JP2009058295A - System, method and program for predicting wind situation - Google Patents

System, method and program for predicting wind situation Download PDF

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JP2009058295A
JP2009058295A JP2007224560A JP2007224560A JP2009058295A JP 2009058295 A JP2009058295 A JP 2009058295A JP 2007224560 A JP2007224560 A JP 2007224560A JP 2007224560 A JP2007224560 A JP 2007224560A JP 2009058295 A JP2009058295 A JP 2009058295A
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wind condition
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JP5283360B2 (en
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Takaaki Ishikawa
孝明 石河
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Chugoku Electric Power Co Inc
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Abstract

<P>PROBLEM TO BE SOLVED: To enable prediction of high analytical accuracy, though it is simple and easy, as to a wind situation in a few days at a plurality of points. <P>SOLUTION: The system is equipped with a representative-neighboring correlation storage part 21 which stores the correlation between the wind situation at a representative point and that at a neighboring point, a prediction acquiring part 35 which acquires information on prediction of the wind situation at the representative point, and a neighboring point prediction part 36 which predicts the wind situation at the neighboring point, based on the correlation stored by the representative-neighboring correlation storage part 21 and the information on the prediction of the wind situation at the representative point acquired by the representative point prediction acquiring part 35. <P>COPYRIGHT: (C)2009,JPO&INPIT

Description

風況予測システム、方法及びプログラムに関する。   The present invention relates to a wind condition prediction system, method, and program.

現在、電力事業者の電力系統に連系される風力発電量が急速に増加しており、風力発電についても翌日電力需給計画に反映することが望まれている。この需給計画には、的確な風力発電量予測が必要であり、この予測には根拠となる的確な風況予測が不可欠である。そこで、電力事業者は、気象庁の風況予測データや種々の風況予測システムを用いて的確な風況予測に努めている。   Currently, the amount of wind power generation connected to the power grid of power companies is rapidly increasing, and it is desired that wind power generation be reflected in the power supply and demand plan the next day. This supply and demand plan requires accurate wind power generation prediction, and accurate wind condition prediction as a basis is indispensable for this prediction. Therefore, electric power companies are making efforts to accurately predict wind conditions using the JMA wind condition prediction data and various wind condition prediction systems.

例えば、特許文献１には、観測ポールによって取得された通年の風況データと三次元風速計によって取得された風況データに基づいて、風力発電所での風況を予測するシステムが開示されている。また、特許文献２には、多数の地点に関する気象予報データに基づいて、風力発電所の風況を予測する方法が開示されている。
特開２００５−１００８２号公報 特開２００５−１６３６０８号公報 For example, Patent Document 1 discloses a system for predicting wind conditions at a wind power plant based on year-round wind condition data acquired by an observation pole and wind condition data acquired by a three-dimensional anemometer. Yes. Patent Document 2 discloses a method for predicting the wind condition of a wind power plant based on weather forecast data relating to a large number of points.
Japanese Patent Laid-Open No. 2005-10082 JP 2005-163608 A

しかし、特許文献１に記載のシステムや特許文献２に記載の方法では、狭い範囲の地域（例えば、１０ｋｍ平方）の風況を個別に予測しようとすると予測精度は高くすることができるものの、全ての地点について風況予測を行うと手間がかかり、また費用が嵩むとの問題がある。すなわち、特許文献１に記載のシステムでは、詳細な風況データを取得する必要がり、また、特許文献２に記載の方法では、複数のサイトの気象予報データを取得する必要があり、このために手間がかかり、また費用が嵩むこととなる。   However, in the system described in Patent Document 1 and the method described in Patent Document 2, prediction accuracy can be increased when trying to individually predict wind conditions in a narrow area (for example, 10 km square). Predicting wind conditions at these points is troublesome and expensive. That is, in the system described in Patent Document 1, it is necessary to acquire detailed wind condition data, and in the method described in Patent Document 2, it is necessary to acquire weather forecast data for a plurality of sites. This is time consuming and expensive.

本発明は、このような課題に対して、複数の地点における近日中（例えば翌日から３日後）の風況について、簡易でありながら精度の高い予測が可能な風況予測システムを提供することを目的とする。   In order to solve such a problem, the present invention provides a wind condition prediction system capable of predicting a wind condition in a near future (for example, three days after the next day) at a plurality of points in a simple but highly accurate manner. Objective.

第１の発明は、第１の地点における風況と第２の地点における風況との相関関係を記憶する第１の相関関係記憶部と、
前記第１の地点における風況予測の情報を取得する予測取得部と、
前記第１の相関関係記憶部が記憶する相関関係と、前記予測取得部が取得した前記第１の地点における風況予測の情報とに基づいて、前記第２の地点における風況を予測する第１の予測部と、
を備えることを特徴とする風況予測システムである。 A first invention is a first correlation storage unit that stores a correlation between a wind condition at a first point and a wind condition at a second point;
A prediction acquisition unit for acquiring information of wind condition prediction at the first point;
Based on the correlation stored in the first correlation storage unit and the information on wind condition prediction at the first point acquired by the prediction acquisition unit, the wind condition at the second point is predicted. 1 predictor;
It is the wind condition prediction system characterized by providing.

第２の発明は、第１の発明に記載の風況予測システムであって、
前記第２の地点における風況と第３の地点における風況との相関関係を記憶する第２の相関関係記憶部と、
前記第２の相関関係記憶部が記憶する相関関係と、前記第１の予測部が予測した前記第２の地点における風況の予測結果とに基づいて、前記第３における地点の風況を予測する第２の予測部と、
をさらに備えることを特徴とする風況予測システムである。 2nd invention is the wind condition prediction system as described in 1st invention, Comprising:
A second correlation storage unit for storing a correlation between a wind condition at the second point and a wind condition at the third point;
Based on the correlation stored in the second correlation storage unit and the prediction result of the wind condition at the second point predicted by the first prediction unit, the wind condition at the third point is predicted. A second predictor to
Is further provided with a wind condition prediction system.

