Browsing by Author "Tuominen, Pekko"

Forecasting of solar power energy production would benefit from accurate sky condition predictions since the presence of clouds is a primary variable effecting the amount of radiation reaching the ground. Unfortunately the spatial and temporal resolution of often used satellite images and numerical weather prediction models can be too small for local, intra-hour estimations. Instead, digital sky images taken from the ground are used as data in this thesis. The two main building blocks needed to make sky condition forecasts are reliable cloud segmentation and cloud movement detection. The cloud segmentation problem is solved using neural networks, a double exposure imaging scheme, automatic sun locationing and a novel method to study the circumsolar region directly without the use of a sun occluder. Two different methods are studied for motion detection. Namely, a block matching method using cross-correlation as the similarity measure and the Lukas-Kanade method. The results chapter shows how neural networks overcome many of the situations labelled as difficult for other methods in the literature. Also, results by the two motion detection methods are presented and analysed. The use of neural networks and the Lukas-Kanade method show much promise for forming the cornerstone of local, intra-hour sky condition now-casting and prediction.