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Nowadays, we are moving forward to more sustainable energy production systems based on renewable sources. Among all Photovoltaic (PV) systems are spreading in our cities. In this view, new models are needed to forecast Global Horizontal Solar Irradiance (GHI), which strongly influences PV production. For example, this forecast is crucial to develop novel control strategies for smart grid management. In this paper, we present a novel methodology to forecast GHI in short- and long-term time-horizons, i.e. from next 15 min up to next 24 h. It implements machine learning techniques to achieve this purpose. We start from the analysis of a real-world dataset with different meteorological information including GHI, in the form of time-series. Then, we combined Variational Mode Decomposition (VMD) and two Convolutional Neural Networks (CNN) together with Random Forest (RF) or Long Short Term Memory (LSTM). Finally, we present the experimental results and discuss their accuracy.
Davide Cannizzaro; Alessandro Aliberti; Lorenzo Bottaccioli; Enrico Macii; Andrea Acquaviva; Edoardo Patti. Solar radiation forecasting based on convolutional neural network and ensemble learning. Expert Systems with Applications 2021, 181, 115167 .
AMA StyleDavide Cannizzaro, Alessandro Aliberti, Lorenzo Bottaccioli, Enrico Macii, Andrea Acquaviva, Edoardo Patti. Solar radiation forecasting based on convolutional neural network and ensemble learning. Expert Systems with Applications. 2021; 181 ():115167.
Chicago/Turabian StyleDavide Cannizzaro; Alessandro Aliberti; Lorenzo Bottaccioli; Enrico Macii; Andrea Acquaviva; Edoardo Patti. 2021. "Solar radiation forecasting based on convolutional neural network and ensemble learning." Expert Systems with Applications 181, no. : 115167.
District Heating (DH) technology is considered to be a sustainable and quasi-renewable way of producing and distributing hot water along the city to heat buildings. However, the main obstacle to wider adoption of DH technology is represented by the thermal request peak in the morning hours of winter days, especially in Mediterranean countries. In this paper, this peak-shaving problem is tackled by combining three different approaches. A thermodynamic model is used to monitor the buildings’ thermal response to energy profile modifications. An agent-based model is adopted in order to represent the end-users and their adaptability to variations of temperatures in buildings. Finally, a Reinforcement Learning algorithm is used to optimally mediate between two needs: on the one hand, a set of anticipations and delays is applied to the energy profiles in order to reduce the thermal request peak. On the other hand, the algorithm learns by trial and error the individual agents’ sensitivity to thermal comfort, avoiding drastic modifications for the most sensitive users. The experiments carried out in the DH network in Torino (north-west of Italy) demonstrate that the proposed approach, compared with a literature solution chosen as a baseline, allows to achieve better results in terms of overall performances and speed of convergence.
Francesco M. Solinas; Lorenzo Bottaccioli; Elisa Guelpa; Vittorio Verda; Edoardo Patti. Peak shaving in district heating exploiting reinforcement learning and agent-based modelling. Engineering Applications of Artificial Intelligence 2021, 102, 104235 .
AMA StyleFrancesco M. Solinas, Lorenzo Bottaccioli, Elisa Guelpa, Vittorio Verda, Edoardo Patti. Peak shaving in district heating exploiting reinforcement learning and agent-based modelling. Engineering Applications of Artificial Intelligence. 2021; 102 ():104235.
Chicago/Turabian StyleFrancesco M. Solinas; Lorenzo Bottaccioli; Elisa Guelpa; Vittorio Verda; Edoardo Patti. 2021. "Peak shaving in district heating exploiting reinforcement learning and agent-based modelling." Engineering Applications of Artificial Intelligence 102, no. : 104235.
In recent years, the introduction and exploitation of innovative information technologies in industrial contexts have led to the continuous growth of digital shop floor environments. The new Industry 4.0 model allows smart factories to become very advanced IT industries, generating an ever-increasing amount of valuable data. As a consequence, the necessity of powerful and reliable software architectures is becoming prominent along with data-driven methodologies to extract useful and hidden knowledge supporting the decision-making process. This article discusses the latest software technologies needed to collect, manage, and elaborate all data generated through innovative Internet-of-Things (IoT) architectures deployed over the production line, with the aim of extracting useful knowledge for the orchestration of high-level control services that can generate added business value. This survey covers the entire data life cycle in manufacturing environments, discussing key functional and methodological aspects along with a rich and properly classified set of technologies and tools, useful to add intelligence to data-driven services. Therefore, it serves both as a first guided step toward the rich landscape of the literature for readers approaching this field and as a global yet detailed overview of the current state of the art in the Industry 4.0 domain for experts. As a case study, we discuss, in detail, the deployment of the proposed solutions for two research project demonstrators, showing their ability to mitigate manufacturing line interruptions and reduce the corresponding impacts and costs.
