AutoMoG: Automated data-driven Model Generation of multi-energy systems using piecewise-linear regression

Forecast generation model of municipal solid waste using multiple linear regression

The objective of this study was to develop a forecast model to determine the rate of generation of municipal solid waste in the municipalities of the Cuenca del Cañón del Sumidero, Chiapas, Mexico. Multiple linear regression was used with social and demographic explanatory variables. The compiled database consisted of 9 variables with 118 specific data per variable, which were analyzed using a ...

Piecewise Constant Controlled Linear Fuzzy Systems

Model predictive control of nonlinear systems using piecewise linear models

We consider the problem of controlling nonlinear systems which are modeled as a set of piecewise linear (PL) or affine systems using model predictive control (MPC). The paper reviews recent results on the analysis and control of PL systems, which can model a wide range of practically relevant nonlinear systems. Using techniques from the theory of linear matrix inequalities (LMIs), we develop a ...

Automatic Generation of a Multi Agent System for Crisis Management by a Model Driven Approach

Considering the increasing occurrences of unexpected events and the need for pre-crisis planning in order to reduce risks and losses, modeling instant response environments is needed more than ever. Modeling may lead to more careful planning for crisis-response operations, such as team formation, task assignment, and doing the task by teams. A common challenge in this way is that the model shou...

Piecewise Linear Regression for Massive Data through Dataspheres

We propose enhancing the tting of linear regression models to massive multidimensional data by partitioning the data using a DataSphere (proposed in our previous work) and tting piecewise linear regression models to each class in the representation. Nonlinear models typically involve several iterations through the data and require the knowledge of every data point. Linear regression models, on ...