の平均空調熱負
荷を正確に予測することが可能となる。このようにし
て、実建物における空調負荷が設計時に想定したものと
異なる場合であっても、平均空調熱負荷を予測すること
により、空調機器を最適に運転制御することができる。According to the present invention, the multiple correlation between the air conditioning heat load and the outside air condition, which is peculiar to the installation environment of the air conditioning equipment to be used, is obtained by performing the multiple regression analysis of the actual measurement data of the air conditioning heat load and the outside air condition. It is configured to calculate an expression.
With this multiple correlation equation, the air-conditioning heat load unique to the installation environment and the outside air condition are accurately related. In addition, the partial regression coefficient in the multiple correlation equation is unique to the installation environment such as the building where the air conditioning equipment is installed, so substitute the standard outside air conditions of the area for the forecast period you want to predict in the multiple correlation equation. This makes it possible to accurately predict the average air conditioning heat load during the prediction period. In this way, even if the air conditioning load in the actual building is different from that assumed at the time of design, it is possible to optimally control the operation of the air conditioning equipment by predicting the average air conditioning heat load.
【００１１】また、本発明は、予測された平均空調熱負
荷の値に基づいて、複数の熱源機器群の最適な運転順位
及び各機器の発停時刻を決定したり、あるいは蓄熱槽の
最適な蓄熱時刻及び放熱時刻を決定したりするように構
成されている。このようにして運転制御パラメータを最
適化することにより、設置環境や時期に対応して細やか
に運転制御することができるので、省エネルギー・省ラ
ンニングコストで設備を稼働させることができる。Further, according to the present invention, based on the predicted value of the average air-conditioning heat load, the optimum operating order of a plurality of heat source device groups and the start / stop time of each device are determined, or the optimum heat storage tank is selected. It is configured to determine the heat storage time and the heat radiation time. By optimizing the operation control parameters in this way, it is possible to perform detailed operation control in accordance with the installation environment and time, and therefore it is possible to operate the equipment with energy saving and running cost saving.
【００１２】[0012]
【実施例】以下に添付図面を参照しながら、本発明に基
づく空調熱負荷予測方法の構成と作用とを具体的に説明
する。図１は、本発明に基づく空調熱負荷予測方法を適
用可能な典型的なシステム構成図である。図示の通り、
本願に基づく空調熱負荷予測方法を適用可能なシステム
は、中央監視装置１１と空調負荷予測システム１２とか
ら構成されており、中央監視装置１１には、入出力イン
タフェース１３及び１４から、それぞれ、気象データや
空調負荷に関する実績データを、自動的に又は手動によ
り入力することが可能である。中央監視装置１１は、例
えばワークステーションのような、中央処理装置、記憶
装置及び入出力装置から構成されるコンピュータシステ
ムであり、入出力インタフェース１３、１４から受け取
った実績データを内部記憶装置内に格納すると共に、そ
れらの実績データを空調負荷予測システム１２に転送す
ることが可能である。DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS The structure and operation of an air conditioning heat load prediction method according to the present invention will be specifically described below with reference to the accompanying drawings. FIG. 1 is a typical system configuration diagram to which the air conditioning heat load prediction method according to the present invention can be applied. As shown,
A system to which the air conditioning heat load prediction method according to the present application can be applied includes a central monitoring device 11 and an air conditioning load prediction system 12, and the central monitoring device 11 receives weather information from input / output interfaces 13 and 14, respectively. It is possible to input the data and the actual data regarding the air conditioning load automatically or manually. The central monitoring device 11 is a computer system including a central processing unit, a storage device, and an input / output device, such as a workstation, and stores the record data received from the input / output interfaces 13 and 14 in the internal storage device. At the same time, it is possible to transfer those performance data to the air conditioning load prediction system 12.
【００１３】空調負荷予測システム１２は受信した実績
データを分析すると共に、その分析データに基づいて空
調負荷の予測をすることが可能である。空調負荷予測シ
ステム１２において予測された予測データは中央監視装
置１１に戻され、中央監視装置１１はこの予測データに
基づいて、空調機器の制御方法を決定し、その決定に応
じて空調機器を運転制御する。なお、図１に示すシステ
ム構成においては、中央監視装置１１と空調負荷予測シ
ステム１２とを別個のハードウェア構成としているが、
当然にこれらを同一のハードウェア構成とすることもで
きる。The air conditioning load prediction system 12 can analyze the received performance data and predict the air conditioning load based on the analysis data. The prediction data predicted by the air conditioning load prediction system 12 is returned to the central monitoring device 11, and the central monitoring device 11 determines the control method of the air conditioning equipment based on this prediction data, and operates the air conditioning equipment according to the determination. Control. In the system configuration shown in FIG. 1, the central monitoring device 11 and the air conditioning load prediction system 12 have separate hardware configurations.