Tania Cerquitelli; Daniele Jahier Pagliari; Andrea Calimera; Lorenzo Bottaccioli; Edoardo Patti; Andrea Acquaviva; Massimo Poncino. Manufacturing as a Data-Driven Practice: Methodologies, Technologies, and Tools. Proceedings of the IEEE 2021, 109, 399 -422.
AMA StyleTania Cerquitelli, Daniele Jahier Pagliari, Andrea Calimera, Lorenzo Bottaccioli, Edoardo Patti, Andrea Acquaviva, Massimo Poncino. Manufacturing as a Data-Driven Practice: Methodologies, Technologies, and Tools. Proceedings of the IEEE. 2021; 109 (4):399-422.
Chicago/Turabian StyleTania Cerquitelli; Daniele Jahier Pagliari; Andrea Calimera; Lorenzo Bottaccioli; Edoardo Patti; Andrea Acquaviva; Massimo Poncino. 2021. "Manufacturing as a Data-Driven Practice: Methodologies, Technologies, and Tools." Proceedings of the IEEE 109, no. 4: 399-422.
Future smart grids with more distributed generation and flexible demand require well-verified control and management services. This paper presents a distributed multi- model co-simulation platform based on Smart Grid Architecture Model (a.k.a. SGAM) to foster general purpose services in smart grids. It aims at providing developers with support to easily set-up a test-bed environment where they can simulate realistic scenarios to assess their algorithms and services. The proposed platform takes advantages of Internet-of-Things communication paradigms and protocols to enable the interoperability among different models and virtual or physical devices that compose a use case. Moreover, the integration of digital real-time simulators unlocks Hardware-In-the-Loop features. To test the functionality of our platform, a novel scheme of fault detection, isolation and restoration is developed, in which communication and interoperability of different functions and devices are crucial. This service is applied on a realistic portion of a power grid in Turin, Italy, where devices communicate over the Internet. Finally, the laboratory experimental results achieved during a real-time co-simulation are discussed.
Luca Barbierato; Abouzar Estebsari; Lorenzo Bottaccioli; Enrico Macii; Edoardo Patti. A Distributed Multimodel Cosimulation Platform to Assess General Purpose Services in Smart Grids. IEEE Transactions on Industry Applications 2020, 56, 5613 -5624.
AMA StyleLuca Barbierato, Abouzar Estebsari, Lorenzo Bottaccioli, Enrico Macii, Edoardo Patti. A Distributed Multimodel Cosimulation Platform to Assess General Purpose Services in Smart Grids. IEEE Transactions on Industry Applications. 2020; 56 (5):5613-5624.
Chicago/Turabian StyleLuca Barbierato; Abouzar Estebsari; Lorenzo Bottaccioli; Enrico Macii; Edoardo Patti. 2020. "A Distributed Multimodel Cosimulation Platform to Assess General Purpose Services in Smart Grids." IEEE Transactions on Industry Applications 56, no. 5: 5613-5624.
Fault alarm data emanated from heterogeneous telecommunication network services and infrastructures are exploding with network expansions. Managing and tracking the alarms with Trouble Tickets using manual or expert rule-based methods has become challenging due to increase in the complexity of Alarm Management Systems and demand for deployment of highly trained experts. As the size and complexity of networks hike immensely, identifying semantically identical alarms, generated from heterogeneous network elements from diverse vendors, with data-driven methodologies has become imperative to enhance efficiency. In this paper, a data-driven Trouble Ticket prediction models are proposed to leverage Alarm Management Systems. To improve performance, feature extraction, using a sliding time-window and feature engineering, from related history alarm streams is also introduced. The models were trained and validated with a data-set provided by the largest telecommunication provider in our country. The experimental results showed the promising efficacy of the proposed approach in suppressing false positive alarms with Trouble Ticket prediction.
Mulugeta Weldezgina Asres; Million Abayneh Mengistu; Pino Castrogiovanni; Lorenzo Bottaccioli; Enrico Macii; Edoardo Patti; Andrea Acquaviva. Supporting Telecommunication Alarm Management System With Trouble Ticket Prediction. IEEE Transactions on Industrial Informatics 2020, 17, 1459 -1469.
AMA StyleMulugeta Weldezgina Asres, Million Abayneh Mengistu, Pino Castrogiovanni, Lorenzo Bottaccioli, Enrico Macii, Edoardo Patti, Andrea Acquaviva. Supporting Telecommunication Alarm Management System With Trouble Ticket Prediction. IEEE Transactions on Industrial Informatics. 2020; 17 (2):1459-1469.
