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Energy demand has grown explosively in recent years, leading to increased attention of energy efficiency (EE) research. Demand response (DR) programs were designed to help power management entities meet energy balance and change end-user electricity usage. Advanced real-time meters (RTM) collect a large amount of fine-granular electric consumption data, which contain valuable information. Understanding the energy consumption patterns for different end users can support demand side management (DSM). This study proposed clustering algorithms to segment consumers and obtain the representative load patterns based on diurnal load profiles. First, the proposed method uses discrete wavelet transform (DWT) to extract features from daily electricity consumption data. Second, the extracted features are reconstructed using a statistical method, combined with Pearson’s correlation coefficient and principal component analysis (PCA) for dimensionality reduction. Lastly, three clustering algorithms are employed to segment daily load curves and select the most appropriate algorithm. We experimented our method on the Manhattan dataset and the results indicated that clustering algorithms, combined with discrete wavelet transform, improve the clustering performance. Additionally, we discussed the clustering result and load pattern analysis of the dataset with respect to the electricity pattern.

The problem of determining the position and orientation of a mobile robot has been addressed by several researchers using sensors of different modalities including video cameras. Invariably, all the vision based approaches for robot localization consider that the camera is mounted on the robot and that the robot working environment is assumed to contain prominent landmarks at known locations. In this paper we propose a robot localization scheme where the robot itself serves as the landmark for cameras that are positioned in the environment to cover the entire work area of the robot. Although the proposed approach is applicable for the robots of any regular shape, we develop the solution to the localization problem by assuming a cylindrical shape for the robot. A complete mathematical analysis of the localization problem is given by extending the three-dimensional structure-from-rotational motion approach to the present task. We also examine the implementation issue of the proposed approach and present experimental results to show its effectiveness.

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