第３の発明は、第１又は２の発明に記載の風況予測システムであって、
過去の所定期間における前記第１の地点の風況データを取得する第１の実績取得部と、
前記所定期間における前記第２の地点の風況データを取得する第２の実績取得部と、
をさらに備え、
前記第１の記憶部に記憶された相関関係が、前記第１の実績取得部が取得した前記所定期間における第１の地点の風況データと前記第２の実績取得部が取得した前記所定期間における第２の地点の風況データとに基づいて算出されたものであることを特徴とする風況予測システムである。 3rd invention is a wind condition prediction system as described in 1st or 2nd invention, Comprising:
A first result acquisition unit that acquires wind condition data of the first point in a past predetermined period;
A second result acquisition unit for acquiring wind condition data of the second point in the predetermined period;
Further comprising
The correlation stored in the first storage unit includes the wind condition data at the first point in the predetermined period acquired by the first result acquisition unit and the predetermined period acquired by the second result acquisition unit. It is a wind condition prediction system characterized by being calculated based on the wind condition data of the 2nd point in.

第４の発明は、第２の発明に記載の風況予測システムであって、
過去の所定期間における前記第２の地点の風況データを取得する第２の実績取得部と、
前記所定期間における前記第３の地点の風況データを記憶する第３の実績取得部と、
をさらに備え、
前記第２の相関関係記憶部に記憶された相関関係が、前記第２の実績取得部が取得した前記所定期間における第２の地点の風況データと前記第３の実績取得部が取得した前記所定期間における第３の地点の風況データとに基づいて算出されたものであることを特徴とする風況予測システムである。 4th invention is the wind condition prediction system as described in 2nd invention, Comprising:
A second result acquisition unit for acquiring wind condition data at the second point in the past predetermined period;
A third result acquisition unit for storing wind condition data at the third point in the predetermined period;
Further comprising
The correlation stored in the second correlation storage unit is obtained by the wind condition data of the second point in the predetermined period acquired by the second actual result acquisition unit and the third actual result acquisition unit. It is a wind condition prediction system characterized by being calculated based on the wind condition data of the 3rd point in a predetermined period.

第５の発明は、第２の発明に記載の風況予測システムであって、
過去の所定期間における前記第１の地点の風況データを取得する第１の実績取得部と、
前記所定期間における前記第２の地点の風況データを取得する第２の実績取得部と、
前記所定期間における前記第３の地点の風況データを記憶する第３の実績取得部と、
をさらに備え、
前記第１の記憶部に記憶された相関関係が、前記第１の実績取得部が取得した前記所定期間における第１の地点の風況データと前記第２の実績取得部が取得した前記所定期間における第２の地点の風況データとに基づいて算出され、
前記第２の相関関係記憶部に記憶された相関関係が、前記第２の実績取得部が取得した前記所定期間における第２の地点の風況データと前記第３の実績取得部が取得した前記所定期間における第３の地点の風況データとに基づいて算出されたものであることを特徴とする風況予測システムである。 5th invention is a wind condition prediction system as described in 2nd invention, Comprising:
A first result acquisition unit that acquires wind condition data of the first point in a past predetermined period;
A second result acquisition unit for acquiring wind condition data of the second point in the predetermined period;
A third result acquisition unit for storing wind condition data at the third point in the predetermined period;
Further comprising
The correlation stored in the first storage unit includes the wind condition data at the first point in the predetermined period acquired by the first result acquisition unit and the predetermined period acquired by the second result acquisition unit. Calculated based on wind condition data at the second point in
The correlation stored in the second correlation storage unit is obtained by the wind condition data of the second point in the predetermined period acquired by the second actual result acquisition unit and the third actual result acquisition unit. It is a wind condition prediction system characterized by being calculated based on the wind condition data of the 3rd point in a predetermined period.

本発明によれば、複数の地点における近日中の風況について、簡易でありながら解析精度の高い予測が可能な風況予測システムを提供することができる。   According to the present invention, it is possible to provide a wind condition prediction system that is capable of predicting a near-term wind condition at a plurality of points in a simple manner with high analysis accuracy.

以下、図面を参照しながら、本発明を実施するための最良の形態を説明する。本発明の実施の形態に係る風況予測システム１は、代表ポイントＲＰ（特許請求の範囲でいう「第１の地点」）における風況予測の情報に基づいて、代表ポイントＲＰの近隣に位置する近隣ポイントＮＰ１〜５（特許請求の範囲でいう「第２の地点」）の風況を予測するものである。また、風況予測システム１は、近隣ポイントＮＰ１〜５の風況予測の情報に基づいて、近隣ポイントＮＰ１〜５の近隣に位置する遠隔ポイントＦＰ１〜７（特許請求の範囲でいう「第３の地点」）の風況を予測するものである。   Hereinafter, the best mode for carrying out the present invention will be described with reference to the drawings. The wind condition prediction system 1 according to the embodiment of the present invention is located in the vicinity of the representative point RP based on the information on the wind condition prediction at the representative point RP (the “first point” in the claims). The wind conditions of the neighboring points NP1 to NP5 (“second point” in the claims) are predicted. Moreover, the wind condition prediction system 1 is based on the information on the wind condition prediction of the neighboring points NP1 to NP5, and the remote points FP1 to FP1-7 located in the vicinity of the neighboring points NP1 to NP5 This is to predict the wind conditions at the point “)”.