Chicago/Turabian StyleMulugeta Weldezgina Asres; Million Abayneh Mengistu; Pino Castrogiovanni; Lorenzo Bottaccioli; Enrico Macii; Edoardo Patti; Andrea Acquaviva. 2020. "Supporting Telecommunication Alarm Management System With Trouble Ticket Prediction." IEEE Transactions on Industrial Informatics 17, no. 2: 1459-1469.
Nearly 40% of primary energy consumption is related to the usage of energy in Buildings. Energy-related data such as indoor air temperature and power consumption of heating/cooling systems can be now collected due to the widespread diffusion of Internet-of-Things devices. Such energy data can be used (i) to train data-driven models than learn the thermal properties of buildings and (ii) to predict indoor temperature evolution. In this paper, we present a Grey-box model to estimate thermal dynamics in buildings based on Unscented Kalman Filter and thermal network representation. The proposed methodology has been applied in two different buildings with two different thermal network discretizations to test its accuracy in indoor air temperature prediction. Due to a lack of a real-world data sampled by Internet of Things (IoT) devices, a realistic data-set has been generated using the software Energy+, by referring to real industrial building models. Results on synthetic and realistic data show the accuracy of the proposed methodology in predicting indoor temperature trends up to the next 24 h with a maximum error lower than 2 °C, considering one year of data with different weather conditions.
Marco Massano; Edoardo Patti; Enrico Macii; Andrea Acquaviva; Lorenzo Bottaccioli. An Online Grey-Box Model Based on Unscented Kalman Filter to Predict Temperature Profiles in Smart Buildings. Energies 2020, 13, 2097 .
AMA StyleMarco Massano, Edoardo Patti, Enrico Macii, Andrea Acquaviva, Lorenzo Bottaccioli. An Online Grey-Box Model Based on Unscented Kalman Filter to Predict Temperature Profiles in Smart Buildings. Energies. 2020; 13 (8):2097.
Chicago/Turabian StyleMarco Massano; Edoardo Patti; Enrico Macii; Andrea Acquaviva; Lorenzo Bottaccioli. 2020. "An Online Grey-Box Model Based on Unscented Kalman Filter to Predict Temperature Profiles in Smart Buildings." Energies 13, no. 8: 2097.
In recent years, the contrast against energy waste and pollution has become mandatory and widely endorsed. Among the many actors at stake, the building sector energy management is one of the most critical. Indeed, buildings are responsible for 40% of total energy consumption only in Europe, affecting more than a third of the total pollution produced. Therefore, energy control policies of buildings (for example, forecast-based policies such as Demand Response and Demand Side Management) play a decisive role in reducing energy waste. On these premises, this paper presents an innovative methodology based on Internet-of-Things (IoT) technology for smart building indoor air-temperature forecasting. In detail, our methodology exploits a specialized Non-linear Autoregressive neural network for short- and medium-term predictions, envisioning two different exploitation: (i) on realistic artificial data and (ii) on real data collected by IoT devices deployed in the building. For this purpose, we designed and optimized four neural models, focusing respectively on three characterizing rooms and on the whole building. Experimental results on both a simulated and a real sensors dataset demonstrate the prediction accuracy and robustness of our proposed models.
Alessandro Aliberti; Lorenzo Bottaccioli; Enrico Macii; Santa Di Cataldo; Andrea Acquaviva; Edoardo Patti. A Non-Linear Autoregressive Model for Indoor Air-Temperature Predictions in Smart Buildings. Electronics 2019, 8, 979 .
AMA StyleAlessandro Aliberti, Lorenzo Bottaccioli, Enrico Macii, Santa Di Cataldo, Andrea Acquaviva, Edoardo Patti. A Non-Linear Autoregressive Model for Indoor Air-Temperature Predictions in Smart Buildings. Electronics. 2019; 8 (9):979.
Chicago/Turabian StyleAlessandro Aliberti; Lorenzo Bottaccioli; Enrico Macii; Santa Di Cataldo; Andrea Acquaviva; Edoardo Patti. 2019. "A Non-Linear Autoregressive Model for Indoor Air-Temperature Predictions in Smart Buildings." Electronics 8, no. 9: 979.
