An Online Strategy to Improve Energy Efficiency through Optimisation


An Online Strategy to Improve Energy Efficiency through Optimisation – Despite the huge growth in renewable energy generation in the last two decades, current solar thermal generation is still the world-leading renewable energy generation. Many problems associated with existing solar thermal generation have to be addressed to make the situation more beneficial, since it is extremely difficult to forecast a temperature change of the sun for the whole solar system, especially for the first few years. In this work, we propose a novel online strategy to improve the energy efficiency of solar thermal generation. The proposed strategy is based on an algorithm called Temporal Sorting, i.e., the task of locating the keypoints in an optimal sequence of events, i.e., the temporal order which occurs when each event is in range of two adjacent events. The keypoint is the location of the most important event in the sequence of events, which we call the Temporal Sorting algorithm. We demonstrate how the Temporal Sorting is a useful tool for a specific type of solar thermal generation, namely, a multi-temperature solar thermal system.

We propose a simple, elegant, and efficient way of learning to compute a high-level representation of an object instance. We train and evaluate several existing methods for object instance computation in both high-level and low-level representations, such as object representation learning and object instance segmentation. Our approach, named as Part-of-Class Representation Deep Learning (P3DRL), uses an information-theoretic framework to learn low-level object instance representations using object instance descriptors in a deep neural network. P3DRL significantly outperforms a variety of state-of-the-art CNN-based approaches in achieving state-of-the-art performance in object instance annotation on both high-level and low-level labels. We show that the proposed algorithm can effectively handle a variety of object instances, providing the ability to learn the object instances and their attributes at a scale of one, and a bounding box.

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An Online Strategy to Improve Energy Efficiency through Optimisation

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  • Improving Deep Generative Models for Classification via Hough Embedding

    Multi-Instance Dictionary Learning for Classification and SegmentationWe propose a simple, elegant, and efficient way of learning to compute a high-level representation of an object instance. We train and evaluate several existing methods for object instance computation in both high-level and low-level representations, such as object representation learning and object instance segmentation. Our approach, named as Part-of-Class Representation Deep Learning (P3DRL), uses an information-theoretic framework to learn low-level object instance representations using object instance descriptors in a deep neural network. P3DRL significantly outperforms a variety of state-of-the-art CNN-based approaches in achieving state-of-the-art performance in object instance annotation on both high-level and low-level labels. We show that the proposed algorithm can effectively handle a variety of object instances, providing the ability to learn the object instances and their attributes at a scale of one, and a bounding box.


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