全天空影像之雲追蹤與太陽遮蔽預測;Tracking Clouds and Predicting occlusion of Sun in All-Sky Images

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题名:	全天空影像之雲追蹤與太陽遮蔽預測;Tracking Clouds and Predicting occlusion of Sun in All-Sky Images
作者:	張財誠;Chang,Tsai-cheng
贡献者:	資訊工程學系
关键词:	全天空影像;雲追蹤;All Sky Image;Cloud Tracking
日期:	2014-08-12
上传时间:	2014-10-15 17:09:47 (UTC+8)
出版者:	國立中央大學
摘要:	近年來，由於過度使用石化能源，其排放的含碳廢氣，造成全球氣候的變遷與暖化。為了減少含碳化合物的排放，世界各國致力於發展綠色能源(如太陽能發電、風力發電、潮汐發電……等)與提高能源使用的效率。而太陽能發電主要是藉著將太陽照度轉化成電能，其照度會隨著季節、時間、雲的遮蔽、氣候等因素所影響，把這些不穩定因素加入太陽能發電系統後，也可能造成系統在可靠度上的疑慮，進而衍生出太陽照度預測的重要性。本研究希望透過影像處理的技術，對全天空影像進行分析，預測雲朵在後續的時間內的移動是否會遮蔽住太陽？藉以降低“雲的遮蔽”在太陽照度的預測上的不穩定性。本研究首先會讀取連續的全天空影像，偵測影像中雲的區域當作遮罩，利用連續影像相減法，取得影像中有移動的部分，再透過遮罩可擷取出影像中雲所移動的區域。利用雲所移動的區域，來擷取局部特徵點，並根據雲的區域遮罩來做分群，分完群的各群會被視為是獨立的雲。接著對連續影像中的各群做追蹤，保留追蹤成功的群組。透過對保留群組中的資訊做計算可取得特徵向量，使用機器學習的方法對特徵向量進行訓練，最後利用訓練得到的模組對其他的連續影像進行預測，並將預測的結果和實際影像情況作比對及分析。 ;In recent years, due to the excessive use of the fossil energy, carbon emissions have caused the global climate warming. In order to reduce the carbon emissions, countries around the world committed to the development of green energy which includes solar power, wind power and hydropower. In Taiwan, research of the solar power gets more attention gradually. But the solar irradiance would change dramatically due to season, time, weather and occlusion of clouds. These factors may cause the worries on the reliability of the solar power system. And forecasting short-term irradiance is important for the operators to manage and allocate resources. In our research, we use the image processing technology to analyze the all-sky images, and use the analysis results to predict the occlusive situation between sun and clouds. The prediction would help increase the reliability of the short-term solar irradiance forecasting. In our research, we read the all-sky images and detect the area of clouds in the images as a mask first. Then, we use the image difference to get the motion region. Applying the cloud mask to motion region, we can get the cloud motion region in the images. Afterwards, we use the cloud motion region to detect the feature points. Then the feature points will be clustered by a clustering algorithm. After obtaining the clustering results, we perform tracking of feature clusters in continuous images. After tracking, we use the tracking information to calculate the feature vector. Then, we use this vector to train the predictive model. Finally, we do the prediction and validate the results with the ground truth. And we get a good performance that the prediction accuracy is higher than 85%.
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