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The ongoing deployment of residential smart meters in numerous jurisdictions has led to an influx of electricity consumption data. This information presents a valuable opportunity to suppliers for better understanding their customer base and designing more effective tariff structures. In the past, various clustering methods have been proposed for meaningful customer partitioning. This paper presents...
In order to extract fresh knowledge out of the data present in a data warehouse, a wide range of knowledge discovery techniques have been provided that process the data in multiple passes. But nowadays, we are facing a challenge of handling massive data in a proper and timely manner so as to extract useful information (knowledge) from streaming data. Such massive streaming data cannot be stored in...
There is a general consensus that electricity price forecasting is an important task nowadays since power market players are interested in the maximization of profit and the minimization of risk. In this context, this paper proposes the use of data-mining techniques to predict the short-term electricity price in the Brazilian market. In Brazil, the market model adopted has unique characteristics with...
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