Of course, these may have the same hardware configuration.
【００１４】次に、上記システムで実施される本発明に
基づく空調熱負荷予測の手順について説明する。上述の
ように、熱源機器の運転順位や発停時刻、あるいは蓄熱
槽の蓄熱時刻や放熱時刻といった空調設備の運転制御パ
ラメータについては、その設計段階において、ある程度
予め設定されている。しかしながら、実建物がその設定
通りに運用されることは稀であり、実際の運用条件に応
じて、上記運転制御パラメータを調整する必要がある。
その場合に、本発明では、過去の実績データに基づいて
将来の空調負荷を予測し、その予測データに基づいて、
運転制御パラメータの調整が行われる。Next, a procedure of air conditioning heat load prediction based on the present invention, which is carried out in the above system, will be described. As described above, the operation control parameters of the air conditioning equipment such as the operation order and start / stop time of the heat source device, the heat storage time and the heat release time of the heat storage tank are preset to some extent at the design stage. However, the actual building is rarely operated according to the setting, and it is necessary to adjust the operation control parameters according to the actual operating conditions.
In that case, in the present invention, a future air conditioning load is predicted based on past performance data, and based on the predicted data,
The operation control parameters are adjusted.
【００１５】本発明によれば、まず実際に稼働している
空調設備の空調熱負荷、及び日最高気温、日最低気温、
日平均絶対湿度などの外気条件などの実績データが所定
期間、例えば過去１ヶ月にわたり実測される。これらの
実測値は、空調設備の規模、設置環境、稼働条件、ある
いは季節によって大きく異なるため、同じ型式の空調設
備に関しても一様の値を示すものではない。しかし、実
測により取得された過去の、例えば１ヶ月にわたる実績
データを重回帰分析すると、図２及び図３に示すよう
に、各空調設備の空調熱負荷は外気条件と精度よく関係
づけられることが分かる。According to the present invention, first, the air conditioning heat load of the air conditioning equipment that is actually operating, and the daily maximum temperature, minimum daily temperature,
Actual data such as outside air conditions such as daily average absolute humidity are measured over a predetermined period, for example, the past month. Since these measured values greatly vary depending on the scale, installation environment, operating conditions, or season of the air conditioning equipment, they do not show uniform values even for air conditioning equipment of the same type. However, when multiple regression analysis is performed on the past actual data obtained over a period of one month, for example, as shown in FIGS. 2 and 3, the air conditioning heat load of each air conditioning facility may be accurately related to the outside air condition. I understand.
【００１６】図２及び図３は、それぞれ、ある年の７月
及び翌８月の実冷水熱負荷と回帰冷水熱負荷との関係を
示すグラフであり、熱負荷実績及び外気条件（日最高気
温、日最低気温、日平均絶対湿度）の重回帰分析結果を
示している。図２及び図３において、縦軸の回帰冷水熱
負荷は、重回帰分析より得られた重回帰式に実績の上記
外気条件を代入した値である。従って、図２及び図３に
より、外気条件と熱負荷を重回帰式に基づき関連づけた
場合の精度が示される。この場合の重相関係数Ｒ2は、
図２の例では、０．７７２であり、図３の例では、０．
８４１を示している。FIG. 2 and FIG. 3 are graphs showing the relationship between the actual cold water heat load and the return cold water heat load in July and August of a certain year, respectively. , Minimum daily temperature, average daily absolute humidity). In FIG. 2 and FIG. 3, the regression cold water heat load on the vertical axis is a value obtained by substituting the actual outside air condition described above into the multiple regression equation obtained from the multiple regression analysis. Therefore, FIGS. 2 and 3 show the accuracy when the outside air condition and the heat load are associated with each other based on the multiple regression equation. The multiple correlation coefficient R 2 in this case is
In the example of FIG. 2, it is 0.772, and in the example of FIG.
841 is shown.
