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The short-term load forecasting problem is addressed by means of a power regression approach. Exploiting the highly correlated nature of the explanatory variables, just two loads are deemed sufficiently informative for prediction purposes: one day before and one week before. The notion of “similar day” is then used to extract a meaningful training set from the historical records. The presence of a...
In the last decade, improvements in diabetes daily management have become possible thanks to the development of minimally-invasive portable sensors which allow continuous glucose monitoring (CGM) for several days. In particular, hypo and hyperglycemia can be promptly detected when glucose exceeds the normal range thresholds, and even avoided through the use of on-line glucose prediction algorithms...
In this paper we propose a new nonparametric approach to identification of linear time invariant systems using subspace methods. The nonparametric paradigm to prediction of stationary stochastic processes, developed in a companion paper, is integrated into a recently proposed subspace method. Simulation results show that this approach significantly improves over standard subspace methods when using...
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