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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/68916


    Title: 基於不同雲種之多模型短期日射量預測;Multi-model short-term solar irradiance prediction based on different cloud types
    Authors: 黃信豪;Huang,Hsin-Hao
    Contributors: 資訊工程學系
    Keywords: 太陽日射量估測;回歸模型;卡爾曼濾波;雲分類;Solar irradiance prediction;Regression Model;Kalman filter;Cloud Classification
    Date: 2015-08-05
    Issue Date: 2015-09-23 14:46:31 (UTC+8)
    Publisher: 國立中央大學
    Abstract: 隨著科技進步,能源使用的需求越來越大,其中電力能源更是不可或缺。然而隨著環保意識抬頭與永續能源發展,為了未來人類能有更好的生活品質,進而使再生能源的研究領域越來越受到重視,尤其以太陽能源最受矚目。
    因為太陽能源受到重視,其太陽日射量不穩定之特性,會造成太陽能發電電廠的消耗成本增加,故本篇論文就是依太陽能發電的電力控管需求,提出短期的太陽日射量預測機制。傳統的衛星影像可以做大範圍以及長期的預測,但是衛星影像大多以數個小時到一天作為預測單位,無法達到以分鐘為單位之精確短期預測,同時也無法針對特定的小範圍精確預測預測。為了在預測上達到更好的時間和空間解析度,全天空相機被引入作為短期預測之取像裝置。
    本篇論文提出一套短時間內日射量預測的系統架構與一套修正預測機制。本篇論文研究的日射量預測系統是以回歸模型(Regression Model)作為基礎,先將全天空影像進行雲的分類後,並搭配日射儀蒐集的資訊當作訓練特徵,根據不同的雲類別訓練出不同的回歸模型。預測時可根據當下的雲類別使用對應的模型搭配歷史資訊進行預測。預測出的結果再利用修正機制改進。修正機制是藉由卡爾曼濾波預測(Kalman Filter predictor)和預測日射量修正公式(Ramp Down Correction Function)並依照融合機制,得到最後的短期預測日射量。最後,本篇論文將用兩種不同時間的資料集來實驗,並驗證與分析。實驗顯示將全天空影像進行雲分類再進行預測,在不同的天氣狀況下,天空的雲層狀況變化差距極大,利用這樣的分類條件可以達到更好的預測效果。另外,本論文提出之修正預測機制,預測日射量修正,以及融合回歸模型和卡爾曼濾波預測模型之機制,可使預測更為準確。
    ;Renewable energy is growing quickly in the modern society. Many countries have devoted themselves to the development of renewable power. And solar energy is one of the most important renewable energy. To overcome its unstable nature and achieve better utilization, forecasting short-term solar irradiance precisely is a crucial issue. This paper proposes a short-term irradiance prediction framework that based on automatic cloud classification. The cloud types are classified according to the features extracted from all-sky images. Multiple regression models are constructed by different cloud types using historical clearness indices or irradiance values as features. Moreover, ramp-down events are detected and the predicted irradiance is corrected on ramp-down events. The amount of correction is determined by the features extracted from the all-sky images. We also design a Kalman-filter based prediction model with time-varying system matrix. Afterwards, we fuse the prediction results of the regressor and the Kalman filter predictor. Finally, we validate the proposed system with two different datasets. Experiments have shown that incorporating cloud type information can capture different characteristics of irradiance variation under different cloud types. Also, the design of time-varying system matrix is able to improve the prediction accuracy.
    Appears in Collections:[資訊工程研究所] 博碩士論文

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