【００１７】ここで、図２及び図３に関して用いられた
重相関式の一例は次の通りである。 図２ ： Q = 372.19*Tmax + 589.87*Tmin + 506120*X
- 708.1 図３ ： Q = 366.56*Tmax + 144.10*Tmin + 749934*X
- 7571.7 上式において、Ｑ：冷水熱負荷［Mcal／日］ Ｔmax：日最高気温［℃］ Ｔmin：日最低気温［℃］ Ｘ：日平均絶対湿度［kg／kgDA］Here, an example of the multiple correlation equation used in connection with FIGS. 2 and 3 is as follows. Figure 2: Q = 372.19 * Tmax + 589.87 * Tmin + 506120 * X
-708.1 Figure 3: Q = 366.56 * Tmax + 144.10 * Tmin + 749934 * X
-7571.7 In the above formula, Q: Cold water heat load [Mcal / day] Tmax: Maximum daily temperature [° C] Tmin: Minimum daily temperature [° C] X: Daily average absolute humidity [kg / kgDA]
【００１８】このようにして得られた上記関係式の偏回
帰係数は、実際に稼働している空調設備が設置されてい
る実建物に固有の数値となるので、かかる重相関式に基
づいて空調熱負荷を予測してやることにより、実際の運
用に即して、各空調設備に固有の運転制御を行うことが
可能である。予測に際しては、予測したい予測期間、例
えば翌月の標準外気条件を上記重相関式に代入すること
により、翌月の平均空調熱負荷を予測することができ
る。代入する標準外気条件としては、当該地域の標準気
象データを用いることができる。このようにして予測さ
れた平均空調熱負荷は、図４に示すように、実測された
実負荷と精度よく一致している。Since the partial regression coefficient of the above relational expression thus obtained is a numerical value peculiar to the actual building in which the air-conditioning equipment that is actually operating is installed, the air-conditioning is calculated based on such a multiple correlation equation. By predicting the heat load, it is possible to perform operation control specific to each air conditioning facility in accordance with actual operation. At the time of prediction, the average air conditioning heat load for the next month can be predicted by substituting the standard outside air condition of the desired prediction period, for example, the standard outside air condition of the next month, into the above multiple correlation equation. As the standard outside air condition to be substituted, standard meteorological data of the area can be used. The average air conditioning heat load predicted in this manner accurately matches the actually measured actual load, as shown in FIG.
【００１９】本発明に基づいて翌月の空調負荷に関する
予測データを利用することにより、実際に稼働している
環境の空調負荷に応じた空調機器運転制御の最適設定が
可能となる。最適化される項目は、例えば、翌月の熱源
機器の最適運転方法（運転樹にや発停時刻）、あるい
は、翌月の蓄熱槽の最適な蓄熱時刻、放熱時刻などであ
る。最適化される項目は、最も高効率で低い熱損失で設
備を稼働できる制御パラメータの中から適宜選択するこ
とができる。By utilizing the predicted data on the air conditioning load of the next month based on the present invention, it becomes possible to optimally set the operation control of the air conditioning equipment according to the air conditioning load of the actual operating environment. The items to be optimized are, for example, the optimum operation method of the heat source device (starting time and start / stop time) of the next month, or the optimum heat storage time and heat release time of the heat storage tank in the next month. Items to be optimized can be appropriately selected from control parameters that can operate the equipment with the highest efficiency and the lowest heat loss.
【００２０】[0020]
【発明の効果】以上のようにして、本発明によれば、過
去の実測データに基づいて設置環境に応じて翌月の空調
負荷を予測することができるので、設置環境や季節に応
じて空調機器運転制御を最適設定することができる。こ
の結果、最も高効率で低い熱損失で設備を稼働すること
が可能となり、省エネルギー・省ランニングコストに優
れた空調機器の運転制御を実施できる。As described above, according to the present invention, the air conditioning load of the next month can be predicted according to the installation environment based on the past measured data. Therefore, the air conditioning equipment can be installed according to the installation environment and the season. The operation control can be optimally set. As a result, the equipment can be operated with the highest efficiency and the lowest heat loss, and the operation control of the air conditioning equipment excellent in energy saving and running cost can be performed.
【図１】本発明に基づく空調負荷予測方法を適用可能な
システムの構成図である。FIG. 1 is a configuration diagram of a system to which an air conditioning load prediction method based on the present invention can be applied.
【図２】本発明に基づいて重回帰分析されたある年の７
月の実冷水熱負荷と回帰冷水熱負荷との関係を示すグラ
フである。FIG. 2 is a year of multiple regression analysis based on the present invention.
It is a graph which shows the relationship between the actual cold water heat load and regression cold water heat load of a month.
【図３】本発明に基づいて重回帰分析されたある年の８
月の実冷水熱負荷と回帰冷水熱負荷との関係を示すグラ
フである。FIG. 3: 8 of a year subjected to multiple regression analysis according to the present invention
It is a graph which shows the relationship between the actual cold water heat load and regression cold water heat load of a month.
【図４】本発明に基づいて予測された平均冷水熱負荷予
測の予測精度の様子を示すグラフである。FIG. 4 is a graph showing the prediction accuracy of the average cold water heat load prediction predicted based on the present invention.
