Embedded IoT platforms are frequently deployed in hostile or physically exposed environments, where compromise of the operating system is a realistic threat. In conventional deployments, OpenSSL executes entirely in user space, leaving cryptographic keys and intermediate material potentially exposed in the presence of a compromised OS or privileged malware. This work presents a portable OpenSSL engine that enables secure and transparent offloading of cryptographic operations to OP-TEE, a software stack leveraging a Trusted Execution Environment (TEE) to confine cryptographic key material and security-critical computations within secure-world memory without modifying existing applications or altering the OpenSSL EVP interface. The proposed engine uses a GlobalPlatform Client API implementation as an underlying communication layer and supports both Copy-Based (CB) data transfer and a Shared-Memory (SM) mode, enabling performance tuning under the resource constraints typical of embedded IoT platforms. An experimental evaluation on an NXP i.MX7 industrial gateway shows that secure-world execution introduces bounded overhead dominated by REE–TEE transitions and memory-management operations. For bandwidth-intensive primitives such as SHA-256, SM mode improves throughput by approximately 10–15% over CB transfers. For symmetric encryption algorithms such as AES-256-CBC, SM communication provides a substantial throughput improvement of approximately 40%, indicating that data-movement overhead plays a significant role on embedded platforms. In contrast, asymmetric primitives (e.g. RSA-1024) remain largely insensitive to transfer optimisations because they operate on small, fixed-size operands, making the overall execution time dominated by invocation overheads and internal big-number computations rather than data movement.

Multi-Energy Systems (MES) represent a paradigm in which various energy systems, such as buildings, power grids, and heating networks, are integrated to operate in a cohesive manner. These systems provide a significant opportunity to improve technical, economic, and environmental outcomes. However, the complexity of MES poses challenges in accurately capturing their interdisciplinary interactions using standalone simulations, which often do not adequately model their interconnected nature. Co-simulation frameworks have emerged as a promising solution to this challenge, enabling the coordination of multiple simulators within a single scenario. However, computationally intensive simulators can often hinder scalability, especially as the complexity of the simulations increases. This paper proposes a methodology to apply vertical scalability techniques to simulators and optimise each single-node performance in co-simulation frameworks. Using COESI, a Mosaik-based co-simulation platform, it was possible to systematically test these techniques by increasing the number of model entities a simulator class must manage in a complex MES scenario. This study highlights selective parallelisation as a key strategy for optimising computational efficiency in MES co-simulation, offering insights to scale complex energy systems effectively. The results demonstrate significant performance gains, particularly for CPU-intensive tasks with stateless simulators, such as executing intensive numerical computations.

Precise residential load modeling is indispensable for crafting effective demand-side management strategies and simulating realistic household power consumption under diverse conditions. This paper introduces a novel generative framework, leveraging the power of Generative Adversarial Networks (GANs), to synthesize highly realistic daily activity patterns. By training on detailed Italian time-use data, the model captures nuanced behavioral statistics, reflecting the inherent variability of human routines. Furthermore, incorporating conditional generation based on the day of the week allows for contextually rich and adaptable simulations, capturing weekly lifestyle variations. Household power profiles are reconstructed by meticulously mapping the generated activities to the characteristic power signatures of common household appliances, resulting in simulations that exhibit strong concordance with empirical load data at both granular, appliance-level, and aggregated household levels. Critically, our GAN-based approach demonstrably accelerates simulation throughput compared to conventional Markov chain methodologies, enabling the efficient and scalable analysis of complex residential energy scenarios, and opening avenues for real-time applications and large-scale urban energy studies.

Integrated modeling and simulation are crucial for optimizing cities’ energy planning. Existing sector-specific analyses have implementation limitations in representing interactions across infrastructures, limiting optimization potentials. An integrated framework simulating multiple interacting components from a systemic perspective could reveal efficiency gains, flexibility, and synergies across urban energy networks to guide sustainable energy transitions. Co-simulation approaches are gaining attention for analyzing complex interconnected systems like District Heating (DH). Traditional single-discipline models present limitations in fully representing the interconnectivity between district heating networks and related subsystems, such as those in buildings and energy generation. Therefore, we propose a co-simulation based framework to simulate DH system behavior while easily integrating models of other subsystems and Functional Mock-up Unit (FMU) simulators. We tested this Plug&Play modular framework for Demand Side Management (DSM) and Storage-based strategies, evaluating their effectiveness in peak reduction while lowering the temperatures of the network.

