IMT Institutional Repository: No conditions. Results ordered -Date Deposited. 2024-05-22T01:32:21ZEPrintshttp://eprints.imtlucca.it/images/logowhite.pnghttp://eprints.imtlucca.it/2011-08-03T13:33:34Z2012-03-05T10:25:09Zhttp://eprints.imtlucca.it/id/eprint/766This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/7662011-08-03T13:33:34ZAppliance operation scheduling for electricity consumption optimizationThis paper concerns the problem of optimally scheduling a set of appliances at the end-user premises. The user's energy fee varies over time, and moreover, in the context of smart grids, the user may receive a reward from an energy aggregator if he/she reduces consumption during certain time intervals. In a household, the problem is to decide when to schedule the operation of the appliances, in order to meet a number of goals, namely overall costs, climatic comfort level and timeliness. We devise a model accounting for a typical household user, and present computational results showing that it can be efficiently solved in real-life instances.Alessandro AgnetisGabriella Dellinogabriella.dellino@imtlucca.itPaolo DettiGianluca De PascaleGiacomo InnocentiAntonio Vicino2011-07-27T09:04:05Z2011-08-04T07:29:08Zhttp://eprints.imtlucca.it/id/eprint/554This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/5542011-07-27T09:04:05ZSet membership identification of piecewise affine modelsThis paper addresses the problem of identification of piecewise affine (PWA)models, which involves the joint estimations of both the parameters of the affine submodels and the partition of the PWA map from data. According to ideas from set-membership identification, the key approach is to characterize the model by its maximum allowed prediction error, which is used as a tuning knob for traning off between prediction accuracy and model complexity. At initialization, the proposed procedure for PWA identification exploits a technique per partitioning an infeasible system of linear inequalities into a (possibly minimum) number of feasible subsystems. This provides both an initial clustering of the datapoints and a guess of the number of required submodels, which therefore is not fixed a priori. A refinement procedure is then applied in order to improve both data classification and parameter estimation. The partition of the PWA maps is finally estimated by considering multicategory classification techniques.Alberto Bemporadalberto.bemporad@imtlucca.itAndrea GarulliSimone PaolettiAntonio Vicino2011-07-27T08:53:53Z2011-08-04T07:29:08Zhttp://eprints.imtlucca.it/id/eprint/504This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/5042011-07-27T08:53:53ZA greedy approach to identification of piecewise affine modelsThis paper addresses the problem of identification of piecewise affine (PWA) models. This problem involves the estimation from data of both the parameters of the affine submodels and the partition of the PWA map. The procedure that we propose for PWA identification exploits a greedy strategy for partitioning an infeasible system of linear inequalities into a minimum number of feasible subsystems: this provides an initial clustering of the datapoints. Then a refinement procedure is applied repeatedly to the estimated clusters in order to improve both the data classification and the parameter estimation. The partition of the PWA map is finally estimated by considering pairwise the clusters of regression vectors, and by finding a separating hyperplane for each of such pairs. We show that our procedure does not require to fix a priori the number of affine submodels, which is instead automatically estimated from the data. Alberto Bemporadalberto.bemporad@imtlucca.itAndrea GarulliSimone PaolettiAntonio Vicino2011-07-27T08:53:49Z2011-08-04T07:29:08Zhttp://eprints.imtlucca.it/id/eprint/566This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/5662011-07-27T08:53:49ZData classification and parameter estimation for the identification of piecewise affine modelsThis paper proposes a three-stage procedure for parametric identification of piece wise affine auto regressive exogenous (PWARX) models. The first stage simultaneously classifies the data points and estimates the number of submodels and the corresponding parameters by solving the MIN PFS problem (partition into a minimum number of feasible subsystems) for a set of linear complementary inequalities derived from input-output data. Then, a refinement procedure reduces misclassifications and improves parameter estimates. The last stage determines a polyhedral partition of the regressor set via two-class or multi-class linear separation techniques. As a main feature, the algorithm imposes that the identification error is bounded by a fixed quantity δ. Such a bound is a useful tuning parameter to trade off between quality of fit and model complexity. Ideas for efficiently addressing the MIN PFS problem, and for improving data classification are also discussed in the paper. The performance of the proposed identification procedure is demonstrated on experimental data from an electronic component placement process in a pick-and-place machine.Alberto Bemporadalberto.bemporad@imtlucca.itAndrea GarulliSimone PaolettiAntonio Vicino2011-07-27T08:45:11Z2011-08-04T07:29:07Zhttp://eprints.imtlucca.it/id/eprint/453This item is in the repository with the URL: http://eprints.imtlucca.it/id/eprint/4532011-07-27T08:45:11ZA bounded-error approach to piecewise affine system identificationThis paper proposes a three-stage procedure for parametric identification of piecewise affine autoregressive exogenous (PWARX) models. The first stage simultaneously classifies the data points and estimates the number of submodels and the corresponding parameters by solving the partition into a minimum number of feasible subsystems (MIN PFS) problem for a suitable set of linear complementary inequalities derived from data. Second, a refinement procedure reduces misclassifications and improves parameter estimates. The third stage determines a polyhedral partition of the regressor set via two-class or multiclass linear separation techniques. As a main feature, the algorithm imposes that the identification error is bounded by a quantity δ. Such a bound is a useful tuning parameter to trade off between quality of fit and model complexity. The performance of the proposed PWA system identification procedure is demonstrated via numerical examples and on experimental data from an electronic component placement process in a pick-and-place machine.Alberto Bemporadalberto.bemporad@imtlucca.itAndrea GarulliSimone PaolettiAntonio Vicino