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Prof. Marco Mussetta
Politecnico di Milano, Dipartimento di Energia

Basic Info


Research Keywords & Expertise

0 Antennas
0 Computational Intelligence
0 Evolutionary Computation
0 Fuzzy Logic
0 Neural Networks

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optimization,
Antennas
Neural Networks
Fuzzy Logic
machine learning
Computational Intelligence
reflectarrays
Evolutionary Computation
Wind Energy

Honors and Awards

Best Conference Paper Award

Neural networks as decision making support system for hydroelectric power plant

IEEE SST 2017




Career Timeline

Politecnico di Milano

University Educator/Researcher

01 March 2011 - 31 August 2021


Politecnico di Torino

Post Doctoral Researcher

01 February 2008 - 01 February 2011




Short Biography

Marco Mussetta is an Associate Professor of Electrical Engineering in Politecnico di Milano, Italy. His research activities include global evolutionary optimization techniques and modeling of renewable energy systems by means of advanced soft computing techniques. Since 2001, Prof. Mussetta coauthored about 200 publications on WoS/Scopus-indexed journals and proceedings of international conferences. He serves as a Reviewer for several IEEE Transactions. He is a Senior Member of IEEE, PES, CIS, IES, a member of the IEEE CIS Fuzzy Systems Technical Committee and the Chair of the IEEE CIS Task Force on “Fuzzy Systems in Renewable Energy and Smart Grid”.

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Conference
Luxembourg
Date: 11-14 July 2021
Conference organizer :
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Marco Mussetta
Project

Project Goal: The EU-funded H2020 project PLATOON aims to digitalise the energy sector, enabling thus higher levels of operational excellence with the adoption of disrupting technologies.

Starting Date:16 January 2020

Current Stage: Ongoing [https://platoon-project.eu/]

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Project

Project Goal: The project envisions to develop and test AUTOFPI, a vision-based expert system for automating the last operations of the FPI process

Starting Date:17 April 2018

Current Stage: Completed [https://www.era-learn.eu/network-information/networks/manunet-iii/manunet-call-2017/automatic-fluorescent-penetrant-inspection-system]

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Journal article
Published: 29 May 2021 in Mathematics
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Fluorescent penetrant inspection (FPI) is a well-assessed non-destructive test method used in manufacturing for detecting cracks and other flaws of the product under test. This is a critical phase in the mechanical and aerospace industrial sector. The purpose of this work was to present the implementation of an automated inspection system, developing a vision-based expert system to automate the inspection phase of the FPI process in an aerospace manufacturing line. The aim of this process was to identify the defectiveness status of some mechanical parts by the means of images. This paper will present, test and compare different machine learning architectures to perform the automated defect detection on a given dataset. For each test sample, several images at different angles were captured to properly populate the input dataset. In this way, the defectiveness status should be found combining the information contained in all the pictures. In particular, the system was designed for increasing the reliability of the evaluations performed on the airplane part, by implementing proper artificial intelligence (AI) techniques to reduce current human operators’ effort. The results show that, for applications in which the dataset available is quite small, a well-designed feature extraction process before the machine learning classifier is a very important step for achieving high classification accuracy.

ACS Style

Alessandro Niccolai; Davide Caputo; Leonardo Chieco; Francesco Grimaccia; Marco Mussetta. Machine Learning-Based Detection Technique for NDT in Industrial Manufacturing. Mathematics 2021, 9, 1251 .

AMA Style

Alessandro Niccolai, Davide Caputo, Leonardo Chieco, Francesco Grimaccia, Marco Mussetta. Machine Learning-Based Detection Technique for NDT in Industrial Manufacturing. Mathematics. 2021; 9 (11):1251.

Chicago/Turabian Style

Alessandro Niccolai; Davide Caputo; Leonardo Chieco; Francesco Grimaccia; Marco Mussetta. 2021. "Machine Learning-Based Detection Technique for NDT in Industrial Manufacturing." Mathematics 9, no. 11: 1251.