図１は、風況予測システム１を用いた風況予測の対象となる近隣ポイントＮＰ１〜５と、遠隔ポイントＦＰ１〜７と、その風況予測の基礎となる風況データを提供する代表ポイントＲＰとの位置関係を示す模式図である。同図に示すように、近隣ポイントＮＰ及び遠隔ポイントＦＰは複数あり、代表ポイントＲＰと各近隣ポイントＮＰとは数ｋｍから２０ｋｍ程度離れた位置にあり、また代表ポイントＲＰと各遠隔ポイントＦＰとは各近隣ポイントＮＰよりも遠い数十ｋｍ程度離れた位置にある。   FIG. 1 shows representative points RP that provide neighboring points NP1 to NP1 to 5 that are targets of wind condition prediction using the wind condition prediction system 1, remote points FP1 to FP7, and wind condition data that is the basis of the wind condition prediction. FIG. As shown in the figure, there are a plurality of neighboring points NP and remote points FP, the representative point RP and each neighboring point NP are located at a distance of several kilometers to 20 km, and the representative point RP and each remote point FP It is at a position about several tens of kilometers away from each neighboring point NP.

＜＜システムの構成と概要＞＞
図２は、本発明の一実施形態である風況予測システム１のハードウェア構成を示す図である。風況予測システム１は、ＰＣ（Ｐｅｒｓｏｎａｌ Ｃｏｍｐｕｔｅｒ）やサーバ等のコンピュータと周辺機器とが接続されることにより構成され、ＣＰＵ（Ｃｅｎｔｒａｌ Ｐｒｏｃｅｓｓｉｎｇ Ｕｎｉｔ）１０、メモリ１１、記憶手段１２、記録媒体読取手段１３、通信インターフェイス１４、入力手段１５、出力手段１６等を備えている。 << System configuration and overview >>
FIG. 2 is a diagram illustrating a hardware configuration of the wind condition prediction system 1 according to the embodiment of the present invention. The wind condition prediction system 1 is configured by connecting a computer such as a PC (Personal Computer) or a server and peripheral devices, and includes a CPU (Central Processing Unit) 10, a memory 11, a storage unit 12, and a recording medium reading unit 13. , Communication interface 14, input means 15, output means 16 and the like.

ＣＰＵ１０は、記憶手段１２に記憶されているプログラムをメモリ１１に読み出して実行する。メモリ１１は、例えばＤＲＡＭ（Ｄｙｎａｍｉｃ Ｒａｎｄｏｍ Ａｃｃｅｓｓ Ｍｅｍｏｒｙ）等である。記憶手段１２は、例えばハードディスクドライブ等である。記録媒体読取手段１３は、例えばＣＤ−ＲＯＭ（Ｃｏｍｐａｃｔ Ｄｉｓｃ Ｒｅａｄ Ｏｎｌｙ Ｍｅｍｏｒｙ）等の記録媒体１７に記録されたプログラムやデータを読み取るドライブ装置である。通信インターフェイス１４は、例えばＮＩＣ（Ｎｅｔｗｏｒｋ Ｉｎｔｅｒｆａｃｅ Ｃａｒｄ）等のネットワーク接続機器である。入力手段１５は、例えばキーボードやマウス等である。出力手段１６は、例えばディスプレイやプリンタ等である。   The CPU 10 reads the program stored in the storage unit 12 into the memory 11 and executes it. The memory 11 is, for example, a DRAM (Dynamic Random Access Memory). The storage unit 12 is, for example, a hard disk drive. The recording medium reading unit 13 is a drive device that reads a program and data recorded on a recording medium 17 such as a CD-ROM (Compact Disc Read Only Memory). The communication interface 14 is a network connection device such as a NIC (Network Interface Card). The input means 15 is, for example, a keyboard or a mouse. The output unit 16 is, for example, a display or a printer.

図３は、本発明の一実施形態である風況予測システム１のブロック構成図である。同図に示すように、風況予測システム１は、代表ポイント実績取得部３１、近隣ポイント実績取得部３２、遠隔ポイント実績取得部３３、代表近隣相関関係算出部３４、代表ポイント予測取得部３５（特許請求の範囲でいう「取得部」）、近隣ポイント予測部３６（特許請求の範囲でいう「第１の予測部」）、近隣遠隔相関関係算出部３７、遠隔ポイント予測部３８（特許請求の範囲でいう「第２の予測部」）等の各機能部と、代表近隣相関関係記憶部２１（特許請求の範囲でいう「第１の相関関係記憶部」）、近隣遠隔相関関係記憶部２２（特許請求の範囲でいう「第２の相関関係記憶部」）等の各記憶部を備える。なお、各記憶部２１〜２２は、記憶手段１２上に構築され、各機能部３１〜３８は、ＣＰＵ１０が記憶手段１２に記憶されたプログラムをメモリ１１に読み出して実行することにより実現される。   FIG. 3 is a block diagram of the wind condition prediction system 1 according to an embodiment of the present invention. As shown in the figure, the wind condition prediction system 1 includes a representative point result acquisition unit 31, a neighboring point result acquisition unit 32, a remote point result acquisition unit 33, a representative neighboring correlation calculation unit 34, and a representative point prediction acquisition unit 35 ( “Acquisition unit” in the claims), neighborhood point prediction unit 36 (“first prediction unit” in the claims), neighborhood remote correlation calculation unit 37, remote point prediction unit 38 (claims) Each function unit such as “second prediction unit” in the range), representative neighborhood correlation storage unit 21 (“first correlation storage unit” in the claims), and neighborhood remote correlation storage unit 22 Each storage unit is provided (“second correlation storage unit” in the claims). The storage units 21 to 22 are constructed on the storage unit 12, and the functional units 31 to 38 are realized by the CPU 10 reading out the program stored in the storage unit 12 to the memory 11 and executing the program.

代表ポイント実績取得部３１は、過去の所定期間（例えば１年間）の代表ポイントにおける風況データを取得する。近隣ポイント実績取得部３２は、代表ポイント実績取得部３１が取得した風況データと同じ所定期間（例えば１年間）の近隣ポイントにおける風況データを取得する。遠隔ポイント実績取得部３３は、近隣ポイント実績取得部３２が取得した風況データと同じ所定期間（例えば１年間）の遠隔ポイントにおける風況データを取得する。   The representative point result acquisition unit 31 acquires wind condition data at a representative point in the past predetermined period (for example, one year). The neighboring point result acquisition unit 32 acquires wind condition data at neighboring points for the same predetermined period (for example, one year) as the wind condition data acquired by the representative point result acquisition unit 31. The remote point record acquisition unit 33 acquires wind condition data at a remote point for the same predetermined period (for example, one year) as the wind condition data acquired by the neighboring point record acquisition unit 32.