Wave Energy is one of the renewable sources with greatest potential. Since power coming from waves fluctuates, the grid integration of wave energy involves several power conditioning stages to comply with grid quality requirements. However, to ensure full integration of wave energy in a smart grid scenario and unlock advanced monitoring and control techniques (e.g. Demand/Response), it is crucial to forecast the output power. This work proposes a methodology to forecast in short-term horizons (i.e. 15 min to 240 min) the power delivered to the grid of the Inertial Sea Wave Energy Converter (ISWEC), a device that harnesses wave power through the inertial effect of a gyroscope. Therefore, we designed, optimized and compared the performance of five known machine learning techniques for time series point forecasting: Random Forest, Support Vector Regression, Long Short-Term Memory Neural Network, Transformer Neural Network and 1 Dimensional Convolutional Neural Network. Additionally, we studied the efficacy of downsampling technique aggregating original dataset sampled every 0.1 s in time steps of 1min, 3min, 5min and 15min to compare the performance behaviour of the different machine learning models for these datasets. Furthermore, we implemented Prediction Intervals (PIs) to calculate the inherent uncertainties associated with the previously mentioned machine learning techniques. These PIs were built based on the Non-Parametric Kernel Density Estimator technique. The point forecasting and the PIs results showed that models’ performance improved as the downsampling increased. Moreover, the Random Forest model was the worst-performing in all cases. Finally, none of the other models can be considered the best overall.

Heating, Ventilation and Air Conditioning (HVAC) optimization for energy consumption reduction is becoming ever more a topic of the utmost environmental and energetic concerns. The two most employed methodologies for optimizing HVAC systems are Model Predictive Control (MPC) and Reinforcement Learning (RL). This paper compares three different RL approaches to HVAC optimization: one based on a black-box system identification model trained on historical data, one based on a white-box model of a building and one online method based on an imitation learning pretraining phase on historical data. The three approaches are compared with a literature baseline and an EnergyPlus baseline. Results show that the overall best method in terms of energy consumption reduction (65% decrease) and thermal comfort increase (25% increase) is the approach based on the white-box model. However, the proposed methodology, based on online and imitation learning, demonstrates remarkable efficiency, achieving comparable improvements in energy consumption after just a few months of online training, while maintaining thermal comfort at around the same level as the baseline. These results prove a direct online RL approach, which avoid the use of costly simulations, can provide a reliable and inexpensive solution to the problem of HVAC optimization.

Nonintrusive load monitoring allows to estimate the energy consumption of major household appliances by just analyzing the aggregated power consumption collected at the main meter of the house. Recent disaggregation algorithms based on deep learning techniques showed superior performance with respect to previous methods. However, they require a large amount of submeter data to be trained. In this work, we present a new solution for training nonintrusive load monitoring algorithms without any supervision from submeters. To achieve this goal, we divided the disaggregation algorithm into two stages—appliance detectionandstate-based disaggregation. In the first stage, we aim at identifying the start and stop times of the individual appliance operations within the whole-house power signal. In the second stage, we reconstruct the power signature of the target device by exploiting appliance-specific power states learned in the house. We tested our methodology on fridges, washing machines, and dishwashers in a public dataset, showing double-digit improvements with respect to previous methods trained with submeter data. Most importantly, the proposed solution allows to collect a large number of appliance power signatures with minor costs, thus helping to achieve the generalization capabilities required by a real-world disaggregation system.