Journal article
Published: 13 January 2021 in Electronics
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Electric mobility can represent a game changing technology for the long-term sustainability of the transportation sector. Pursuing this target, a model to simulate an Electric Vehicle (EV) for Formula SAE Electric competition is herein proposed: all the subsystems of the EV and the hybrid storage of the Li-ion batteries and Ultra-Capacitors (UCs) are implemented, in order to store the kinetic energy of the regenerative braking in the storage system through the Kinetic Energy Recovery System (KERS). A bidirectional DC-DC resonant converter is herein applied to the KERS to manage the UC pack. The operational limits of the proposed system, keeping the soft-switching properties, are discussed, and the results show the capability of the converter to operate under resonant mode in both boost and buck mode. A drawback is the presence of high current peaks in the resonant inductor. The use of more than one converter in interleaving and the adoption of a suitable capability factor ensure the proper operation of the system.

ACS Style

Alberto Dolara; Sonia Leva; Giacomo Moretti; Marco Mussetta; Yales Romulo De Novaes. Design of a Resonant Converter for a Regenerative Braking System Based on Ultracap Storage for Application in a Formula SAE Single-Seater Electric Racing Car. Electronics 2021, 10, 161 .

AMA Style

Alberto Dolara, Sonia Leva, Giacomo Moretti, Marco Mussetta, Yales Romulo De Novaes. Design of a Resonant Converter for a Regenerative Braking System Based on Ultracap Storage for Application in a Formula SAE Single-Seater Electric Racing Car. Electronics. 2021; 10 (2):161.

Chicago/Turabian Style

Alberto Dolara; Sonia Leva; Giacomo Moretti; Marco Mussetta; Yales Romulo De Novaes. 2021. "Design of a Resonant Converter for a Regenerative Braking System Based on Ultracap Storage for Application in a Formula SAE Single-Seater Electric Racing Car." Electronics 10, no. 2: 161.

Journal article
Published: 15 October 2020 in Forecasting
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The increasing penetration of non-programmable renewable energy sources (RES) is enforcing the need for accurate power production forecasts. In the category of hydroelectric plants, Run of the River (RoR) plants belong to the class of non-programmable RES. Data-driven models are nowadays the most widely adopted methodologies in hydropower forecast. Among all, the Artificial Neural Network (ANN) proved to be highly successful in production forecast. Widely adopted and equally important for hydropower generation forecast is the HYdrological Predictions for the Environment (HYPE), a semi-distributed hydrological Rainfall–Runoff model. A novel hybrid method, providing HYPE sub-basins flow computation as input to an ANN, is here introduced and tested both with and without the adoption of a decomposition approach. In the former case, two ANNs are trained to forecast the trend and the residual of the production, respectively, to be then summed up to the previously extracted seasonality component and get the power forecast. These results have been compared to those obtained from the adoption of a ANN with rainfalls in input, again with and without decomposition approach. The methods have been assessed by forecasting the Run-of-the-River hydroelectric power plant energy for the year 2017. Besides, the forecasts of 15 power plants output have been fairly compared in order to identify the most accurate forecasting technique. The here proposed hybrid method (HYPE and ANN) has shown to be the most accurate in all the considered study cases.

ACS Style

Emanuele Ogliari; Alfredo Nespoli; Marco Mussetta; Silvia Pretto; Andrea Zimbardo; Nicholas Bonfanti; Manuele Aufiero. A Hybrid Method for the Run-Of-The-River Hydroelectric Power Plant Energy Forecast: HYPE Hydrological Model and Neural Network. Forecasting 2020, 2, 410 -428.

AMA Style

Emanuele Ogliari, Alfredo Nespoli, Marco Mussetta, Silvia Pretto, Andrea Zimbardo, Nicholas Bonfanti, Manuele Aufiero. A Hybrid Method for the Run-Of-The-River Hydroelectric Power Plant Energy Forecast: HYPE Hydrological Model and Neural Network. Forecasting. 2020; 2 (4):410-428.

Chicago/Turabian Style

Emanuele Ogliari; Alfredo Nespoli; Marco Mussetta; Silvia Pretto; Andrea Zimbardo; Nicholas Bonfanti; Manuele Aufiero. 2020. "A Hybrid Method for the Run-Of-The-River Hydroelectric Power Plant Energy Forecast: HYPE Hydrological Model and Neural Network." Forecasting 2, no. 4: 410-428.

