基於 BIM 的屋頂光伏產量預測分析;BIM-based analysis of roof-top PV power generation prediction

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Please use this identifier to cite or link to this item: http://ir.lib.ncu.edu.tw/handle/987654321/87075

Title:	基於 BIM 的屋頂光伏產量預測分析;BIM-based analysis of roof-top PV power generation prediction
Authors:	王蘇傑;Wang, Su-Jie
Contributors:	土木工程學系
Keywords:	建築資訊模型;太陽能發電;光伏產量預測;回歸模型;Building Information Modeling;Solar energy generation;Photovoltaic (PV) power generation prediction;Regression model
Date:	2021-07-19
Issue Date:	2021-12-07 14:52:55 (UTC+8)
Publisher:	國立中央大學
Abstract:	在全球氣候變暖及綠色能源興起的背景下，太陽能作?首屈一指的清潔能源，具有分佈廣泛的優勢，由於很多已開發地區的土地資源有限，所以目前建築整合太陽能已經成?都市地區發展太陽光電的方向。但是都市發展太陽能需要考慮到都市複雜的環境，例如臨棟高樓、樹木的影響，其中最為重要的臨棟陰影對屋頂光伏發電裝置的影響。因此，本研究將基於 BIM 模型，以城市環境為考量，對屋頂的光伏產量做預測。首先是基於遮擋，考慮到不同的遮擋情形，參數方面選取遮擋物的方位、間距及高差，盡可能的去模擬真實狀況。接著考慮到不同的地理氣候對城市的影響，例如溫度高低、日照時間長短等，應搜集具備不同的地理氣候特徵的城市資料，去模擬的光伏產量。最後對整理後的資料做多元線性回歸，建立預測模型。;Based on global warming and the rise of green energy, solar energy becomes the leading clean energy source which results in spreading out widely. Since the limited resources in many developed areas, building-integrated photovoltaics (BIPV) has become the objective of solar power development in urban areas. However, the development of solar energy in urban areas requires considerations of the complicated environment, such as the impact of adjoining high buildings, trees, and the shadow of the adjoining buildings influencing the rooftop photovoltaic power generation device the most. Therefore, this study employs Building Information Modeling (BIM) and takes the urban environments into account to predict the PV production on roofs. First, considering different occlusion situations, the orientation, spacing, and height difference of the occlusion objects are selected as parameters to simulate the real situation as much as possible. Then, considering the impact of different geographical climates on cities, such as temperature, sunshine duration, etc., the data of different geographical and climatic characteristics to simulate the photovoltaic output is needed. Finally, performing multiple linear regression on the collated data is to build a predictive model.
Appears in Collections:	[Graduate Institute of Civil Engineering] Electronic Thesis & Dissertation

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