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Found 10 papers, 1 papers with code
An Online Hierarchical Energy Management System for Energy Communities, Complying with the Current Technical Legislation Framework
In 2018, with proper legislation, the European Union (EU) defined the Renewable Energy Community (REC) as a local electrical grid whose participants share their self-produced renewable energy, aiming at reducing bill costs by taking advantage of proper incentives.
An overview and comparative analysis of Recurrent Neural Networks for Short Term Load Forecasting
Recently, new important families of recurrent architectures have emerged and their applicability in the context of load forecasting has not been investigated completely yet.
A Hierarchical Genetic Optimization of a Fuzzy Logic System for Flow Control in Micro Grids
In particular, this study focuses on the application of a Hierarchical Genetic Algorithm (HGA) for tuning the Rule Base (RB) of a Fuzzy Inference System (FIS), trying to discover a minimal fuzzy rules set in a Fuzzy Logic Controller (FLC) adopted to perform decision making in the microgrid.
Data-driven detrending of nonstationary fractal time series with echo state networks
We assume that such a dynamical process is predictable to a certain degree by means of a class of recurrent networks called Echo State Network (ESN), which are capable to model a generic dynamical process.
On the impact of topological properties of smart grids in power losses optimization problems
In this paper, we face the problem of joint optimization of both topology and network parameters in a real smart grid.
Building pattern recognition applications with the SPARE library
This paper presents the SPARE C++ library, an open source software tool conceived to build pattern recognition and soft computing systems.
An Agent-Based Algorithm exploiting Multiple Local Dissimilarities for Clusters Mining and Knowledge Discovery
We propose a multi-agent algorithm able to automatically discover relevant regularities in a given dataset, determining at the same time the set of configurations of the adopted parametric dissimilarity measure yielding compact and separated clusters.
Classifying sequences by the optimized dissimilarity space embedding approach: a case study on the solubility analysis of the E. coli proteome
We evaluate a version of the recently-proposed classification system named Optimized Dissimilarity Space Embedding (ODSE) that operates in the input space of sequences of generic objects.
Toward a multilevel representation of protein molecules: comparative approaches to the aggregation/folding propensity problem
This paper builds upon the fundamental work of Niwa et al. [34], which provides the unique possibility to analyze the relative aggregation/folding propensity of the elements of the entire Escherichia coli (E. coli) proteome in a cell-free standardized microenvironment.
Modeling and Recognition of Smart Grid Faults by a Combined Approach of Dissimilarity Learning and One-Class Classification
Modern electric power grids (smart grids) are equipped with smart sensors that allow to gather real-time information regarding the physical status of all the component elements belonging to the whole infrastructure (e. g., cables and related insulation, transformers, breakers and so on).