For planning and development and in real-time operation of smart grids, it is important to evaluate the impacts of photovoltaic (PV) distributed generation. In this paper, we present an integrated platform, constituted by two main components: a PV simulator and a real-time distribution network simulator. The first, designed and developed following the microservice approach and providing REST web services, simulates real-sky solar radiation on rooftops and estimates the PV energy production; the second, based on a digital real-time power systems simulator, simulates the behaviour of the electric network under the simulated generation scenarios. The platform is tested on a case study based on real data for a district of the city of Turin, Italy. In the results, we show possible applications of the platform for power flow forecasting during real-time operation and to detect possible voltage and transformers capacity problems during planning due to high penetration of Renewable Energy Sources. In particular, the results show that the case study distribution network, in the actual configuration, is not ready to accommodate all the generation capacity that can be installed as, in certain hours of the day and in certain days of the year, the capacity of some transformers is exceeded.
Lorenzo Bottaccioli; Abouzar Estebsari; Edoardo Patti; Enrico Pons; Andrea Acquaviva. Planning and real-time management of smart grids with high PV penetration in Italy. Proceedings of the Institution of Civil Engineers - Engineering Sustainability 2019, 172, 272 -282.
AMA StyleLorenzo Bottaccioli, Abouzar Estebsari, Edoardo Patti, Enrico Pons, Andrea Acquaviva. Planning and real-time management of smart grids with high PV penetration in Italy. Proceedings of the Institution of Civil Engineers - Engineering Sustainability. 2019; 172 (6):272-282.
Chicago/Turabian StyleLorenzo Bottaccioli; Abouzar Estebsari; Edoardo Patti; Enrico Pons; Andrea Acquaviva. 2019. "Planning and real-time management of smart grids with high PV penetration in Italy." Proceedings of the Institution of Civil Engineers - Engineering Sustainability 172, no. 6: 272-282.
Counterbalancing climate change is one of the biggest challenges for engineers around the world. One of the areas in which optimization techniques can be used to reduce energy needs, and with that the pollution derived from its production, is building design. With this study of a generic office located both in a northern country and in a temperate/Mediterranean site, we want to introduce a coding approach to dynamic energy simulation, able to suggest, from the early-design phases when the main building forms are defined, optimal configurations considering the energy needs for heating, cooling and lighting. Generally, early-design considerations of energy need reduction focus on the winter season only, in line with the current regulations; nevertheless a more holistic approach is needed to include other high consumption voices, e.g., for space cooling and lighting. The main considered design parameter is the WWR (window-to-wall ratio), even if further variables are considered in a set of parallel analyses (level of insulation, orientation, activation of low-cooling strategies including shading devices and ventilative cooling). Finally, the effect of different levels of occupancy was included in the analysis to regress results and compare the WWR with corresponding heating and cooling needs. This approach is adapted to Passivhaus design optimization, working on energy need minimisation acting on envelope design choices. The results demonstrate that it is essential to include, from the early-design configurations, a larger set of variables in order to optimize the expected energy needs on the basis of different aspects (cooling, heating, lighting, design choices). Coding is performed using Python scripting, while dynamic energy simulations are based on EnergyPlus.
Giacomo Chiesa; Andrea Acquaviva; Mario Grosso; Lorenzo Bottaccioli; Maurizio Floridia; Edoardo Pristeri; Edoardo Sanna. Parametric Optimization of Window-to-Wall Ratio for Passive Buildings Adopting A Scripting Methodology to Dynamic-Energy Simulation. Sustainability 2019, 11, 3078 .
AMA StyleGiacomo Chiesa, Andrea Acquaviva, Mario Grosso, Lorenzo Bottaccioli, Maurizio Floridia, Edoardo Pristeri, Edoardo Sanna. Parametric Optimization of Window-to-Wall Ratio for Passive Buildings Adopting A Scripting Methodology to Dynamic-Energy Simulation. Sustainability. 2019; 11 (11):3078.
Chicago/Turabian StyleGiacomo Chiesa; Andrea Acquaviva; Mario Grosso; Lorenzo Bottaccioli; Maurizio Floridia; Edoardo Pristeri; Edoardo Sanna. 2019. "Parametric Optimization of Window-to-Wall Ratio for Passive Buildings Adopting A Scripting Methodology to Dynamic-Energy Simulation." Sustainability 11, no. 11: 3078.
Predicting power demand of building heating systems is a challenging task due to the high variability of their energy profiles. Power demand is characterized by different heating cycles including sequences of various transient and steady-state phases. To effectively perform the predictive task by exploiting the huge amount of fine-grained energy-related data collected through Internet of Things (IoT) devices, innovative and scalable solutions should be devised. This paper presents PHi-CiB, a scalable full-stack distributed engine, addressing all tasks from energy-related data collection, to their integration, storage, analysis, and modeling. Heterogeneous data measurements (e.g., power consumption in buildings, meteorological conditions) are collected through multiple hardware (e.g., IoT devices) and software (e.g., web services) entities. Such data are integrated and analyzed to predict the average power demand of each building for different time horizons. First, the transient and steady-state phases characterizing the heating cycle of each building are automatically identified; then the power-level forecasting is performed for each phase. To this aim, PHi-CiB relies on a pipeline of three algorithms: the Exponentially Weighted Moving Average, the Multivariate Adaptive Regression Spline, and the Linear Regression with Stochastic Gradient Descent. PHi-CiB’s current implementation exploits Apache Spark and MongoDB and supports parallel and scalable processing and analytical tasks. Experimental results, performed on energy-related data collected in a real-world system show the effectiveness of PHi-CiB in predicting heating power consumption of buildings with a limited prediction error and an optimal horizontal scalability.