Journal article
Published: 01 October 2020 in Sustainability
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Accurate modelling of the fuel cell characteristics curve is essential for the simulation analysis, control management, performance evaluation, and fault detection of fuel cell power systems. However, the big challenge in fuel cell modelling is the multi-variable complexity of the characteristic curves. In this paper, we propose the implementation of a computer graphic technique called Bézier curve to approximate the characteristics curves of the fuel cell. Four different case studies are examined as follows: Ballard Systems, Horizon H-12 W stack, NedStackPS6, and 250 W proton exchange membrane fuel cells (PEMFC). The main objective is to minimize the absolute errors between experimental and calculated data by using the control points of the Bernstein–Bézier function and de Casteljau’s algorithm. The application of this technique entails subdividing the fuel cell curve to some segments, where each segment is approximated by a Bézier curve so that the approximation error is minimized. Further, the performance and accuracy of the proposed techniques are compared with recent results obtained by different metaheuristic algorithms and analytical methods. The comparison is carried out in terms of various statistical error indicators, such as Individual Absolute Error (IAE), Relative Error (RE), Root Mean Square Error (RMSE), Mean Bias Errors (MBE), and Autocorrelation Function (ACF). The results obtained by the Bézier curve technique show an excellent agreement with experimental data and are more accurate than those obtained by other comparative techniques.

ACS Style

Mohamed Louzazni; Sameer Al-Dahidi; Marco Mussetta. Fuel Cell Characteristic Curve Approximation Using the Bézier Curve Technique. Sustainability 2020, 12, 8127 .

AMA Style

Mohamed Louzazni, Sameer Al-Dahidi, Marco Mussetta. Fuel Cell Characteristic Curve Approximation Using the Bézier Curve Technique. Sustainability. 2020; 12 (19):8127.

Chicago/Turabian Style

Mohamed Louzazni; Sameer Al-Dahidi; Marco Mussetta. 2020. "Fuel Cell Characteristic Curve Approximation Using the Bézier Curve Technique." Sustainability 12, no. 19: 8127.

Journal article
Published: 07 August 2020 in Mathematics and Computers in Simulation
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The electrical load forecasting is a fundamental technique for consumer load prediction for utilities. The accurate load forecasting is crucial to design Demand Response (DR) programs in the paradigm of smart grids. Artificial Neural Network (ANN) based techniques have been widely used in recent years and applied to predict the electric load with high accuracy to participate in DR programs for commercial, industrial and residential consumers. This research work is focused on the use and comparison of two ANN-based load forecasting techniques, i.e. Feed-Forward Neural Network (FFNN) and Echo State Network (ESN), on a dataset related to commercial buildings, in view of a possible DR program application. The results of both models are compared based on the load forecasting accuracy through experimental measurements and suitably defined metrics.

ACS Style

Muhammad Mansoor; Francesco Grimaccia; Sonia Leva; Marco Mussetta. Comparison of echo state network and feed-forward neural networks in electrical load forecasting for demand response programs. Mathematics and Computers in Simulation 2020, 184, 282 -293.

AMA Style

Muhammad Mansoor, Francesco Grimaccia, Sonia Leva, Marco Mussetta. Comparison of echo state network and feed-forward neural networks in electrical load forecasting for demand response programs. Mathematics and Computers in Simulation. 2020; 184 ():282-293.

Chicago/Turabian Style

Muhammad Mansoor; Francesco Grimaccia; Sonia Leva; Marco Mussetta. 2020. "Comparison of echo state network and feed-forward neural networks in electrical load forecasting for demand response programs." Mathematics and Computers in Simulation 184, no. : 282-293.