Electric Vehicles (EVs) provide an alternative to traditional mobility and a sustainable means of transportation. As a result, electric vehicle sales are increasing across Europe, prompting researchers to wonder about the impact of EVs on smart grids. The proposed framework simulates users’ activities, highly characterising individual behaviour using Time Use Survey (TUS) data to estimate EV usage and consumption. Then, for each trip, the routes between origin and destination are determined, simulating in separate modules i) the driving behaviour, ii) the motion of the EV and its discharge considering spatial data and iii) the charge considering users’ preference. Thanks to the spatial information openly available, it is possible to characterise the simulation and improve EV consumption estimation. Different scenarios are analysed to demonstrate the versatility of the proposed framework by exploiting its modularity. The individuals’ heterogeneity is considered by using an agent-oriented approach. Furthermore, the simulation proceeds on a time-step basis to enable the use of the simulator in a co-simulation environment for future purposes, such as the integration of power networks. The results indicate that achieving a high realism with limited, i.e. containing scarce data for the problem under study, is feasible, enabling researchers to make informed decisions about future mobility.

With the growth of tourism industry, airplanes have became an affordable choice for medium- and long-distance travels. Accurate forecasting of flights tickets helps the aviation industry to match demand, supply flexibly and optimize aviation resources. Airline companies use dynamic pricing strategies to determine the price of airline tickets to maximize profits. Passengers want to purchase tickets at the lowest selling price for the flight of their choice. However, airline tickets are a special commodity that is time-sensitive and scarce, and the price of airline tickets is affected by various factors.Our research work provides a systematic comparison of various traditional machine learning methods (i.e., Ridge Regression, Lasso Regression, K-Nearest Neighbor, Decision Tree, XGBoost, Random Forest) and deep learning methods (e.g., Fully Connected Networks, Convolutional Neural Networks, Transformer) to address the problem of airfare prediction, by keeping the consumers’ needs. Moreover, we proposed innovative Bayesian neural networks, which represent the first exploitation attempt of Bayesian Inference for the airfare prediction task, to the best of our knowledge. Therefore, we evaluate the performance of our implemented and optimized models on an open dataset. The experimental results show that deep learning-based methods achieve better results on average than traditional ones, while Bayesian neural networks can achieve better performance among the other machine learning methods. However, taking into account both prediction performance and computational time, the Random Forest turns out to be the best choice to apply in this scenario.

In recent times, the consistent growth of wave energy makes it one of the most promising forms of renewable energy. Due to the intermittency and non-stationary nature of waves, the grid integration of these renewable energy sources involves a series of complex power conditioning stages to deliver grid electric power that meets the corresponding quality standards. Furthermore, to enable optimal management and operation of a smart grid power system, forecasting the wave power delivered to the grid is essential. In this paper, we present a novel approach based on Long Short-Term Memory Neural Network to forecast the wave power delivered to the grid of a Wave Energy Converter (WEC) - the ISWEC, which is a device able to harvest sea energy by exploiting the inertial effect of a gyroscope - in short-time horizons (e.g. 1min). The data for the analysis was obtained from a simulator that combines a model of the ISWEC device and the power conditioning grid integration for this particular WEC. In addition, to investigate the effectiveness of downsampling, we compared the performance behavior of the raw dataset and downsampled versions of it. The results showed that as the downsampling increases, so does the forecasting accuracy: the forecasting performance of the raw dataset returned the worst results, while the one of the dataset with the biggest downsampling studied returned the best.

Electric vehicles (EVs) have been globally recognized as a reliable alternative to fossil fuel vehicles. The core component of an electric vehicle is its rechargeable battery pack. However, there still needs to be large-scale publicly available EV data to investigate and distribute effective solutions to monitor the conditions of the EV’s battery pack. Hence, we propose an EV simulator that generates EV battery pack internal signals starting from the input driving cycle. The simulated data resemble the behavior of a multi-cell EV battery pack undergoing the user’s utilization of the EV. The simulated data include vehicle speed, voltage, current, State of Charge (SOC), and internal temperature of the battery pack. The virtual-EV model simulator, including the battery pack subsystem, has been tuned using real-world EV data-sheet information. The battery pack embeds thermal and aging models for further realism, influencing the output signals given the environmental temperature and the battery’s State of Health (SOH). The data generated by the virtual EV simulator have been validated with real EV data signals sampled by an equivalent real-world EV. The data comparison yields a minimum R2value of 0.94 and a Root Mean Squared Error not higher than 2.74V for the battery pack’s voltage and SOC, respectively.