なお、各実績取得部３１〜３３は、各地点に設置された風況を計測する計測装置から通信回線を介して取得してもよく、また、夫々の地点の風況データを逐次記憶したデータベースにアクセスすることによって取得してもよい。   In addition, each result acquisition part 31-33 may acquire via the communication line from the measuring device which measures the wind condition installed in each point, and the database which memorize | stored the wind condition data of each point sequentially May be obtained by accessing

図４は、代表ポイント実績取得部３１において取得される風況データのデータ構成を示す図である。図５は、近隣ポイント実績取得部３２において取得される風況データのデータ構成を示す図である。図６は、遠隔ポイント実績取得部３３において取得される風況データのデータ構成を示す図である。図４〜６に示すように、風況データには１年間における各地点の毎日毎時の風速が含まれている。   FIG. 4 is a diagram illustrating a data configuration of wind condition data acquired by the representative point result acquisition unit 31. FIG. 5 is a diagram illustrating a data configuration of wind condition data acquired by the neighborhood point result acquisition unit 32. FIG. 6 is a diagram illustrating a data configuration of wind condition data acquired by the remote point result acquisition unit 33. As shown in FIGS. 4 to 6, the wind condition data includes the hourly wind speed of each point in one year.

代表近隣相関関係算出部３４は、代表ポイント実績取得部３１及び近隣ポイント実績取得部３２が取得した風況データに基づいて、代表ポイントと近隣ポイントの風況の相関関係を算出する。   The representative neighborhood correlation calculation unit 34 calculates the correlation between the representative point and the neighboring point wind conditions based on the wind condition data obtained by the representative point result obtaining unit 31 and the neighboring point result obtaining unit 32.

具体的には、一般に５００ｍ以上の上空における風況は地形の影響を受けず、数十ｋｍ四方程度の同一地域内では同一方向で同一風速の風が吹いているが、地上付近における風況は地形の影響を受けて、その同一地域内であっても各地点において風況が異なることが知られている。そこで、代表近隣相関関係算出部３４が、代表ポイント実績取得部３１が取得した代表ポイントＲＰの風況データと近隣ポイント実績取得部３２が取得した近隣ポイントＮＰ１の風況データとを比較して両者の相関関係である風速比αＲＮ１を算出して、代表近隣相関関係記憶部２１において記憶する。また、同様に、近隣ポイントＮＰ２〜ＮＰ５の風速と、代表ポイントＲＰの風速との風速比αＲＮ２〜αＲＮ５についても算出して、代表近隣相関関係記憶部２１において記憶する。 Specifically, in general, the wind conditions above 500m are not affected by topography, and the same wind speed is blowing in the same direction within the same area of about several tens of kilometers, but the wind condition near the ground is It is known that the wind conditions are different at each point even within the same area under the influence of topography. Therefore, the representative neighborhood correlation calculating unit 34 compares the wind condition data of the representative point RP acquired by the representative point result acquiring unit 31 with the wind condition data of the neighboring point NP1 acquired by the neighboring point result acquiring unit 32, and both. Is calculated and stored in the representative neighborhood correlation storage unit 21. Similarly, the wind speed ratios α RN2 to α RN5 between the wind speeds of the neighboring points NP2 to NP5 and the wind speed of the representative point RP are also calculated and stored in the representative neighboring correlation storage unit 21.

なお、各風速比αは季節毎又は時間帯毎に算出し、当該季節又は当該時間帯に応じて用いることとしてもよい。また、代表ポイント実績取得部３１及び近隣ポイント実績取得部３２が取得する最新の風況データに基づいて、各風速比αを再算出し、代表近隣相関関係記憶部２１に記憶された各風速比αを随時更新することとしてもよい。   Each wind speed ratio α may be calculated for each season or each time zone, and used according to the season or the time zone. Further, each wind speed ratio α is recalculated based on the latest wind condition data acquired by the representative point result acquiring unit 31 and the neighboring point result acquiring unit 32, and each wind speed ratio stored in the representative neighboring correlation storage unit 21 is calculated. α may be updated at any time.

図７は、風速比αのデータ構成を示す図である。同図に示す通り、代表近隣相関関係算出部３４が算出した各日時における各風速比αが、代表近隣相関関係記憶部２１に記憶されている。   FIG. 7 is a diagram showing a data configuration of the wind speed ratio α. As shown in the figure, each wind speed ratio α at each date and time calculated by the representative neighborhood correlation calculation unit 34 is stored in the representative neighborhood correlation storage unit 21.

代表ポイント予測取得部３５は、代表ポイントＲＰにおける風況予測の情報を取得する。この風況予測の情報は、例えば、特許文献１あるいは特許文献２に記載の風況予測システムによって予測された結果である。   The representative point prediction acquisition unit 35 acquires information on wind condition prediction at the representative point RP. This wind condition prediction information is, for example, a result predicted by the wind condition prediction system described in Patent Document 1 or Patent Document 2.

近隣ポイント予測部３６は、代表近隣相関関係記憶部２１が記憶する相関関係αと、代表ポイント予測取得部３５が取得した代表ポイントＲＰの風況予測の情報に基づいて、近隣ポイントＮＰ１〜ＮＰ５における風況を予測する。具体的には、例えば、近隣ポイントＮＰ１の風速は、同日同時刻のＲＰの風速に風速比αＲＮ１を乗じることによって算出される。同様にして、近隣ポイントＮＰ２〜ＮＰ５における風況についても予測できる。 The neighboring point prediction unit 36 is based on the correlation α stored in the representative neighboring correlation storage unit 21 and the wind condition prediction information of the representative point RP acquired by the representative point prediction acquiring unit 35, in the neighboring points NP1 to NP5. Predict wind conditions. Specifically, for example, the wind speed at the neighboring point NP1 is calculated by multiplying the wind speed of the RP at the same time on the same day by the wind speed ratio α RN1 . Similarly, the wind conditions at the neighboring points NP2 to NP5 can be predicted.