Journal article
Published: 11 June 2020 in Energies
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The forecasting of solar irradiance in photovoltaic power generation is an important tool for the integration of intermittent renewable energy sources (RES) in electrical utility grids. This study evaluates two machine learning (ML) algorithms for intraday solar irradiance forecasting: multigene genetic programming (MGGP) and the multilayer perceptron (MLP) artificial neural network (ANN). MGGP is an evolutionary algorithm white-box method and is a novel approach in the field. Persistence, MGGP and MLP were compared to forecast irradiance at six locations, within horizons from 15 to 120 min, in order to compare these methods based on a wide range of reliable results. The assessment of exogenous inputs indicates that the use of additional weather variables improves irradiance forecastability, resulting in improvements of 5.68% for mean absolute error (MAE) and 3.41% for root mean square error (RMSE). It was also verified that iterative predictions improve MGGP accuracy. The obtained results show that location, forecast horizon and error metric definition affect model accuracy dominance. Both Haurwitz and Ineichen clear sky models have been implemented, and the results denoted a low influence of these models in the prediction accuracy of multivariate ML forecasting. In a broad perspective, MGGP presented more accurate and robust results in single prediction cases, providing faster solutions, while ANN presented more accurate results for ensemble forecasting, although it presented higher complexity and requires additional computational effort.

ACS Style

Gabriel Mendonça De Paiva; Sergio Pires Pimentel; Bernardo Pinheiro Alvarenga; Enes Marra; Marco Mussetta; Sonia Leva. Multiple Site Intraday Solar Irradiance Forecasting by Machine Learning Algorithms: MGGP and MLP Neural Networks. Energies 2020, 13, 3005 .

AMA Style

Gabriel Mendonça De Paiva, Sergio Pires Pimentel, Bernardo Pinheiro Alvarenga, Enes Marra, Marco Mussetta, Sonia Leva. Multiple Site Intraday Solar Irradiance Forecasting by Machine Learning Algorithms: MGGP and MLP Neural Networks. Energies. 2020; 13 (11):3005.

Chicago/Turabian Style

Gabriel Mendonça De Paiva; Sergio Pires Pimentel; Bernardo Pinheiro Alvarenga; Enes Marra; Marco Mussetta; Sonia Leva. 2020. "Multiple Site Intraday Solar Irradiance Forecasting by Machine Learning Algorithms: MGGP and MLP Neural Networks." Energies 13, no. 11: 3005.

Journal article
Published: 14 April 2020 in Mathematics
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The correct design of a Wireless Sensor Network (WSN) is a very important task because it can highly influence its installation and operational costs. An important aspect that should be addressed with WSN is the routing definition in multi-hop networks. This problem is faced with different methods in the literature, and here it is managed with a recently developed swarm intelligence algorithm called Social Network Optimization (SNO). In this paper, the routing definition in WSN is approached with two different problem codifications and solved with SNO and Particle Swarm Optimization. The first codification allows the optimization algorithm more degrees of freedom, resulting in a slower and in many cases sub-optimal solution. The second codification reduces the degrees of freedom, speeding significantly the optimization process and blocking in some cases the convergence toward the real best network configuration.

ACS Style

Alessandro Niccolai; Francesco Grimaccia; Marco Mussetta; Alessandro Gandelli; Riccardo Zich. Social Network Optimization for WSN Routing: Analysis on Problem Codification Techniques. Mathematics 2020, 8, 583 .

AMA Style

Alessandro Niccolai, Francesco Grimaccia, Marco Mussetta, Alessandro Gandelli, Riccardo Zich. Social Network Optimization for WSN Routing: Analysis on Problem Codification Techniques. Mathematics. 2020; 8 (4):583.

Chicago/Turabian Style

Alessandro Niccolai; Francesco Grimaccia; Marco Mussetta; Alessandro Gandelli; Riccardo Zich. 2020. "Social Network Optimization for WSN Routing: Analysis on Problem Codification Techniques." Mathematics 8, no. 4: 583.

Journal article
Published: 01 January 2020 in IEEE Access
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ACS Style

Sonia Leva; Alfredo Nespoli; Silvia Pretto; Marco Mussetta; Emanuele Giovanni Carlo Ogliari. PV Plant Power Nowcasting: A Real Case Comparative Study With an Open Access Dataset. IEEE Access 2020, 8, 194428 -194440.

AMA Style

Sonia Leva, Alfredo Nespoli, Silvia Pretto, Marco Mussetta, Emanuele Giovanni Carlo Ogliari. PV Plant Power Nowcasting: A Real Case Comparative Study With an Open Access Dataset. IEEE Access. 2020; 8 ():194428-194440.