Additive Manufacturing (AM), a cornerstone of Industry 4.0, is expected to revolutionise production in practically all industries. However, multiple production challenges still exist, preventing its diffusion. In recent years, Machine Learning algorithms have been employed to overcome these hurdles. Nonetheless, the usage of these algorithms is constrained by the scarcity of data together with the challenges associated with accessing and integrating the information generated during the AM pipeline. In this work, we present a vendor-agnostic platform for AM that enables collecting, storing, analysing and linking the heterogeneous data of the complete AM process. We conducted an extensive analysis of the different AM datatypes and identified the most suitable technologies for storing them. Furthermore, we performed an in-depth study of the requirements of different AM stakeholders to develop a rich and intuitive Graphical User Interface. We showcased the specific usage of the platform for Powder Bed Fusion, one of the most popular AM processes, in a real industrial scenario, integrating specific existing modules for in-situ monitoring and real-time defect detection.

In smart grids, consumers can be involved in demand response programs to reduce the total power consumption of their households during the peak hours of the day. Unfortunately, nowadays, utility companies are facing important challenges in the implementation of demand response programs because of their negative impact on the comfort of end-users. In this article, we cluster the different operation modes of household appliances based on the analysis of their power signatures. For this purpose, we implement an autoencoder neural network to create a better data representation of the power signatures. Then, we cluster the different operational programs by using aK-means algorithm fitted to the new data representation. To test our methodology, we study the operation modes of some washing machines and dishwashers whose power signatures were derived from both submeters and nonintrusive load monitoring techniques. Our clustering analysis reveals the existence of multiple working programs showing well-defined features in terms of both average energy consumption and duration. Our results can then be used to improve demand response programs by reducing their impact on the comfort of end-users. Furthermore, end-users can rely on our framework to favor lighter operation modes and reduce their overall energy consumption.

Li-ion batteries have become the most important technology for electric mobility. One of the most pressing chal-lenges is the development of reliable methods for battery state-of-health (SOH) diagnosis and estimation of remaining useful life. In electric mobility scenario, battery capacity degradation prediction is crucial to ensure service availability and life duration. This research work provides a comprehensive comparative analysis of neural networks for a data-driven approach suitable for SOH estimation on single cells, stressed under laboratory conditions. For this purpose, different neural networks (i.e., LSTM, GRU, 1D-CNN, CNN-LSTM) are trained and optimized on NASA Randomized Battery Usage dataset. Experimental results demonstrate that data-driven neural networks generally performed well SOH estimation on single cells. In detail, the 1D-CNN best predicts SOH and has the lowest variance in the output. The LSTM have the highest variance in estimating SOH, while GRU and CNN-LSTM tend to overestimate and underestimate the value of SOH, respectively.

The ongoing energy transition to reduce carbon emissions presents some of the most formidable challenges the energy sector has ever experienced, requiring a paradigm change that involves diverse players and heterogeneous concerns, including regulations, economic drivers, societal, and environmental aspects. Central to this transition is the adoption of integrated Multi-Energy Systems (MES) to efficiently produce, distribute, store, and convert energy among different vectors. A deep understanding of MES is fundamental to harness the potential for energy savings and foster energy transition towards a low carbon future. Unfortunately, the inherent complexity of MES makes them extremely difficult to analyze, understand, design and optimize. This work proposes a digital twin co-simulation platform that provides a structured basis to design, develop and validate novel solutions and technologies for multi-energy system. The platform will enable the definition of a virtual representation of the real-world (digital twin) as a composition of models (co-simulation) that analyze the environment from multiple viewpoints and at different spatio-temporal scales.