近隣遠隔相関関係算出部３７は、近隣ポイント実績取得部３２及び遠隔ポイント実績取得部３３が取得した風況データに基づいて、遠隔ポイントとその遠隔ポイントに最寄の近隣ポイントとの風況の相関関係を算出する。   The neighborhood remote correlation calculating unit 37 correlates the wind condition between the remote point and the nearest neighbor point based on the wind condition data acquired by the neighborhood point result acquiring unit 32 and the remote point result acquiring unit 33. Calculate the relationship.

具体的には、遠隔ポイントＦＰ１の風況を予測する場合、代表近隣相関関係算出部３４における処理と同様に、近隣ポイント実績取得部３２が取得した遠隔ポイントＦＰ１に最寄の近隣ポイントＮＰ１の風況データと、遠隔ポイント実績取得部３３が取得した遠隔ポイントＦＰ１の風況データとを比較して両者の相関関係である風速比βＮ１Ｆ１を算出して、近隣遠隔相関関係記憶部２２において記憶する。なお、風速比βＮ１Ｆ１は季節毎、時間帯毎に算出してもよい。また、同様に、遠隔ポイントＦＰ２〜ＦＰ７における風速とそれぞれに最寄りの近隣ポイントの風速との風速比βについても算出して、近隣遠隔相関関係記憶部２２において記憶する。 Specifically, when the wind condition of the remote point FP1 is predicted, the wind of the nearest neighbor point NP1 to the remote point FP1 acquired by the nearby point result acquisition unit 32 is the same as the processing in the representative neighborhood correlation calculation unit 34. The wind speed ratio β N1F1 which is a correlation between the situation data and the wind condition data of the remote point FP1 acquired by the remote point result acquisition unit 33 is calculated and stored in the neighborhood remote correlation storage unit 22 . The wind speed ratio β N1F1 may be calculated for each season and for each time zone. Similarly, the wind speed ratio β between the wind speed at the remote points FP2 to FP7 and the wind speed at the nearest neighboring point is also calculated and stored in the neighboring remote correlation storage unit 22.

なお、各風速比βは、各風速比αと同様、季節毎又は時間帯毎に算出し、当該季節又は当該時間帯に応じて用いることとしてもよい。また、近隣ポイント実績取得部３２及び遠隔ポイント実績取得部３３が取得する最新の風況データに基づいて、各風速比βを再算出し、近隣遠隔相関関係記憶部２２に記憶された各風速比βを随時更新することとしてもよい。   As with each wind speed ratio α, each wind speed ratio β may be calculated for each season or each time zone and used according to the season or the time zone. Further, each wind speed ratio β is recalculated based on the latest wind condition data acquired by the neighboring point result obtaining unit 32 and the remote point result obtaining unit 33, and each wind speed ratio stored in the neighboring remote correlation storage unit 22 is calculated. β may be updated as needed.

遠隔ポイント予測部３８は、近隣遠隔相関関係記憶部２２が記憶する相関関係βと、予測対象の遠隔ポイントに最寄であって近隣ポイント予測部３６が算出した近隣ポイントの風況予測の情報に基づいて、遠隔ポイントにおける風況をそれぞれ予測する。具体的には、例えば、遠隔ポイントＦＰ１の風速は、遠隔ポイントＦＰ１の最寄の近隣ポイントＮＰ１の同日同時刻の風速に風速比βＮ１Ｆ１を乗じることによって算出される。同様にして、遠隔ポイントＦＰ２〜ＦＰ７における風況についても予測することができる。 The remote point prediction unit 38 uses the correlation β stored in the neighboring remote correlation storage unit 22 and the wind condition prediction information of the neighboring point calculated by the neighboring point prediction unit 36 that is closest to the remote point to be predicted. Based on this, wind conditions at remote points are predicted. Specifically, for example, the wind speed at the remote point FP1 is calculated by multiplying the wind speed at the same day at the nearest neighboring point NP1 of the remote point FP1 by the wind speed ratio βN1F1 . Similarly, the wind conditions at the remote points FP2 to FP7 can be predicted.

図８は、風速比βのデータ構成を示す図である。同図に示す通り、近隣遠隔相関関係算出部３７が算出した各日時における各風速比βが、近隣遠隔相関関係記憶部２２に記憶されている。   FIG. 8 is a diagram illustrating a data configuration of the wind speed ratio β. As shown in the figure, each wind speed ratio β at each date and time calculated by the neighborhood remote correlation calculation unit 37 is stored in the neighborhood remote correlation storage unit 22.

＜＜システムの処理＞＞
図９は、代表ポイントＲＰにおける風況予測の情報に基づいて、代表ポイントＲＰの近隣に位置する近隣ポイントＮＰ１〜５の風況を予測する処理フローを示す図である。 << System processing >>
FIG. 9 is a diagram showing a processing flow for predicting the wind conditions of the neighboring points NP1 to NP5 located in the vicinity of the representative point RP based on the wind condition prediction information at the representative point RP.

まず、代表ポイント実績取得部３１が代表ポイントにおける過去の風況データを取得する（Ｓ９０２）。次に、近隣ポイント実績取得部３２が各近隣ポイントにおける過去の風況データを取得する（Ｓ９０４）。そして、代表近隣相関関係算出部３４が、代表ポイントにおける風況と各近隣ポイントにおける風況との相関関係αを算出し（Ｓ９０６）、その代表近隣相関関係算出部３４が算出した相関関係αを、代表近隣相関関係記憶部２１が記憶する（Ｓ９０８）。   First, the representative point result acquisition unit 31 acquires past wind condition data at the representative point (S902). Next, the neighboring point result obtaining unit 32 obtains past wind condition data at each neighboring point (S904). Then, the representative neighborhood correlation calculation unit 34 calculates the correlation α between the wind condition at the representative point and the wind condition at each neighboring point (S906), and calculates the correlation α calculated by the representative neighborhood correlation calculation unit 34. The representative neighborhood correlation storage unit 21 stores the information (S908).