Chicago/Turabian Style

Sonia Leva; Alfredo Nespoli; Silvia Pretto; Marco Mussetta; Emanuele Giovanni Carlo Ogliari. 2020. "PV Plant Power Nowcasting: A Real Case Comparative Study With an Open Access Dataset." IEEE Access 8, no. : 194428-194440.

Journal article
Published: 01 December 2019 in Electronics
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Forecasting the power production from renewable energy sources (RESs) has become fundamental in microgrid applications to optimize scheduling and dispatching of the available assets. In this article, a methodology to provide the 24 h ahead Photovoltaic (PV) power forecast based on a Physical Hybrid Artificial Neural Network (PHANN) for microgrids is presented. The goal of this paper is to provide a robust methodology to forecast 24 h in advance the PV power production in a microgrid, addressing the specific criticalities of this environment. The proposed approach has to validate measured data properly, through an effective algorithm and further refine the power forecast when newer data are available. The procedure is fully implemented in a facility of the Multi-Good Microgrid Laboratory (MG L a b 2 ) of the Politecnico di Milano, Milan, Italy, where new Energy Management Systems (EMSs) are studied. Reported results validate the proposed approach as a robust and accurate procedure for microgrid applications.

ACS Style

Alfredo Nespoli; Marco Mussetta; Emanuele Ogliari; Sonia Leva; Luis Fernández-Ramírez; Pablo García-Triviño. Robust 24 Hours ahead Forecast in a Microgrid: A Real Case Study. Electronics 2019, 8, 1434 .

AMA Style

Alfredo Nespoli, Marco Mussetta, Emanuele Ogliari, Sonia Leva, Luis Fernández-Ramírez, Pablo García-Triviño. Robust 24 Hours ahead Forecast in a Microgrid: A Real Case Study. Electronics. 2019; 8 (12):1434.

Chicago/Turabian Style

Alfredo Nespoli; Marco Mussetta; Emanuele Ogliari; Sonia Leva; Luis Fernández-Ramírez; Pablo García-Triviño. 2019. "Robust 24 Hours ahead Forecast in a Microgrid: A Real Case Study." Electronics 8, no. 12: 1434.

Journal article
Published: 27 November 2019 in Energies
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The inherently non-dispatchable nature of renewable sources, such as solar photovoltaic, is regarded as one of the main challenges hindering their massive integration in existing electric grids. Accurate forecasting of the power output of the solar plant might therefore play a key role towards this goal. In this paper, we compare several machine learning and deep learning algorithms for intra-hour forecasting of the output power of a 1 MW photovoltaic plant, using meteorological data acquired in the field. With the best performing algorithms, our data-driven workflow provided prediction performance that compares well with the present state of the art and could be applied in an industrial setting.

ACS Style

Simone Sala; Alfonso Amendola; Sonia Leva; Marco Mussetta; Alessandro Niccolai; Emanuele Ogliari. Comparison of Data-Driven Techniques for Nowcasting Applied to an Industrial-Scale Photovoltaic Plant. Energies 2019, 12, 4520 .

AMA Style

Simone Sala, Alfonso Amendola, Sonia Leva, Marco Mussetta, Alessandro Niccolai, Emanuele Ogliari. Comparison of Data-Driven Techniques for Nowcasting Applied to an Industrial-Scale Photovoltaic Plant. Energies. 2019; 12 (23):4520.

Chicago/Turabian Style

Simone Sala; Alfonso Amendola; Sonia Leva; Marco Mussetta; Alessandro Niccolai; Emanuele Ogliari. 2019. "Comparison of Data-Driven Techniques for Nowcasting Applied to an Industrial-Scale Photovoltaic Plant." Energies 12, no. 23: 4520.

Journal article
Published: 28 March 2019 in Mathematics
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Wireless Sensor Networks (WSN) have been widely adopted for years, but their role is growing significantly currently with the increase of the importance of the Internet of Things paradigm. Moreover, since the computational capability of small-sized devices is also increasing, WSN are now capable of performing relevant operations. An optimal scheduling of these in-network processes can affect both the total computational time and the energy requirements. Evolutionary optimization techniques can address this problem successfully due to their capability to manage non-linear problems with many design variables. In this paper, an evolutionary algorithm recently developed, named Social Network Optimization (SNO), has been applied to the problem of task allocation in a WSN. The optimization results on two test cases have been analyzed: in the first one, no energy constraints have been added to the optimization, while in the second one, a minimum number of life cycles is imposed.