Electric infrastructures have been pushed forward to handle tasks they were not originally designed to perform. To improve reliability and efficiency, state-of-the-art power grids include improved security, reduced peak loads, increased integration of renewable sources, and lower operational costs. In this framework, “smart grids” are built around bidirectional communication technologies, where “smart meters” communicate with all other entities and collect data from the power grid, offering specific features to each actor playing in the energy marketplace. In this article, to overcome some of the challenges raised by smart grids and smart meters, we propose a distributed metering infrastructure, which provides bidirectional communication, self-configuration, and autoupdate capabilities. Our 3-phase smart meters follow the basics Internet of Things principles and have the ability to run, either onboard or distributed on the network, multiple algorithms for smart grid management. These algorithms can be freely added, updated, or removed on the fly, thanks to the autoupdate feature of the system. Moreover, to reduce costs and improve scalability, we prove that it is possible to implement our smart meters using only off-the-shelf and inexpensive hardware devices. A digital real-time simulator (i.e., Opal-RT) has been used to assess the capabilities of both the infrastructure and the meter. Our experimental analysis shows that the latency introduced by the data transmission over the Internet is compliant with the limits imposed by the IEC 61850 standard. As a consequence, our architecture does not affect the operational status of the smart grid, making it a viable solution to support the deployment of novel services.

Online non-intrusive load monitoring algorithms have captivated academia and industries as parsimonious solutions for household energy efficiency monitoring as well as a safety control, anomaly detection, and demand-side management. However, the computational energy cost for executing such algorithms should not overcome the promised energy efficiency from the disaggregated appliance specific consumption information feed-backs. Moreover, the energy efficiency of cloud computing systems is also becoming a concern for the environment due to carbon emission. This study analyzes the energy spent to execute NILM algorithms via computation cost estimation and prediction using computing system-level power monitoring and data-driven approaches. A generic framework for an automated algorithm cost monitoring and modeling methodologies is devised for large load scale deployment of Cloud-based Online-NILM algorithms. The efficacy of the proposed approach was examined and validated on two computing system use-cases, i.e., Dedicated Server and Cloud Virtual Server. The prediction models, developed using statistical and machine learning tools, demonstrate the promising applicability of the data-driven approach with a very high prediction accuracy without detailed knowledge of the computing systems and the algorithm.

PhotoVoltaic (PV) installations are a widespread source of renewable energy, and are quite common urban buildings’ roofs. To soften both the initial investment and the recurrent maintenance costs, the current market trends delegate the construction of PV installations to Energy Aggregators, i.e., grouping of consumers and producers that act as a single entity to satisfy local energy demand and to sell the surplus energy to the grid. In this perspective, PV installations can be designed with a larger perspective, i.e., at district level, to maximize power production not of a single building but rather of a number of blocks of a city. This implies new challenges, including efficient data management (the covered area can be squared kilometers wide) and optimal PV installation (the number of PV modules can be in the order of hundreds or even thousands). This paper proposes a framework to combine detailed geographic and irradiance information to determine an optimal PV installation over a district, by maximizing both power production and economic convenience. Our simulation results run on a real-world district prove that the framework allows an advanced evaluation of costs and benefit, that can be used by Energy Aggregators to design a new PV installation, and demonstrate an improvement on power generation up to 20% w.r.t. standard installations.

In the context of Industry 4.0, metal Additive Manufacturing (AM) is considered a promising technology for medical, aerospace and automotive fields. However, the lack of assurance of the quality of the printed parts can be an obstacle for a larger diffusion in industry. To this date, AM is most of the times a trial-and-error process, where the faulty artefacts are detected only after the end of part production. This impacts on the processing time and overall costs of the process. A possible solution to this problem is the in-situ monitoring and detection of defects, taking advantage of the layer-by-layer nature of the build. In this paper, we describe a system for in-situ defects monitoring and detection for metal Powder Bed Fusion (PBF), that leverages an off-axis camera mounted on top of the machine. A set of fully automated algorithms based on Computer Vision and Machine Learning allow the timely detection of a number of powder bed defects and the monitoring of the object's profile for the entire duration of the build.

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.

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 have 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 article, 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 Italy. The experimental results showed the promising efficacy of the proposed approach in suppressing false positive alarms with trouble ticket prediction.