一方、代表ポイント予測取得部３５は、代表ポイントにおける風況予測の情報を取得する（Ｓ９１０）。近隣ポイント予測部３６は、代表近隣相関関係記憶部２１が記憶する相関関係αと、代表ポイント予測取得部３５が取得した代表ポイントＲＰの風況予測の情報に基づいて、近隣ポイントＮＰ１〜ＮＰ５における風況を予測する（Ｓ９１２）。   On the other hand, the representative point prediction acquisition unit 35 acquires information on wind condition prediction at the representative points (S910). The neighboring point prediction unit 36 is based on the correlation α stored in the representative neighboring correlation storage unit 21 and the wind condition prediction information of the representative point RP acquired by the representative point prediction acquiring unit 35, in the neighboring points NP1 to NP5. The wind condition is predicted (S912).

このようにして、近隣ポイントＮＰ１〜ＮＰ５における風況は予測される。   In this way, the wind conditions at the neighboring points NP1 to NP5 are predicted.

図１０は、各近隣ポイントＮＰ１〜ＮＰ５における風況予測の情報に基づいて、各近隣ポイントＮＰ１〜ＮＰ５の近隣に位置する各遠隔ポイントＦＰ１〜７の風況を予測する処理フローを示す図である。   FIG. 10 is a diagram showing a processing flow for predicting the wind conditions of the remote points FP1 to FP7 located in the vicinity of the neighboring points NP1 to NP5 based on the wind condition prediction information at the neighboring points NP1 to NP5. .

まず、近隣ポイント実績取得部３２が各近隣ポイントＮＰ１〜ＮＰ５における過去の風況データを取得する（Ｓ１００２）。次に、近隣ポイント実績取得部３２が各近隣ポイントにおける過去の風況データを取得する（Ｓ１００４）。そして、近隣遠隔相関関係算出部３７が、各近隣ポイントＮＰ１〜ＮＰ５における風況と各遠隔ポイントＦＰ１〜ＦＰ７における風況との相関関係βを算出し（Ｓ１００６）、その近隣遠隔相関関係算出部３７が算出した相関関係βを、近隣遠隔相関関係記憶部２２が記憶する（Ｓ１００８）。   First, the neighboring point result obtaining unit 32 obtains past wind condition data at the neighboring points NP1 to NP5 (S1002). Next, the neighboring point result obtaining unit 32 obtains past wind condition data at each neighboring point (S1004). Then, the neighborhood remote correlation calculation unit 37 calculates the correlation β between the wind conditions at each of the neighboring points NP1 to NP5 and the wind conditions at each of the remote points FP1 to FP7 (S1006), and the neighborhood remote correlation calculation unit 37 The neighborhood remote correlation storage unit 22 stores the correlation β calculated by (S1008).

一方、近隣ポイント予測部３６は、図９に示された処理フローにより各近隣ポイントＮＰ１〜ＮＰ５における風況を予測する（Ｓ１０１０）。遠隔ポイント予測部３８は、近隣遠隔相関関係記憶部２２が記憶する相関関係βと、近隣ポイント予測部３６が算出した各近隣ポイントＮＰ１〜ＮＰ５の風況予測の情報に基づいて、遠隔ポイントＦＰ１〜ＦＰ７における風況を予測する（Ｓ１０１２）。   On the other hand, the neighboring point prediction unit 36 predicts the wind conditions at the neighboring points NP1 to NP5 by the processing flow shown in FIG. 9 (S1010). The remote point prediction unit 38 is based on the correlation β stored in the neighboring remote correlation storage unit 22 and the wind condition prediction information of the neighboring points NP1 to NP5 calculated by the neighboring point prediction unit 36. The wind condition at FP7 is predicted (S1012).

このようにして、遠隔ポイントＦＰ１〜ＦＰ７における風況は予測される。   In this way, the wind conditions at the remote points FP1 to FP7 are predicted.

以上の通り、本実施形態の風況予測システム１によれば、複数の地点における風況について、簡易でありながら精度の高い予測が可能となる。すなわち、ある１地点の精度の高い風況予測を取得できれば、それに基づいて、近隣（代表ポイントから数十ｋｍ以内）の地点に関する風況を予測することができる。また、その近隣の地点の風況予測結果に基づいて、更に遠隔（代表ポイントから約百ｋｍ以内）の地点についての風況を予測することができる。   As described above, according to the wind condition prediction system 1 of the present embodiment, the wind condition at a plurality of points can be easily predicted with high accuracy. That is, if it is possible to obtain a highly accurate wind condition prediction at a certain point, it is possible to predict a wind condition related to a nearby point (within several tens of kilometers from the representative point) based on that. Further, it is possible to predict the wind condition at a further remote point (within about 100 km from the representative point) based on the wind condition prediction result at the neighboring points.

また、代表ポイント実績取得部３１、近隣ポイント実績取得部３２及び遠隔ポイント実績取得部３３が最新の風況データを取得することにより、各風速比α及びβを再算出し、代表近隣相関関係記憶部２１に記憶された各風速比α及び近隣遠隔相関関係記憶部２２に記憶された各風速比βを随時更新することができる。これによれば、さらに精度の高い風況予測を行うことができる。   In addition, the representative point result acquiring unit 31, the neighboring point result acquiring unit 32, and the remote point result acquiring unit 33 acquire the latest wind condition data, thereby recalculating the wind speed ratios α and β, and storing the representative neighboring correlation memory. Each wind speed ratio α stored in the unit 21 and each wind speed ratio β stored in the neighborhood remote correlation storage unit 22 can be updated as needed. According to this, it is possible to perform wind condition prediction with higher accuracy.

