Alicia Troncoso

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Evaluating in Massive Open Online Courses (MOOCs) is a difficult task because of the huge number of students involved in the courses. Peer grading is an effective method to cope with this problem, but something must be done to lessen the effect of the subjective evaluation. In this paper we present a matrix factorization approach able to learn from the(More)
Data mining has become an essential tool during the last decade to analyze large sets of data. The variety of techniques it includes and the successful results obtained in many application fields, make this family of approaches powerful and widely used. In particular, this work explores the application of these techniques to time series forecasting.(More)
With increasing importance being attached to big data mining, analysis, and forecasting in the field of wind energy, how to select an optimization model to improve the forecasting accuracy of the wind speed time series is not only an extremely challenging problem, but also a problem of concern for economic forecasting. The artificial intelligence model is(More)
Solar energy generated from PhotoVoltaic (PV) systems is one of the most promising types of renewable energy. However, it is highly variable as it depends on the solar irradiance and other meteorological factors. This variability creates difficulties for the large-scale integration of PV power in the electricity grid and requires accurate forecasting of the(More)
Battery grouping is a technology widely used to improve the performance of battery packs. In this paper, we propose a time series clustering based battery grouping method. The proposed method utilizes the whole battery charge/discharge sequence for battery grouping. The time sequences are first denoised with a wavelet denoising technique. The similarity(More)
This paper addresses the problem of energy consumption prediction using neural networks over a set of public buildings. Since energy consumption in the public sector comprises a substantial share of overall consumption, the prediction of such consumption represents a decisive issue in the achievement of energy savings. In our experiments, we use the data(More)
This work evaluates artificial neural networks’ accuracy when used to predict earthquakes magnitude in Tokyo. Several seismicity indicators have been retrieved from the literature and used as input for the networks. Some of them have been improved and parameterized in order to extract more valuable knowledge from datasets. The experimental set-up includes(More)
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