The design of photovoltaic (PV) installations mostly relies on rule-of-thumb criteria and on gross estimates of the shading patterns, and the few optimized approaches are generally focused on the problem of identifying the most suitable surfaces (e.g., roofs) in a larger geographic area (e.g., city or district). This article proposes a framework to address the design and the optimization of PV installations through a set of microservices focusing on the different variables of the design: identification of the target surfaces, elaboration of weather data, modeling of the PV panel, and floorplanning of the panel on the surface. The microservices architecture ensures extensibility and generality, as the user may execute only a subset of the proposed services or provide novel algorithms to extend the existing ones. Additionally, the framework provides a set of built-in models that allow sensitivity to the distribution of shades and accurate modeling of the power production over time. We show the many benefits of the proposed framework on two different use cases.

The growing interest in Multi-Energy Systems (MES) leads the scientific community to implement innovative technologies to analyse and simulate these complex systems. Two main research trends are identified in such analysis: i) improve the usability and capability of preexisting reference architectures in the energy field to cope with high-level use case descriptions, and ii) study the interoperability of such reference architectures in order to increase systematic and functional analysis of MES use cases. GAMES is a a general-purpose architectural model for MES engineering application. The aim is twofold: i) GAMES implements an extension of Smart Grid Architecture Model (SGAM) to cope with MES use case descriptions, and ii) it offers a methodology to deal with a systemic description of the use case through a combination of UML and SysML integrated in the proposed architectural model. Furthermore, GAMES will allow the implementation of Domain Specific Language (DSL) and hardware configuration for the specific components described by UML/SysML diagrams. Compared to other solutions, GAMES allows to assess both research trends in a single hierarchical ICT infrastructure.

Following the Smart Grid view, current energy generation systems based on fossil fuels will be replaced with renewable energy sources. Photovoltaic (PV) is currently considered the most promising technology, due to decreasing costs of the devices and to the limited invasiveness in existing infrastructures, that make PV installations quite common urban buildings' roofs. To maximise both power production and Return Of Investment (ROI) of PV installations, new techniques and methodologies should be applied to limit sources of inefficiencies, like shading and power losses due to an incorrect installation. In this paper, we propose a novel solution for an optimal configuration and placement of PV systems in buildings' roofs. Given a number of alternative configurations and a roof of interest, it combines detailed geographic and irradiance information to determine the optimal PV installation, by maximizing both power production and ROI. Our simulation results on two real-world roofs demonstrate an improvement on power generation up to 23% w.r.t. standard compact installations. These results also highlight that a cost analysis, often ignored by standard installation strategies, is nonetheless necessary to guarantee optimal results in terms of PV production and revenue.

In this paper, we present a novel distributed framework for real-time management and co-simulation of demand response (DR) in smart grids. Our solution provides a (near-) real-time co-simulation platform to validate new DR-policies exploiting Internet-of-Things approach performing software-in-the-loop. Hence, the behavior of real-world power systems can be emulated in a very realistic way and different DR-policies can be easily deployed and/or replaced in a plug-and-play fashion, without affecting the rest of the framework. In addition, our solution integrates real Internet-connected smart devices deployed at customer premises and along the smart grid to retrieve energy information and send actuation commands. Thus, the framework is also ready to manage DR in a real-world smart grid. This is demonstrated on a realistic smart grid with a test case DR-policy.

Shading is a crucial issue for the placement of PV installations, as it heavily impacts power production and the corresponding return of investment. Nonetheless, residential rooftop installations still rely on rule-of-thumb criteria and on gross estimates of the shading patterns, while more optimized approaches focus solely on the identification of suitable surfaces (e.g., roofs) in a larger geographic area (e.g., city or district). This work addresses the challenge of identifying an optimal (with respect to the overall energy production) placement of PV panels on a roof. The novel aspect of the proposed solution lies in the possibility of having a sparse, irregular placement of individual modules so as to better exploit the variance of solar data. The latter are represented in terms of the distribution of irradiance and temperature values over the roof, as elaborated from historical traces and Geographical Information System (GIS) data. Experimental results will prove the effectiveness of the algorithm through three real world case studies, and that the generated optimal solutions allow to increase power production by up to 28% with respect to rule-of-thumb solutions.

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.

