Consensus-based approach to peer-to-peer electricity markets with product differentiation

With the sustained deployment of distributed generation capacities and the more proactive role of consumers, power systems and their operation are drifting away from a conventional top-down hierarchical structure. Electricity market structures, however, have not yet embraced that evolution. Respecting the high-dimensional, distributed and dynamic nature of modern power systems would translate to designing peer-to-peer markets or, at least, to using such an underlying decentralized structure to enable a bottom-up approach to future electricity markets. A peer-to-peer market structure based on a Multi-Bilateral Economic Dispatch (MBED) formulation is introduced, allowing for multi-bilateral trading with product differentiation, for instance based on consumer preferences. A Relaxed Consensus+Innovation (RCI) approach is described to solve the MBED in fully decentralized manner. A set of realistic case studies and their analysis allow us showing that such peer-to-peer market structures can effectively yield market outcomes that are different from centralized market structures and optimal in terms of respecting consumers preferences while maximizing social welfare. Additionally, the RCI solving approach allows for a fully decentralized market clearing which converges with a negligible optimality gap, with a limited amount of information being shared.Comment: Accepted for publication in IEEE Transactions on Power System

Consecutive Sequential Probability Ratio Tests of Multiple Statistical Hypotheses

In this paper, we develop a simple approach for testing multiple statistical hypotheses based on the observations of a number of probability ratios enumerated consecutively with respect to the index of hypotheses. Explicit and tight bounds for the probability of making wrong decisions are obtained for choosing appropriate parameters for the proposed tests. In the special case of testing two hypotheses, our tests reduce to Wald's sequential probability ratio tests.Comment: 29 pages, no figure; The main results of this paper have appeared in Proceedings of SPIE Conferences, Baltimore, Maryland, April 24-27, 201

Parameter Tuning Using Gaussian Processes

Most machine learning algorithms require us to set up their parameter values before applying these algorithms to solve problems. Appropriate parameter settings will bring good performance while inappropriate parameter settings generally result in poor modelling. Hence, it is necessary to acquire the “best” parameter values for a particular algorithm before building the model. The “best” model not only reflects the “real” function and is well fitted to existing points, but also gives good performance when making predictions for new points with previously unseen values. A number of methods exist that have been proposed to optimize parameter values. The basic idea of all such methods is a trial-and-error process whereas the work presented in this thesis employs Gaussian process (GP) regression to optimize the parameter values of a given machine learning algorithm. In this thesis, we consider the optimization of only two-parameter learning algorithms. All the possible parameter values are specified in a 2-dimensional grid in this work. To avoid brute-force search, Gaussian Process Optimization (GPO) makes use of “expected improvement” to pick useful points rather than validating every point of the grid step by step. The point with the highest expected improvement is evaluated using cross-validation and the resulting data point is added to the training set for the Gaussian process model. This process is repeated until a stopping criterion is met. The final model is built using the learning algorithm based on the best parameter values identified in this process. In order to test the effectiveness of this optimization method on regression and classification problems, we use it to optimize parameters of some well-known machine learning algorithms, such as decision tree learning, support vector machines and boosting with trees. Through the analysis of experimental results obtained on datasets from the UCI repository, we find that the GPO algorithm yields competitive performance compared with a brute-force approach, while exhibiting a distinct advantage in terms of training time and number of cross-validation runs. Overall, the GPO method is a promising method for the optimization of parameter values in machine learning

Basics of Feature Selection and Statistical Learning for High Energy Physics

This document introduces basics in data preparation, feature selection and learning basics for high energy physics tasks. The emphasis is on feature selection by principal component analysis, information gain and significance measures for features. As examples for basic statistical learning algorithms, the maximum a posteriori and maximum likelihood classifiers are shown. Furthermore, a simple rule based classification as a means for automated cut finding is introduced. Finally two toolboxes for the application of statistical learning techniques are introduced.Comment: 12 pages, 8 figures. Part of the proceedings of the Track 'Computational Intelligence for HEP Data Analysis' at iCSC 200

Rule Induction by EDA with Instance-Subpopulations

In this paper, a new rule induction method by using EDA with instance-subpopulations is proposed. The proposed method introduces a notion of instance-subpopulation, where a set of individuals matching a training instance. Then, EDA procedure is separately carried out for each instance-subpopulation. Individuals generated by each EDA procedure are merged to constitute the population at the next generation. We examined the proposed method on Breast-cancer in Wisconsin and Chess End-Game. The comparisons with other algorithms show the effectiveness of the proposed method

STRATEGY MANAGEMENT IN A MULTI-AGENT SYSTEM USING NEURAL NETWORKS FOR INDUCTIVE AND EXPERIENCE-BASED LEARNING

Intelligent agents and multi-agent systems prove to be a promising paradigm for solving problems in a distributed, cooperative way. Neural networks are a classical solution for ensuring the learning ability of agents. In this paper, we analyse a multi-agent system where agents use different training algorithms and different topologies for their neural networks, which they use to solve classification and regression problems provided by a user. Out of the three training algorithms under investigation, Backpropagation, Quickprop and Rprop, the first demonstrates inferior performance to the other two when considered in isolation. However, by optimizing the strategy of accepting or rejecting tasks, Backpropagation agents succeed in outperforming the other types of agents in terms of the total utility gained. This strategy is learned also with a neural network, by processing the results of past experiences. Therefore, we show a way in which agents can use neural network models for both external purposes and internal ones.agents, learning, neural networks, strategy management multi-agent system.