なお、以上の実施形態の説明は、本発明の理解を容易にするためのものであり、本発明を限定するものではない。本発明はその趣旨を逸脱することなく、変更、改良され得ると共に本発明にはその等価物が含まれることは勿論である。例えば、代表ポイントＲＰの風速と近隣ポイントＮＰの風速との相関関係は、比例関係に限らず、例えば最小二乗法によって求められるものであってもよい。   In addition, the description of the above embodiment is for facilitating understanding of the present invention, and does not limit the present invention. It goes without saying that the present invention can be changed and improved without departing from the gist thereof, and that the present invention includes equivalents thereof. For example, the correlation between the wind speed of the representative point RP and the wind speed of the neighboring point NP is not limited to a proportional relationship, and may be obtained by, for example, the least square method.

本発明の一実施形態である風況予測システム１のハードウェア構成を示す図である。It is a figure which shows the hardware constitutions of the wind condition prediction system 1 which is one Embodiment of this invention. 本発明の一実施形態である風況予測システム１のブロック構成図である。It is a block block diagram of the wind condition prediction system 1 which is one Embodiment of this invention. 風況予測システム１を用いての風況予測の対象となる近隣ポイントＮＰ１〜５と、遠隔ポイントＦＰ１〜７と、その風況予測の基礎となる風況データを提供する代表ポイントＲＰの位置関係を示す概念図である。Positional relationship between the neighboring points NP1 to NP1 to 5 that are subject to wind condition prediction using the wind condition prediction system 1, the remote points FP1 to FP7, and the representative point RP that provides wind condition data that is the basis of the wind condition prediction FIG. 代表ポイント実績取得部３１において取得される風況データのデータ構成を示す図である。It is a figure which shows the data structure of the wind condition data acquired in the representative point results acquisition part 31. FIG. 近隣ポイント実績取得部３２において取得される風況データのデータ構成を示す図である。It is a figure which shows the data structure of the wind condition data acquired in the neighborhood point results acquisition part 32. FIG. 遠隔ポイント実績取得部３３において取得される風況データのデータ構成を示す図である。It is a figure which shows the data structure of the wind condition data acquired in the remote point results acquisition part 33. FIG. 代表近隣相関関係記憶部２１が記憶する風速比αのデータ構成を示す図である。It is a figure which shows the data structure of the wind speed ratio (alpha) which the representative neighborhood correlation memory | storage part 21 memorize | stores. 近隣遠隔相関関係記憶部２２が記憶する風速比βのデータ構成を示す図である。It is a figure which shows the data structure of the wind speed ratio (beta) which the neighborhood remote correlation memory | storage part 22 memorize | stores. 代表ポイントＲＰにおける風況予測の情報に基づいて、代表ポイントＲＰの近隣に位置する近隣ポイントＮＰ１〜５の風況を予測する処理フローを示す図である。It is a figure which shows the processing flow which estimates the wind condition of the neighboring points NP1-5 located in the vicinity of the representative point RP based on the information of the wind condition prediction in the representative point RP. 各近隣ポイントＮＰ１〜ＮＰ５における風況予測の情報に基づいて、各近隣ポイントＮＰ１〜ＮＰ５の近隣に位置する各遠隔ポイントＦＰ１〜７の風況を予測する処理フローを示す図である。It is a figure which shows the processing flow which estimates the wind condition of each remote point FP1-7 located in the vicinity of each neighboring point NP1-NP5 based on the information of wind condition prediction in each neighboring point NP1-NP5.

符号の説明Explanation of symbols

１ 風況予測システム
１０ ＣＰＵ
１１ メモリ
１２ 記憶手段
１３ 記録媒体読取手段
１４ 通信インターフェイス
１５ 入力手段
１６ 出力手段
１７ 記録媒体
２１ 代表近隣相関関係記憶部（第１の相関関係記憶部）
２２ 近隣遠隔相関関係記憶部（第２の相関関係記憶部）
３１ 代表ポイント実績取得部
３２ 近隣ポイント実績取得部
３３ 遠隔ポイント実績取得部
３４ 代表近隣相関関係記憶部
３５ 代表ポイント予測取得部（取得部）
３６ 近隣ポイント予測部（第１の予測部）
３７ 近隣遠隔相関関係記憶部
３８ 遠隔ポイント予測部（第２の予測部） 1 Wind condition prediction system 10 CPU
DESCRIPTION OF SYMBOLS 11 Memory 12 Storage means 13 Recording medium reading means 14 Communication interface 15 Input means 16 Output means 17 Recording medium 21 Representative neighborhood correlation memory | storage part (1st correlation memory | storage part)
22 Neighborhood remote correlation storage unit (second correlation storage unit)
31 representative point result acquisition unit 32 neighborhood point result acquisition unit 33 remote point result acquisition unit 34 representative neighborhood correlation storage unit 35 representative point prediction acquisition unit (acquisition unit)
36 Neighborhood point prediction unit (first prediction unit)
37 Neighborhood remote correlation storage unit 38 Remote point prediction unit (second prediction unit)

Claims (7)