ACS Style

Alessandro Niccolai; Francesco Grimaccia; Marco Mussetta; Riccardo Zich. Optimal Task Allocation in Wireless Sensor Networks by Means of Social Network Optimization. Mathematics 2019, 7, 315 .

AMA Style

Alessandro Niccolai, Francesco Grimaccia, Marco Mussetta, Riccardo Zich. Optimal Task Allocation in Wireless Sensor Networks by Means of Social Network Optimization. Mathematics. 2019; 7 (4):315.

Chicago/Turabian Style

Alessandro Niccolai; Francesco Grimaccia; Marco Mussetta; Riccardo Zich. 2019. "Optimal Task Allocation in Wireless Sensor Networks by Means of Social Network Optimization." Mathematics 7, no. 4: 315.

Conference paper
Published: 12 November 2018 in Day 3 Wed, November 14, 2018
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Asset optimization has recently become a crucial issue in Oil&Gas industry, considering oil price conjuncture and an increased awareness on environmental aspects. In this paper, an Artificial Intelligence (AI) technique is presented, which is able to manage big dataset to automatically match the entire production model against measured field data. The tool is based on a hybrid in-house developed AI technique, integrating deep neural networks, biogenetical algorithms, commercial simulators and real-time data. The workflow starts with the modeling of the production system through physics-based commercial simulators. A sensitivity analysis identifies the critical variables, which are then randomly varied with a Sobol distribution, exploring the entire solution domain. With these data, a proxy model to the commercial software is generated using an artificial neural network. Finally, the AI tool fed by real-time data is used to match the field behavior: uncertain parameters are modified through a differential evolution algorithm that minimizes the error between calculated and measured variables. The matching parameters are, then, passed to the simulators achieving a field representative model. The tool has been developed considering an operating field in offshore western Africa. The typical uncertain parameters in this kind of field are related to the fluid characteristics, in particular densities and compositions, but also to the physical characterization of the pipelines such as roughness and heat transfer characteristics. The matching process has been performed coupling the proxy model, which is a neural network able to replicate the field behavior, and a differential evolution algorithm as the optimization algorithm. The fitness function to be minimized is a Mean Absolute Percentage Error (MAPE) that represents the distance between the actual field production parameters and the modelled ones. The best configuration of both the neural network and the differential evolution algorithm required a computational time of 6 seconds with a MAPE equal to 2.6%. These results are compared to the one obtained coupling the same differential evolution algorithm with the commercial simulator to perform the matching. The required computational time is equal to about 20 hours (70400s) and a MAPE equal to 2.2%. The big gain with the novel approach is clearly the knocking down of computational time with a comparable error. In this paper, it has been shown how substituting the physical model with a proxy one can give substantial advantages in terms of computational time. In principle, with the velocity of the tool implemented, the matching procedure could be done on a daily basis. This is a breakthrough because it allows having the simulator model always tuned and ready to be utilized.

ACS Style

Marco Giuliani; Luca Cadei; Marco Montini; Amalia Bianco; Alessandro Niccolai; Marco Mussetta; Francesco Grimaccia. Hybrid Artificial Intelligence Techniques for Automatic Simulation Models Matching with Field Data. Day 3 Wed, November 14, 2018 2018, 1 .

AMA Style

Marco Giuliani, Luca Cadei, Marco Montini, Amalia Bianco, Alessandro Niccolai, Marco Mussetta, Francesco Grimaccia. Hybrid Artificial Intelligence Techniques for Automatic Simulation Models Matching with Field Data. Day 3 Wed, November 14, 2018. 2018; ():1.

Chicago/Turabian Style

Marco Giuliani; Luca Cadei; Marco Montini; Amalia Bianco; Alessandro Niccolai; Marco Mussetta; Francesco Grimaccia. 2018. "Hybrid Artificial Intelligence Techniques for Automatic Simulation Models Matching with Field Data." Day 3 Wed, November 14, 2018 , no. : 1.