Andrea Acquaviva; Daniele Apiletti; Antonio Attanasio; Elena Baralis; Lorenzo Bottaccioli; Tania Cerquitelli; Silvia Chiusano; Enrico Macii; Edoardo Patti. Forecasting Heating Consumption in Buildings: A Scalable Full-Stack Distributed Engine. Electronics 2019, 8, 491 .
AMA StyleAndrea Acquaviva, Daniele Apiletti, Antonio Attanasio, Elena Baralis, Lorenzo Bottaccioli, Tania Cerquitelli, Silvia Chiusano, Enrico Macii, Edoardo Patti. Forecasting Heating Consumption in Buildings: A Scalable Full-Stack Distributed Engine. Electronics. 2019; 8 (5):491.
Chicago/Turabian StyleAndrea Acquaviva; Daniele Apiletti; Antonio Attanasio; Elena Baralis; Lorenzo Bottaccioli; Tania Cerquitelli; Silvia Chiusano; Enrico Macii; Edoardo Patti. 2019. "Forecasting Heating Consumption in Buildings: A Scalable Full-Stack Distributed Engine." Electronics 8, no. 5: 491.
In order to systematically shift existing control and management paradigms in distribution systems to new interoperable communication supported schemes in smart grids, we need to map newly developed use cases to standard reference models like Smart Grid Architecture Model (SGAM). From the other side, any new use cases should be tested and validated ex-ante before being deployed in the real-world system. Considering various types of actors in smart grids, use cases are usually tested using co-simulation platforms. Currently, there is no efficient co-simulation platform which supports interoperability analysis based on SGAM. In this paper, we present our developed test platform which offers a support to design new use cases based on SGAM. We used this platform to develop a new scheme for wide area monitoring of existing distribution systems under growing penetration of Photovoltaic production. Off-the-shelf solutions of state estimation for wide area monitoring are either used for passive distribution grids or applied to the active networks with wide measurement of distributed generators. Our proposed distribution state estimation algorithm does not require wide area measurements and relies on the data provided by a PV simulator we developed. This practical scheme is tested experimentally on a realistic urban distribution grid. The monitoring results shows a very low error rate of about 1 % by using our PV simulator under high penetration of PV with about 30 % error of load forecast. Using our SGAM-based platform, we could propose and examine an Internet-of-Things-based infrastructure to deploy the use case.
Abouzar Estebsari; Luca Barbierato; Alireza Bahmanyar; Lorenzo Bottaccioli; Enrico Macii; Edoardo Patti. A SGAM-Based Test Platform to Develop a Scheme for Wide Area Measurement-Free Monitoring of Smart Grids under High PV Penetration. Energies 2019, 12, 1417 .
AMA StyleAbouzar Estebsari, Luca Barbierato, Alireza Bahmanyar, Lorenzo Bottaccioli, Enrico Macii, Edoardo Patti. A SGAM-Based Test Platform to Develop a Scheme for Wide Area Measurement-Free Monitoring of Smart Grids under High PV Penetration. Energies. 2019; 12 (8):1417.
Chicago/Turabian StyleAbouzar Estebsari; Luca Barbierato; Alireza Bahmanyar; Lorenzo Bottaccioli; Enrico Macii; Edoardo Patti. 2019. "A SGAM-Based Test Platform to Develop a Scheme for Wide Area Measurement-Free Monitoring of Smart Grids under High PV Penetration." Energies 12, no. 8: 1417.
