Operational Planning of Thermal Generators with Factored Markov Decision Process Models

We describe a method for creating conditional plans for controllable thermal power generators operating together with uncontrollable renewable power generators, under significant uncertainty in demand and output. The resulting stochastic sequential decision problem has mixed discrete and continuous state variables and dynamics, and we propose a discretization method for the continuous part of the model that unifies all variables into a large discrete Markov decision process model. Although this model is way too large to be solved directly, its state transition probabilities can be factored efficiently, and a reduction of all continuous variables to one net demand variable makes it tractable by dynamic programming over a suitably constructed AND/OR tree. The proposed algorithm outperformed existing non-stochastic solvers on several problem instances, resulting in both lower risks and operational costs

Univariate Short-Term Prediction of Road Travel Times

This paper presents an experimental comparison of several statistical machine learning methods for short-term prediction of travel times on road segments. The comparison includes linear regression, neural networks, regression trees, k-nearest neighbors, and locally-weighted regression, tested on the same historical data. In spite of the expected superiority of non-linear methods over linear regression, the only non-linear method that could consistently outperform linear regression was locally-weighted regression. This suggests that novel iterative linear regression algorithms should be a preferred prediction methods for large-scale travel time prediction

Mitsubishi Electric Research Laboratories

This paper presents an experimental comparison of several statistical machine learning methods for short-term prediction of travel times on road segments. The comparison includes linear regression, neural networks, regression trees, k-nearest neighbors, and locally-weighted regression, tested on the same historical data. In spite of the expected superiority of non-linear methods over linear regression, the only non-linear method that could consistently outperform linear regression was locally-weighted regression. This suggests that novel iterative linear regression algorithms should be a preferred prediction method for large-scale travel time prediction

Construction of large-scale Bayesian networks by local to global search

Most existing algorithms for structural learning of Bayesian networks are suitable for constructing small-sized networks which consist of several tens of nodes. In this paper, we present a novel approach to the efficient and relatively-precise induction of large-scale Bayesian networks with up to several hundreds of nodes. The approach is based on the concept of Markov blanket and makes use of the divide-and-conquer principle. The proposed method has been evaluated on two benchmark datasets and a real-life DNA microarray data, demonstrating the ability to learn the large-scale Bayesian network structure efficiently