Journal article
Published: 07 November 2018 in IEEE Transactions on Industrial Electronics
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The employment of solar micro-converter allows a more detailed monitoring of the PV output power at the single module level; thus, machine learning techniques are capable to track the peculiarities of modules in the PV plants such as regular shadings. In this way it is possible to compare in real-time the day-ahead forecast power with the actual one in order to better evaluate faults or anomalous trends which might have occurred in the PV plant. This paper presents a method for an effective fault diagnosis; this method is based on the day-ahead forecast of the output power from an existing PV module, linked to a micro-converter, and on the outcome of the neighbor PV modules. Finally, this paper proposes also the analysis of the most common error definitions with new mathematical formulations, by comparing their effectiveness and immediate comprehension, in view of increasing power forecasting accuracy and performing both real-time and offline analysis of PV modules performance and possible faults.

ACS Style

Sonia Leva; Marco Mussetta; Emanuele Ogliari. PV Module Fault Diagnosis Based on Microconverters and Day-Ahead Forecast. IEEE Transactions on Industrial Electronics 2018, 66, 3928 -3937.

AMA Style

Sonia Leva, Marco Mussetta, Emanuele Ogliari. PV Module Fault Diagnosis Based on Microconverters and Day-Ahead Forecast. IEEE Transactions on Industrial Electronics. 2018; 66 (5):3928-3937.

Chicago/Turabian Style

Sonia Leva; Marco Mussetta; Emanuele Ogliari. 2018. "PV Module Fault Diagnosis Based on Microconverters and Day-Ahead Forecast." IEEE Transactions on Industrial Electronics 66, no. 5: 3928-3937.

Conference paper
Published: 01 July 2018 in 2018 IEEE Congress on Evolutionary Computation (CEC)
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Nowadays wireless and networking technologies represent a crucial research area which involves communication and digital electronics and integrate a large amount of distributed sensing capabilities. The recent improved integration in sensing, communication and computing technologies in mobile phone and similar devices contributed to increase the penetration of Wireless Sensor Networks (WSN) concept in everyday life. In particular, in this work the computational capability of a WSN is used to perform a distributed run of the recently presented optimization Evolutionary Algorithm called Social Network Optimization (SNO). Additionally, the aim of this paper is to present a distributed implementation of SNO (called d-SNO), suitably developed for real-world distributed application problems. The distributed computation capability of the WSN is then optimized in order to have an optimally spread computational time, taking into account the different computational speed of each sensor of the network.

ACS Style

Francesco Grimaccia; Marco Mussetta; Alessandro Niccolai; Riccardo E. Zich. Optimal Computational Distribution of Social Network Optimization in Wireless Sensor Networks. 2018 IEEE Congress on Evolutionary Computation (CEC) 2018, 1 -7.

AMA Style

Francesco Grimaccia, Marco Mussetta, Alessandro Niccolai, Riccardo E. Zich. Optimal Computational Distribution of Social Network Optimization in Wireless Sensor Networks. 2018 IEEE Congress on Evolutionary Computation (CEC). 2018; ():1-7.

Chicago/Turabian Style

Francesco Grimaccia; Marco Mussetta; Alessandro Niccolai; Riccardo E. Zich. 2018. "Optimal Computational Distribution of Social Network Optimization in Wireless Sensor Networks." 2018 IEEE Congress on Evolutionary Computation (CEC) , no. : 1-7.

Conference paper
Published: 01 July 2018 in 2018 IEEE Congress on Evolutionary Computation (CEC)
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Binary problems are common in engineering and they can be suitably faced with Evolutionary Optimization. In the antenna field, these problems are quite common and they are characterized to be often multi-modal and non-convex, so they cannot be easily solved by means of standard optimization techniques. In particular, three different Evolutionary Algorithms have been frequently considered in recent years in the field of antenna arrays optimization, namely Stud-Genetic Algorithm (Stud-GA), binary Particle Swarm Optimization (bPSO) and Social Network Optimization (SNO). The aim of this paper is to extensively compare these three heuristics over standard benchmark functions and on a well-known antenna problem, i.e. the optimization of a thinned array. Numerical simulation will be conducted on an array of 121 elements and performances of the different approaches will be compared and validated over this real-world electromagnetic application.

