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    Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/65620


    Title: 基於回歸模型與利用全天空影像特徵和歷史資訊之短期日射量預測;Short-term Solar Irradiance Forecasting Based on Regression Model using All-Sky Image Features and Historical Data
    Authors: 林賢勁;Lin,Sian-jing
    Contributors: 資訊工程學系
    Keywords: 短期日射量預測;全天空影像;回歸模型;預測修正;Solar irradiance prediction;All-sky image;Regression model;Correction
    Date: 2014-07-16
    Issue Date: 2014-10-15 17:06:25 (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,並搭配全天空影像與日射儀蒐集的資訊,當作訓練特徵,建立回歸模型。而預測模型又分為遮蔽模型與明朗模型。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,本篇論文將用具挑戰性的資料集來實驗,並驗證與分析。實驗顯示搭配全天空影像所抽取之特徵,可以達到更好的預測效果。並且本論文提出之雙模型預測較單模型預測可有效降低預測誤差。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。
    ;For the qualities of life and sustainable development in the future, renewable power draws much attention 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 take advantage of solar energy effectively, integrated and large scale photovoltaic systems need to overcome the unstable nature of solar resource.
    This thesis proposes a short-term irradiance prediction framework that uses regression models. The prediction model is constructed by all-sky image features and historical data which is clearness index or irradiance. Two models are constructed based on the occlusion condition near the solar disk. 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 compare the prediction results of regression models with a Kalman filter predictor.
    Afterwards, we fuse the prediction results of the regressor and the Kalman filter predictor. Finally, we validate the proposed system using challenging experimental datasets.
    Appears in Collections:[資訊工程研究所] 博碩士論文

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