第１の地点における風況と第２の地点における風況との相関関係を記憶する第１の相関関係記憶部と、
前記第１の地点における風況予測の情報を取得する予測取得部と、
前記第１の相関関係記憶部が記憶する相関関係と、前記予測取得部が取得した前記第１の地点における風況予測の情報とに基づいて、前記第２の地点における風況を予測する第１の予測部と、
を備えることを特徴とする風況予測システム。 A first correlation storage unit for storing a correlation between a wind condition at the first point and a wind condition at the second point;
A prediction acquisition unit for acquiring information of wind condition prediction at the first point;
Based on the correlation stored in the first correlation storage unit and the information on wind condition prediction at the first point acquired by the prediction acquisition unit, the wind condition at the second point is predicted. 1 predictor;
A wind condition prediction system characterized by comprising: 請求項１に記載の風況予測システムであって、
前記第２の地点における風況と第３の地点における風況との相関関係を記憶する第２の相関関係記憶部と、
前記第２の相関関係記憶部が記憶する相関関係と、前記第１の予測部が予測した前記第２の地点における風況の予測結果とに基づいて、前記第３の地点における風況を予測する第２の予測部と、
をさらに備えることを特徴とする風況予測システム。 The wind condition prediction system according to claim 1,
A second correlation storage unit for storing a correlation between a wind condition at the second point and a wind condition at the third point;
The wind condition at the third point is predicted based on the correlation stored in the second correlation storage unit and the prediction result of the wind condition at the second point predicted by the first prediction unit. A second predictor to
A wind condition prediction system characterized by further comprising: 請求項１又は２に記載の風況予測システムであって、
過去の所定期間における前記第１の地点の風況データを取得する第１の実績取得部と、
前記所定期間における前記第２の地点の風況データを取得する第２の実績取得部と、
をさらに備え、
前記第１の記憶部に記憶された相関関係は、前記第１の実績取得部が取得した前記所定期間における第１の地点の風況データと前記第２の実績取得部が取得した前記所定期間における第２の地点の風況データとに基づいて算出されたものであることを特徴とする風況予測システム。 The wind condition prediction system according to claim 1 or 2,
A first result acquisition unit that acquires wind condition data of the first point in a past predetermined period;
A second result acquisition unit for acquiring wind condition data of the second point in the predetermined period;
Further comprising
The correlation stored in the first storage unit includes the wind condition data at the first point in the predetermined period acquired by the first result acquisition unit and the predetermined period acquired by the second result acquisition unit. The wind condition prediction system calculated based on the wind condition data of the 2nd point in. 請求項２に記載の風況予測システムであって、
過去の所定期間における前記第２の地点の風況データを取得する第２の実績取得部と、
前記所定期間における前記第３の地点の風況データを記憶する第３の実績取得部と、
をさらに備え、
前記第２の相関関係記憶部に記憶された相関関係は、前記第２の実績取得部が取得した前記所定期間における第２の地点の風況データと前記第３の実績取得部が取得した前記所定期間における第３の地点の風況データとに基づいて算出されたものであることを特徴とする風況予測システム。 The wind condition prediction system according to claim 2,
A second result acquisition unit for acquiring wind condition data at the second point in the past predetermined period;
A third result acquisition unit for storing wind condition data at the third point in the predetermined period;
Further comprising
The correlation stored in the second correlation storage unit is obtained by the wind condition data of the second point in the predetermined period acquired by the second result acquisition unit and the third result acquisition unit acquired by the second result acquisition unit. A wind condition prediction system calculated based on wind condition data at a third point in a predetermined period. 請求項２に記載の風況予測システムであって、
過去の所定期間における前記第１の地点の風況データを取得する第１の実績取得部と、
前記所定期間における前記第２の地点の風況データを取得する第２の実績取得部と、
前記所定期間における前記第３の地点の風況データを記憶する第３の実績取得部と、
をさらに備え、
前記第１の記憶部に記憶された相関関係は、前記第１の実績取得部が取得した前記所定期間における第１の地点の風況データと前記第２の実績取得部が取得した前記所定期間における第２の地点の風況データとに基づいて算出され、
前記第２の相関関係記憶部に記憶された相関関係は、前記第２の実績取得部が取得した前記所定期間における第２の地点の風況データと前記第３の実績取得部が取得した前記所定期間における第３の地点の風況データとに基づいて算出されたものであることを特徴とする風況予測システム。 The wind condition prediction system according to claim 2,
A first result acquisition unit that acquires wind condition data of the first point in a past predetermined period;
A second result acquisition unit for acquiring wind condition data of the second point in the predetermined period;
A third result acquisition unit for storing wind condition data at the third point in the predetermined period;
Further comprising
The correlation stored in the first storage unit includes the wind condition data at the first point in the predetermined period acquired by the first result acquisition unit and the predetermined period acquired by the second result acquisition unit. Calculated based on wind condition data at the second point in
The correlation stored in the second correlation storage unit includes the wind condition data of the second point in the predetermined period acquired by the second actual result acquisition unit and the third actual result acquisition unit acquired by the third actual result acquisition unit. A wind condition prediction system calculated based on wind condition data at a third point in a predetermined period. 第１の地点における風況と第２の地点における風況との相関関係を記憶する第１の相関関係記憶部を備えるコンピュータが行う風況予測方法であって、
前記コンピュータが、
前記第１の地点における風況予測の情報を取得するステップと、
前記第１の相関関係記憶部が記憶する相関関係と、前記取得した前記第１の地点における風況予測の情報とに基づいて、前記第２の地点における風況を予測するステップと、
を実行することを特徴とする風況予測方法。 A wind condition prediction method performed by a computer including a first correlation storage unit that stores a correlation between a wind condition at a first point and a wind condition at a second point,
The computer is
Obtaining information on wind condition prediction at the first point;
Predicting the wind condition at the second point based on the correlation stored in the first correlation storage unit and the obtained wind condition prediction information at the first point;
The wind condition prediction method characterized by performing. 第１の地点における風況と第２の地点における風況との相関関係を記憶する第１の相関関係記憶部を備えるコンピュータに用いる風況予測プログラムであって、
前記コンピュータに、
前記第１の地点における風況予測の情報を取得するステップと、
前記第１の相関関係記憶部が記憶する相関関係と、前記取得した前記第１の地点における風況予測の情報とに基づいて、前記第２の地点における風況を予測するステップと、
を実行させることを特徴とする風況予測プログラム。 A wind condition prediction program used for a computer including a first correlation storage unit that stores a correlation between a wind condition at a first point and a wind condition at a second point,
In the computer,
Obtaining information on wind condition prediction at the first point;
Predicting the wind condition at the second point based on the correlation stored in the first correlation storage unit and the obtained wind condition prediction information at the first point;
A wind condition prediction program characterized by causing
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