The range of operations of electric vehicles (EVs) is a critical aspect that may affect the user's attitude toward them. For manned EVs, range anxiety is still perceived as a major issue and recent surveys have shown that one-third of potential European users are deterred by this problem when considering the move to an EV. A similar consideration applies to aerial EVs for commercial use, where a careful planning of the flying range is essential not only to guarantee the service but also to avoid the loss of the EVs due to charge depletion during the flight. Therefore, route planning for EVs for different purposes (range estimation, route optimization) and/or application scenarios (terrestrial, aerial EVs) is an essential element to foster the acceptance of EVs as a replacement of traditional vehicles. One essential element to enable such accurate planning is an accurate model of the actual power consumption. While very elaborate models for the electrical motors of EVs do exist, the motor power does not perfectly match the power drawn from the battery because of battery non-idealities. In this paper, we propose a general methodology that allows to predict and/or optimize the operation range of EVs, by allowing different accuracy/complexity tradeoffs for the models describing the route, the vehicle, and the battery, and taking into account the decoupling between motor and battery power. We demonstrate our method on two use cases. The first one is a traditional driving range prediction for a terrestrial EV; the second one concerns an unmanned aerial vehicle, for which the methodology will be used to determine the energy-optimal flying speed for a set of parcel delivery tasks.
DonKyu Baek; Yukai Chen; Alberto Bocca; Lorenzo Bottaccioli; Santa Di Cataldo; Valentina Gatteschi; Daniele Jahier Pagliari; Edoardo Patti; Gianvito Urgese; Naehyuck Chang; Alberto Macii; Enrico Macii; Paolo Montuschi; Massimo Poncino. Battery-Aware Operation Range Estimation for Terrestrial and Aerial Electric Vehicles. IEEE Transactions on Vehicular Technology 2019, 68, 5471 -5482.
AMA StyleDonKyu Baek, Yukai Chen, Alberto Bocca, Lorenzo Bottaccioli, Santa Di Cataldo, Valentina Gatteschi, Daniele Jahier Pagliari, Edoardo Patti, Gianvito Urgese, Naehyuck Chang, Alberto Macii, Enrico Macii, Paolo Montuschi, Massimo Poncino. Battery-Aware Operation Range Estimation for Terrestrial and Aerial Electric Vehicles. IEEE Transactions on Vehicular Technology. 2019; 68 (6):5471-5482.
Chicago/Turabian StyleDonKyu Baek; Yukai Chen; Alberto Bocca; Lorenzo Bottaccioli; Santa Di Cataldo; Valentina Gatteschi; Daniele Jahier Pagliari; Edoardo Patti; Gianvito Urgese; Naehyuck Chang; Alberto Macii; Enrico Macii; Paolo Montuschi; Massimo Poncino. 2019. "Battery-Aware Operation Range Estimation for Terrestrial and Aerial Electric Vehicles." IEEE Transactions on Vehicular Technology 68, no. 6: 5471-5482.
In recent years, various online tools and databases have been developed to assess the potential energy output of photovoltaic (PV) installations in different geographical areas. However, these tools generally provide a spatial resolution of a few kilometers and, for a systematic analysis at large scale, they require continuous querying of their online databases. In this article, we present a methodology for fast estimation of the yearly sum of global solar irradiation and PV energy yield over large-scale territories. The proposed method relies on a multiple-regression model including only well-known geodata, such as latitude, altitude above sea level and average ambient temperature. Therefore, it is particularly suitable for a fast, preliminary, offline estimation of solar PV output and to analyze possible investments in new installations. Application of the method to a random set of 80 geographical locations throughout Europe and Africa yields a mean absolute percent error of 4.4% for the estimate of solar irradiation (13.6% maximum percent error) and of 4.3% for the prediction of photovoltaic electricity production (14.8% maximum percent error for free-standing installations; 15.4% for building-integrated ones), which are consistent with the general accuracy provided by the reference tools for this application. Besides photovoltaic potentials, the proposed method could also find application in a wider range of installation assessments, such as in solar thermal energy or desalination plants.
Alberto Bocca; Luca Bergamasco; Matteo Fasano; Lorenzo Bottaccioli; Eliodoro Chiavazzo; Alberto Macii; Pietro Asinari. Multiple-Regression Method for Fast Estimation of Solar Irradiation and Photovoltaic Energy Potentials over Europe and Africa. Energies 2018, 11, 3477 .
AMA StyleAlberto Bocca, Luca Bergamasco, Matteo Fasano, Lorenzo Bottaccioli, Eliodoro Chiavazzo, Alberto Macii, Pietro Asinari. Multiple-Regression Method for Fast Estimation of Solar Irradiation and Photovoltaic Energy Potentials over Europe and Africa. Energies. 2018; 11 (12):3477.
Chicago/Turabian StyleAlberto Bocca; Luca Bergamasco; Matteo Fasano; Lorenzo Bottaccioli; Eliodoro Chiavazzo; Alberto Macii; Pietro Asinari. 2018. "Multiple-Regression Method for Fast Estimation of Solar Irradiation and Photovoltaic Energy Potentials over Europe and Africa." Energies 11, no. 12: 3477.
Lorenzo Bottaccioli; Santa Di Cataldo; Andrea Acquaviva; Edoardo Patti. Realistic Multi-Scale Modeling of Household Electricity Behaviors. IEEE Access 2018, 7, 2467 -2489.