１１ 中央監視装置 １２ 空調負荷予測システム １３ 気象データ入出力インタフェース １４ 空調負荷入出力インタフェース 11 Central Monitoring Device 12 Air Conditioning Load Prediction System 13 Meteorological Data Input / Output Interface 14 Air Conditioning Load Input / Output Interface
Claims (3)
空調設備の設置環境の外気条件を所定期間にわたり実測
し、その結果取得された空調熱負荷実測データ及び外気
条件実測データに関して重回帰分析し、その結果からそ
の空調設備の設置環境に固有の上記空調熱負荷と上記外
気条件との重相関式を求め、その重相関式に予測したい
予測期間の標準外気条件を適用することにより、その予
測期間にわたる平均空調熱負荷を予測することを特徴と
する、空調熱負荷予測方法。1. A multiple regression analysis of the air conditioning heat load of the controlled air conditioning equipment and the outside air condition of the installation environment of the air conditioning equipment are measured over a predetermined period, and the air conditioning heat load measurement data and the outside air condition measurement data acquired as a result are measured. Then, from the result, the multiple correlation equation between the above-mentioned air conditioning heat load and the outside air condition specific to the installation environment of the air conditioning equipment is obtained, and by applying the standard outside air condition of the forecast period to be predicted to the multiple correlation equation, A method for predicting an air conditioning heat load, characterized by predicting an average air conditioning heat load over a prediction period.
請求項１に記載の空調熱負荷予測方法により予測された
上記平均空調熱負荷に基づいて、上記熱源機器群の最適
な運転順位及び各機器の発停時刻を決定することを特徴
とする、請求項１に記載の空調熱負荷予測方法。2. The air conditioning equipment includes a plurality of heat source equipment groups,
An optimum operating order of the heat source device group and start / stop times of each device are determined based on the average air conditioning heat load predicted by the air conditioning heat load prediction method according to claim 1. Item 2. The air conditioning heat load prediction method according to Item 1.
記載の空調熱負荷予測方法により予測された上記平均空
調熱負荷に基づいて、上記蓄熱槽の最適な蓄熱時刻及び
放熱時刻とを決定することを特徴とする、請求項１又は
２に記載の空調熱負荷予測方法。3. The optimum heat storage time and heat release time of the heat storage tank based on the average air conditioning heat load predicted by the air conditioning heat load prediction method according to claim 1, wherein the air conditioning equipment includes a heat storage tank. The air conditioning heat load prediction method according to claim 1 or 2, wherein
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| JP4350176A JPH06174285A (en) | 1992-12-04 | 1992-12-04 | Air conditioning heat load prediction method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| JP4350176A JPH06174285A (en) | 1992-12-04 | 1992-12-04 | Air conditioning heat load prediction method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| JPH06174285A true JPH06174285A (en) | 1994-06-24 |
Family
ID=18408733
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| JP4350176A Pending JPH06174285A (en) | 1992-12-04 | 1992-12-04 | Air conditioning heat load prediction method |
Country Status (1)
| Country | Link |
|---|---|
| JP (1) | JPH06174285A (en) |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH0894131A (en) * | 1994-09-28 | 1996-04-12 | Takasago Thermal Eng Co Ltd | Brine concentration management method for heating tower |
| JP2000274772A (en) * | 1999-03-23 | 2000-10-06 | Mitsubishi Electric Corp | Energy management device and management method |
| JP2009019874A (en) * | 2008-10-21 | 2009-01-29 | Daikin Ind Ltd | Estimated power consumption calculation device for air conditioner, estimated power consumption calculation program, and energy-saving efficiency calculation device for air conditioner |
| WO2016147780A1 (en) * | 2015-03-13 | 2016-09-22 | 株式会社東芝 | Device characteristic model learning device, device characteristic model learning method, and storage medium |
-
1992
- 1992-12-04 JP JP4350176A patent/JPH06174285A/en active Pending
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JPH0894131A (en) * | 1994-09-28 | 1996-04-12 | Takasago Thermal Eng Co Ltd | Brine concentration management method for heating tower |
| JP2000274772A (en) * | 1999-03-23 | 2000-10-06 | Mitsubishi Electric Corp | Energy management device and management method |
| JP2009019874A (en) * | 2008-10-21 | 2009-01-29 | Daikin Ind Ltd | Estimated power consumption calculation device for air conditioner, estimated power consumption calculation program, and energy-saving efficiency calculation device for air conditioner |
| WO2016147780A1 (en) * | 2015-03-13 | 2016-09-22 | 株式会社東芝 | Device characteristic model learning device, device characteristic model learning method, and storage medium |
| JP2016170715A (en) * | 2015-03-13 | 2016-09-23 | 株式会社東芝 | Equipment characteristics model learning system, a method and a program of learning a model of equipment characteristics |
| CN107735735A (en) * | 2015-03-13 | 2018-02-23 | 株式会社东芝 | Device characteristics model learning device, device characteristics model learning method and storage medium |
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