In recent years, the research about energy waste and CO2 emission reduction has gained a strong momentum, also pushed by European and national funding initiatives. The main purpose of this large effort is to reduce the effects of greenhouse emission, climate change to head for a sustainable society. In this scenario, Information and Communication Technologies (ICT) play a key role. From one side, advances in physical and environmental information sensing, communication and processing, enabled the monitoring of energy behaviour of buildings in real-time. The access to this information has been made easy and ubiquitous thank to Internet-of-Things (IoT) devices and protocols. From the other side, the creation of digital repositories of buildings and districts (i.e. Building Information Models - BIM) enabled the development of complex and rich energy models that can be used for simulation and prediction purposes. As such, an opportunity is emerging of mixing these two information categories to either create better models and to detect unwanted or inefficient energy behaviours. In this paper, we present a software architecture for management and simulation of energy behaviours in buildings that integrates heterogeneous data such as BIM, IoT, GIS (Geographical Information System) and meteorological services. This integration allows: i) (near-) real-time visualisation of energy consumption information in the building context and ii) building performance evaluation through energy modelling and simulation exploiting data from the field and real weather conditions. Finally, we discuss the experimental results obtained in a real-world case-study.

Due to the increasing penetration of distributed generation, storage, electric vehicles, and new information communication technologies, distribution networks are evolving toward the smart grid paradigm. For this reason, new control strategies, algorithms, and technologies need to be tested and validated before their actual field implementation. In this paper, we present a novel modular distributed infrastructure, based on real-time simulation, for multipurpose smart grid studies. The different components of the infrastructure are described, and the system is applied to a case study based on a real urban district located in northern Italy. The presented infrastructure is shown to be flexible and useful for different and multidisciplinary smart grid studies.

This paper presents an Internet-of-Things software infrastructure that enables energy management and simulation of new control policies in a city district. The proposed platform enables the interoperability and the correlation of (near-)real-time building energy profiles with environmental data from sensors as well as building and grid models. In a smart city context, this platform fulfills 1) the integration of heterogeneous data sources at the building and district level, and 2) the simulation of novel energy policies at the district level aimed at the optimization of the energy usage accounting also for its impact on building comfort. The platform has been deployed in a real-world district and a novel control policy for the heating distribution network has been developed and tested. Results are presented and discussed in the paper.

New approaches and business models for the development of renewable sources are needed as an alternative to feed-in tariffs. In this work, we present a tool-chain based on a distributed infrastructure for planning renewable energy systems deployment. This solution aims at fostering new services and business models by promoting energy community actions. Such tool-chain is able to: (i) evaluate the photovoltaic potential of the rooftops of a community; (ii) perform economic assessments of distributed photovoltaic system plants considering a “community based business model”. As case study, we considered a foothill community in north-west of Italy in which the tool-chain performed economic and energetic analyses. In order to integrate the proposed business model in the Italian regulatory framework, we analysed the Italian laws for electricity distribution and operation, highlighting the limitations in integrating such community approach.

Nowadays ICT is becoming a key factor to enhance the energy optimization in our cities. At district level, real-time information can be accessed to monitor and control the energy distribution network. Moreover, the fine grain monitoring and control done at building level can provide additional information to develop more efficient control policies for energy distribution in the district. In this paper we present a distributed software infrastructure for district energy management, which aims to provide a digital archive of the city in which energetic information is available. Such information is considered as the input for a decision system, which aims to increase the energy efficiency by promoting local balancing and shaving peak loads. As case study, we integrated in our proposed cloud the heating distribution network in Turin and we present exploitable options based on real-world environmental data to increase the energy efficiency and minimize the peak request.

One of the major challenges in today's economy concerns the reduction in energy usage and CO2footprint in existing Public buildings and Spaces without significant construction works, by an intelligent ICT-based service monitoring and managing the energy consumption. In particular, interoperability between heterogeneous devices and networks, both existing and to be deployed is a key features to create efficient services and holistic energy control policies. In this paper we describe an innovative software infrastructure to provide a web-service based, hardware independent access to the heterogeneous networks of wireless sensor nodes, such as smart plugs for measuring energy motes for temperature, relative humidity and light monitoring. The proposed infrastructure allows easy extension to other networks, thus representing a contribute to the opening of a market for ICT-based customized solutions integrating numerous products from different vendors and offering services from design of integrated systems to the operation and maintenance phases.