AMA StyleLorenzo Bottaccioli, Santa Di Cataldo, Andrea Acquaviva, Edoardo Patti. Realistic Multi-Scale Modeling of Household Electricity Behaviors. IEEE Access. 2018; 7 ():2467-2489.
Chicago/Turabian StyleLorenzo Bottaccioli; Santa Di Cataldo; Andrea Acquaviva; Edoardo Patti. 2018. "Realistic Multi-Scale Modeling of Household Electricity Behaviors." IEEE Access 7, no. : 2467-2489.
Marco Ravina; Edoardo Patti; Lorenzo Bottaccioli; Deborah Panepinto; Andrea Acquaviva; Maria Chiara Zanetti. IMPLEMENTING AIR-POLLUTION AND HEALTH-DAMAGE COSTS IN URBAN MULTI-ENERGY SYSTEMS MODELLING. Air Pollution XXVI 2018, 230, 95 -106.
AMA StyleMarco Ravina, Edoardo Patti, Lorenzo Bottaccioli, Deborah Panepinto, Andrea Acquaviva, Maria Chiara Zanetti. IMPLEMENTING AIR-POLLUTION AND HEALTH-DAMAGE COSTS IN URBAN MULTI-ENERGY SYSTEMS MODELLING. Air Pollution XXVI. 2018; 230 ():95-106.
Chicago/Turabian StyleMarco Ravina; Edoardo Patti; Lorenzo Bottaccioli; Deborah Panepinto; Andrea Acquaviva; Maria Chiara Zanetti. 2018. "IMPLEMENTING AIR-POLLUTION AND HEALTH-DAMAGE COSTS IN URBAN MULTI-ENERGY SYSTEMS MODELLING." Air Pollution XXVI 230, no. : 95-106.
The fundamental role of temperature in relation to solar cell efficiency in photovoltaic (PV) installations, is well established and documented in the scientific community. Correlation between PV cell temperature and performance efficiency is described generally as a function of climatic and installation conditions, which depend on location and PV panel mounting, respectively. In this paper, we propose a very simple yet efficient mathematical model, which can perform an accurate analysis of temperature-dependent losses in PV installations using well-known geodata, especially latitude. After creating a number of models, the best was applied to 50 different locations in Europe. The maximum absolute estimation difference of the efficiency due to temperature is always within 1.6%, and the mean absolute error within 0.6%, with respect to PVGIS, which is a reference web tool for PV performance assessment.
Alberto Bocca; Lorenzo Bottaccioli; Alberto Macii. Temperature Efficiency Analysis in Photovoltaics Using Basic Geodata: Application to Europe. 2018 International Symposium on Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM) 2018, 823 -828.
AMA StyleAlberto Bocca, Lorenzo Bottaccioli, Alberto Macii. Temperature Efficiency Analysis in Photovoltaics Using Basic Geodata: Application to Europe. 2018 International Symposium on Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM). 2018; ():823-828.
Chicago/Turabian StyleAlberto Bocca; Lorenzo Bottaccioli; Alberto Macii. 2018. "Temperature Efficiency Analysis in Photovoltaics Using Basic Geodata: Application to Europe." 2018 International Symposium on Power Electronics, Electrical Drives, Automation and Motion (SPEEDAM) , no. : 823-828.
Sara Vinco; Lorenzo Bottaccioli; Edoardo Patti; Andrea Acquaviva; Enrico Macii; Massimo Poncino. GIS-based optimal photovoltaic panel floorplanning for residential installations. 2018 Design, Automation & Test in Europe Conference & Exhibition (DATE) 2018, 1 .
AMA StyleSara Vinco, Lorenzo Bottaccioli, Edoardo Patti, Andrea Acquaviva, Enrico Macii, Massimo Poncino. GIS-based optimal photovoltaic panel floorplanning for residential installations. 2018 Design, Automation & Test in Europe Conference & Exhibition (DATE). 2018; ():1.
Chicago/Turabian StyleSara Vinco; Lorenzo Bottaccioli; Edoardo Patti; Andrea Acquaviva; Enrico Macii; Massimo Poncino. 2018. "GIS-based optimal photovoltaic panel floorplanning for residential installations." 2018 Design, Automation & Test in Europe Conference & Exhibition (DATE) , no. : 1.
Alessandro Aliberti; Lorenzo Bottaccioli; Giansalvo Cirrincione; Enrico Macii; Andrea Acquaviva; Edoardo Patti. Forecasting Short-term Solar Radiation for Photovoltaic Energy Predictions. Proceedings of the 7th International Conference on Smart Cities and Green ICT Systems 2018, 44 -53.
