Picture for Antonello Rizzi

Antonello Rizzi

An Online Hierarchical Energy Management System for Energy Communities, Complying with the Current Technical Legislation Framework

Add code
Jan 22, 2024
Viaarxiv icon

An overview and comparative analysis of Recurrent Neural Networks for Short Term Load Forecasting

Add code
Jul 20, 2018
Figure 1 for An overview and comparative analysis of Recurrent Neural Networks for Short Term Load Forecasting
Figure 2 for An overview and comparative analysis of Recurrent Neural Networks for Short Term Load Forecasting
Figure 3 for An overview and comparative analysis of Recurrent Neural Networks for Short Term Load Forecasting
Figure 4 for An overview and comparative analysis of Recurrent Neural Networks for Short Term Load Forecasting
Viaarxiv icon

A Hierarchical Genetic Optimization of a Fuzzy Logic System for Flow Control in Micro Grids

Add code
Mar 01, 2017
Figure 1 for A Hierarchical Genetic Optimization of a Fuzzy Logic System for Flow Control in Micro Grids
Figure 2 for A Hierarchical Genetic Optimization of a Fuzzy Logic System for Flow Control in Micro Grids
Figure 3 for A Hierarchical Genetic Optimization of a Fuzzy Logic System for Flow Control in Micro Grids
Figure 4 for A Hierarchical Genetic Optimization of a Fuzzy Logic System for Flow Control in Micro Grids
Viaarxiv icon

Data-driven detrending of nonstationary fractal time series with echo state networks

Add code
Oct 03, 2016
Figure 1 for Data-driven detrending of nonstationary fractal time series with echo state networks
Figure 2 for Data-driven detrending of nonstationary fractal time series with echo state networks
Figure 3 for Data-driven detrending of nonstationary fractal time series with echo state networks
Figure 4 for Data-driven detrending of nonstationary fractal time series with echo state networks
Viaarxiv icon

Toward a multilevel representation of protein molecules: comparative approaches to the aggregation/folding propensity problem

Add code
Apr 30, 2015
Figure 1 for Toward a multilevel representation of protein molecules: comparative approaches to the aggregation/folding propensity problem
Figure 2 for Toward a multilevel representation of protein molecules: comparative approaches to the aggregation/folding propensity problem
Figure 3 for Toward a multilevel representation of protein molecules: comparative approaches to the aggregation/folding propensity problem
Figure 4 for Toward a multilevel representation of protein molecules: comparative approaches to the aggregation/folding propensity problem
Viaarxiv icon

Building pattern recognition applications with the SPARE library

Add code
Feb 20, 2015
Figure 1 for Building pattern recognition applications with the SPARE library
Figure 2 for Building pattern recognition applications with the SPARE library
Figure 3 for Building pattern recognition applications with the SPARE library
Figure 4 for Building pattern recognition applications with the SPARE library
Viaarxiv icon

On the impact of topological properties of smart grids in power losses optimization problems

Add code
Jan 21, 2015
Figure 1 for On the impact of topological properties of smart grids in power losses optimization problems
Figure 2 for On the impact of topological properties of smart grids in power losses optimization problems
Figure 3 for On the impact of topological properties of smart grids in power losses optimization problems
Figure 4 for On the impact of topological properties of smart grids in power losses optimization problems
Viaarxiv icon

Classifying sequences by the optimized dissimilarity space embedding approach: a case study on the solubility analysis of the E. coli proteome

Add code
Jan 14, 2015
Figure 1 for Classifying sequences by the optimized dissimilarity space embedding approach: a case study on the solubility analysis of the E. coli proteome
Figure 2 for Classifying sequences by the optimized dissimilarity space embedding approach: a case study on the solubility analysis of the E. coli proteome
Figure 3 for Classifying sequences by the optimized dissimilarity space embedding approach: a case study on the solubility analysis of the E. coli proteome
Figure 4 for Classifying sequences by the optimized dissimilarity space embedding approach: a case study on the solubility analysis of the E. coli proteome
Viaarxiv icon

Modeling and Recognition of Smart Grid Faults by a Combined Approach of Dissimilarity Learning and One-Class Classification

Add code
Dec 17, 2014
Figure 1 for Modeling and Recognition of Smart Grid Faults by a Combined Approach of Dissimilarity Learning and One-Class Classification
Figure 2 for Modeling and Recognition of Smart Grid Faults by a Combined Approach of Dissimilarity Learning and One-Class Classification
Figure 3 for Modeling and Recognition of Smart Grid Faults by a Combined Approach of Dissimilarity Learning and One-Class Classification
Figure 4 for Modeling and Recognition of Smart Grid Faults by a Combined Approach of Dissimilarity Learning and One-Class Classification
Viaarxiv icon

An Agent-Based Algorithm exploiting Multiple Local Dissimilarities for Clusters Mining and Knowledge Discovery

Add code
Sep 17, 2014
Figure 1 for An Agent-Based Algorithm exploiting Multiple Local Dissimilarities for Clusters Mining and Knowledge Discovery
Figure 2 for An Agent-Based Algorithm exploiting Multiple Local Dissimilarities for Clusters Mining and Knowledge Discovery
Figure 3 for An Agent-Based Algorithm exploiting Multiple Local Dissimilarities for Clusters Mining and Knowledge Discovery
Figure 4 for An Agent-Based Algorithm exploiting Multiple Local Dissimilarities for Clusters Mining and Knowledge Discovery
Viaarxiv icon