Feature Construction and Calibration for Clustering Daily Load Curves from Smart-Meter Data
نویسندگان
چکیده
منابع مشابه
Load Hiding to Preserve Privacy from Smart Meter Measurements
Load profiles generated by smart meters represent a privacy concern when analyzed by Non-Invasive Load Monitoring algorithms. Past research in preserving privacy and countering these algorithms has negated the benefits provided by smart meters. This research seeks to protect consumer privacy while maintaining the benefits of smart meters. The method proposed achieves this through the addition o...
متن کاملClustering of Smart Meter Data for Data Compression and Fast Power Flow Computation
The amount of data generated by smart meters is a challenge for storage, but also for computation. In order to derive meaningful knowledge from the recorded data, simulations must be able to use it simply and effectively. This paper presents an algorithm that uses agglomerative hierarchical clustering to compress smart meter data and optimally prepares it for use in Monte Carlo simulations, whi...
متن کاملForecasting Daily Electricity Load Curves
Short term electricity load forecasting is a well-known problem, and many neural computing approaches for solving it have been proposed in recent years. In this paper, we argue in favour of its decomposition into two subproblems, and propose a solution for one of them: the prediction, en bloc, of the daily load profile, or configuration, for the different hours of a particular future date. From...
متن کاملOptimal Feature Selection for Data Classification and Clustering: Techniques and Guidelines
In this paper, principles and existing feature selection methods for classifying and clustering data be introduced. To that end, categorizing frameworks for finding selected subsets, namely, search-based and non-search based procedures as well as evaluation criteria and data mining tasks are discussed. In the following, a platform is developed as an intermediate step toward developing an intell...
متن کاملOptimal Feature Selection for Data Classification and Clustering: Techniques and Guidelines
In this paper, principles and existing feature selection methods for classifying and clustering data be introduced. To that end, categorizing frameworks for finding selected subsets, namely, search-based and non-search based procedures as well as evaluation criteria and data mining tasks are discussed. In the following, a platform is developed as an intermediate step toward developing an intell...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Transactions on Industrial Informatics
سال: 2016
ISSN: 1551-3203,1941-0050
DOI: 10.1109/tii.2016.2528819