AMA StyleAlessandro Aliberti, Lorenzo Bottaccioli, Giansalvo Cirrincione, Enrico Macii, Andrea Acquaviva, Edoardo Patti. Forecasting Short-term Solar Radiation for Photovoltaic Energy Predictions. Proceedings of the 7th International Conference on Smart Cities and Green ICT Systems. 2018; ():44-53.
Chicago/Turabian StyleAlessandro Aliberti; Lorenzo Bottaccioli; Giansalvo Cirrincione; Enrico Macii; Andrea Acquaviva; Edoardo Patti. 2018. "Forecasting Short-term Solar Radiation for Photovoltaic Energy Predictions." Proceedings of the 7th International Conference on Smart Cities and Green ICT Systems , no. : 44-53.
One of the ambitious goals of the "Smart city" paradigm is to design zero-energy buildings. Buildings can be considered as connected cyber-physical systems that require the construction of sound methodologies inherited from the Electronic Design Automation (EDA) research. In particular, aiming at autonomous buildings, the effective design of renewable energy sources is a key aspect for which such methodologies have to be developed. In this work, we propose a modeling strategy for the early estimation of the performance of photovoltaic (PV) arrays. Although a plethora of PV panel models there exists, most of these models suffer from accuracy/complexity tradeoffs. On one hand, building fast models forces to ignore either the correlation between temperature and irradiance, or the topology of panels, thus yielding inaccurate estimations. On the other, more accurate models are time consuming and require costly measurements or circuit analysis, that cannot be extracted from the sole datasheet. This paper proposes a compact semi-empirical model, suitable for real time simulation and built solely from information derived from the PV panel datasheet. The model is built by empirically fitting an expression of the panel operating point as a function of both irradiance and temperature, and of the adopted PV system topology. The accuracy and effectiveness of the proposed model have been validated w.r.t. the production traces of the PV systems of a real world industrial building.
Sara Vinco; Lorenzo Bottaccioli; Edoardo Patti; Andrea Acquaviva; Massimo Poncino. A Compact PV Panel Model for Cyber-Physical Systems in Smart Cities. 2018 IEEE International Symposium on Circuits and Systems (ISCAS) 2018, 1 -5.
AMA StyleSara Vinco, Lorenzo Bottaccioli, Edoardo Patti, Andrea Acquaviva, Massimo Poncino. A Compact PV Panel Model for Cyber-Physical Systems in Smart Cities. 2018 IEEE International Symposium on Circuits and Systems (ISCAS). 2018; ():1-5.
Chicago/Turabian StyleSara Vinco; Lorenzo Bottaccioli; Edoardo Patti; Andrea Acquaviva; Massimo Poncino. 2018. "A Compact PV Panel Model for Cyber-Physical Systems in Smart Cities." 2018 IEEE International Symposium on Circuits and Systems (ISCAS) , no. : 1-5.
In recent years, many governments are promoting a widespread deployment of Renewable Energy Sources (RES) together with an optimization of energy consumption. The main purpose consists on decarbonizing the energy production and reducing the CO2 footprints. However, RES imply uncertain energy production. To foster this transition, we need novel tools to model and simulate Multi-Energy-Systems combining together different technologies and analysing heterogeneous information, often in (near-) real-time. In this paper, first we present the main challenges identified after a literature review and the motivation that drove this research in developing MESsi. Then, we propose MESsi, a novel distributed infrastructure for modelling and cosimulating Multi-Energy-Systems. This infrastructure is a framework suitable for general purpose energy simulations in cities. Finally, we introduce possible simulation scenarios that have different spatio-temporal resolutions. Space resolution ranges from the
Lorenzo Bottaccioli; Edoardo Patti; Enrico Macii; Andrea Acquaviva. Distributed Infrastructure for Multi-Energy-Systems Modelling and Co-simulation in Urban Districts. Proceedings of the 7th International Conference on Smart Cities and Green ICT Systems 2018, 262 -269.
AMA StyleLorenzo Bottaccioli, Edoardo Patti, Enrico Macii, Andrea Acquaviva. Distributed Infrastructure for Multi-Energy-Systems Modelling and Co-simulation in Urban Districts. Proceedings of the 7th International Conference on Smart Cities and Green ICT Systems. 2018; ():262-269.
Chicago/Turabian StyleLorenzo Bottaccioli; Edoardo Patti; Enrico Macii; Andrea Acquaviva. 2018. "Distributed Infrastructure for Multi-Energy-Systems Modelling and Co-simulation in Urban Districts." Proceedings of the 7th International Conference on Smart Cities and Green ICT Systems , no. : 262-269.