ACS Style

Francesco Grimaccia; Marco Mussetta; Alessandro Niccolai; Riccardo E. Zich. Comparison of Binary Evolutionary Algorithms for Optimization of Thinned Array Antennas. 2018 IEEE Congress on Evolutionary Computation (CEC) 2018, 1 -8.

AMA Style

Francesco Grimaccia, Marco Mussetta, Alessandro Niccolai, Riccardo E. Zich. Comparison of Binary Evolutionary Algorithms for Optimization of Thinned Array Antennas. 2018 IEEE Congress on Evolutionary Computation (CEC). 2018; ():1-8.

Chicago/Turabian Style

Francesco Grimaccia; Marco Mussetta; Alessandro Niccolai; Riccardo E. Zich. 2018. "Comparison of Binary Evolutionary Algorithms for Optimization of Thinned Array Antennas." 2018 IEEE Congress on Evolutionary Computation (CEC) , no. : 1-8.

Conference paper
Published: 01 July 2018 in 2018 IEEE Congress on Evolutionary Computation (CEC)
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Development and improvement of solar forecasting models have been extensively addressed in the past years due to the importance of solar energy as a renewable energy source. This work presents an application and improvement of intra-day solar predictive models based on genetic programming. Forecasts were evaluated in time horizons of 10 minutes up to 180 minutes ahead as future steps at two completely different locations: one in northern hemisphere and another in the southern hemisphere. The improvement strategy was validated in comparison of error metrics to the ones obtained by benchmark methods of solar forecasting. The proposed model results will be presented and validated for each considered location.

ACS Style

Gabriel Paiva; Sergio Pires Pimentel; Sonia Leva; Marco Mussetta. Intelligent Approach to Improve Genetic Programming Based Intra-Day Solar Forecasting Models. 2018 IEEE Congress on Evolutionary Computation (CEC) 2018, 1 -8.

AMA Style

Gabriel Paiva, Sergio Pires Pimentel, Sonia Leva, Marco Mussetta. Intelligent Approach to Improve Genetic Programming Based Intra-Day Solar Forecasting Models. 2018 IEEE Congress on Evolutionary Computation (CEC). 2018; ():1-8.

Chicago/Turabian Style

Gabriel Paiva; Sergio Pires Pimentel; Sonia Leva; Marco Mussetta. 2018. "Intelligent Approach to Improve Genetic Programming Based Intra-Day Solar Forecasting Models." 2018 IEEE Congress on Evolutionary Computation (CEC) , no. : 1-8.

Conference paper
Published: 01 July 2018 in 2018 International Joint Conference on Neural Networks (IJCNN)
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Application of Machine Learning in forecasting renewable energy sources (RES) is increasing: in particular, several neural networks have been employed to perform the day-ahead photo-voltaic output power forecast. The aim of this paper is to consider different training approaches in order to improve the accuracy of the PV power prediction, with particular attention to day-ahead and intra-day forecasts. Additionally, novel error metrics, specifically proposed for the defined task, are compared with traditional ones, showing the best approach for the different considered cases. The results will be validated over a 1-year time range of experimentally measured data, for a PV module installed in the Solar Tech Lab in the department of Energy at Politecnico di Milano.

ACS Style

Alfredo Nespoli; Emanuele Ogliari; Alberto Dolara; Francesco Grimaccia; Sonia Leva; Marco Mussetta. Validation of ANN Training Approaches for Day-Ahead Photovoltaic Forecasts. 2018 International Joint Conference on Neural Networks (IJCNN) 2018, 1 -6.

AMA Style

Alfredo Nespoli, Emanuele Ogliari, Alberto Dolara, Francesco Grimaccia, Sonia Leva, Marco Mussetta. Validation of ANN Training Approaches for Day-Ahead Photovoltaic Forecasts. 2018 International Joint Conference on Neural Networks (IJCNN). 2018; ():1-6.

Chicago/Turabian Style

Alfredo Nespoli; Emanuele Ogliari; Alberto Dolara; Francesco Grimaccia; Sonia Leva; Marco Mussetta. 2018. "Validation of ANN Training Approaches for Day-Ahead Photovoltaic Forecasts." 2018 International Joint Conference on Neural Networks (IJCNN) , no. : 1-6.