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Real-time knowledge about the energy being exchanged sustains energy efficiency applications and services. The event-based data-saving approach to the measurements of electric energy has emerged recently with conceptual and practical implications, also thanks to the manufacturing of a new technological solution. This paper explains the fundamental underlying concepts that have led to these improvements through real-case examples. This work borrows from ontology the distinction between the concepts of endurants and perdurants, associating these concepts to the quantities involved in the energy metering process. In the new event-based computational framework, energy metering is interpreted by detecting average power and accumulated energy variations, as well as highlighting the importance of the information provided by the rate of change of energy and by the rate of events gathered from meters.

Climate change dramatically impacts the structure of energy business by adding renewables. In traditional energy networks, the balance between the demand for- and the offer of- power is managed by increasing the amount of commodity or by shedding loads. Customer-centered grid brings new paradigm about changing the economic behavior of small- and medium- scaled energy users instead of varying the amounts of commodity that is called flexibility. Flexible cooperative behavior of many actors participating in liberalized intraday market reduces pollutants because it optimizes renewable energy. However, it has implications on power markets in terms of prices and has technology implications nation-wide. This article discusses a generational change that moves decision making from few control rooms to the democratic neighborhood of the web. The objective is to present a changed socio-economic context and attract the attention of researcher to new challenges. The main result is a dynamic equilibrium that exists between the reward offered by the utilities to their customers and the advantage obtained by the utilities from such a flexible behavior. The article main conclusion is on the role of new market product called demand-side flexibility in the transformation of power markets towards ecological sustainability. Key words: Demand-side flexibility, energy market, intraday trading.

The Renewable Energy Sources (RES) have added large amounts of unpredictability in energy planning operations. The discrepancy between the forecast and the effective demand figures could led to dropouts and/or blackouts. To manage possible mismatch between demand and generation, a flexible behavior of energy users can be explored in the context of Transactive Energy Control (TEC). In the latest years, the potential of Distributed Ledger Technology (DLT) in various applications has been widely recognized by research literature. The main objective of this paper is to offer a structured exposition of different applicative scenarios in the view of using DLT in TEC context. This work explores the potential to diminish the unpredictability of RES by decentralized TEC operations in highly scaled context in which billions of small scale users could be efficiently aggregated into clusters capable to offer an equivalent of sufficient reserves.

This article presents a research work on contrasting the Manipulation of Demand figures via IoT networks (MadIoT). MadIoT is a new example of large-scale coordinated attacks on the power grid that targets high-wattage electric appliances. By altering the concentration in time of switching on-off certain number of appliances, the stability of the energy distribution network as a whole it can be compromised. An issue is the fact that no individual consumer can detect MadIoT autonomously. Current energy business scenario uses interval-energy meters capillary deployed to remote users. Current meters operate in the timescale of minutes, which makes undetectable the MadIoT attacks. The authors propose using next-generation event-based real-time demand-oriented meters in order to collect true data about the timing of switching on-off devices. The above-mentioned big data stream enables newly introduced Security Agents to perform machine-reasoning over timing patterns against an unexpected probability density of events in certain neighborhood(s).

This article discusses information protection in the context of Event-based Energy Management Systems in consumer-centered Smart Grid with renewable energy. Authors consider hybrid real time smart metering scenario that provides both the measurement data to the Advanced Metering Infrastructure plus the real-time stream of compressive sensing events directed to the Supervisory Control And Data Acquisition system. The threat is represented by an attacker that injects packets aimed to mislead the Energy Management System. Authors introduce new Security Agent that establishes interoperability between the above-mentioned subsystems aimed to mitigate said treat. The proposed method bridges the Legacy approach and the Service Orientation.

An energy distribution network is a critical infrastructure that any compromise has an enormous impact on daily lives and the economy. The objective of this work is a computerized tool for distributed monitoring, dynamic re-configuration and control of DC distribution topology. This paper describes the double bar bus DC system exploiting the event-driven, service-oriented architecture, and real-time metering with nonuniform time sampling as an example of neighborhood optimization. We build a system with the capability to assess the resilience of and to rebuild better resilient grid partitions at run-time. The result is an intelligent system distributing loads between two buses dynamically in a way to keep self-sustainable and/or non-interruptible portion running at one bus by moving few other loads to the second bus. In standalone modality, the tool assesses the survivability of microgrid with high penetration of renewable energy. Running in cooperation with grid management tools, the same software can reconfigure optimally the local topology at run-time.

The hybrid scheme of electricity metering combining time- and event-driven approaches makes visible events. It enables the detection, understanding, and management of nonstationary flows of electricity as they vary by real-life processes. We use the internal metering data to supply electric values and additional meta-information being elicited, e.g., scalar values characterizing variations of power flows and their timing. The capability to recognize events and their sequences produces new information flows in smart grids. We discuss how to exploit the process knowledge in the grid's control.

Demographic developments have challenged our research on how to assist elderly people by using robots. The KSERA (Knowledgeable SErvice Robots for Aging) project integrates smart home technology and a socially-assistive robot to extend independent living for elderly people, in particular those with COPD (Chronic Obstructive Pulmonary Disease). The social robot is the most visible component of the system playing the role of communication interface between the elderly, the smart home, and the external world. The robot’s behavior is determined in part by sensor information gathered through the smart home. To ensure user acceptance, we used user-centered design to implement the robot’s behavior. This paper describes the KSERA system, how it was developed based on user needs, treatment plans, and lab studies, and how we validated the approach through user studies and field trials. The key enabling technologies for successful socially-assistive robots include person- and self-localization abilities, person-aware navigation, speech recognition and generation, robot gestures, emulated emotions, eye contact and joint attention, and audio-video communication with family members and care givers.

The event-driven electricity metering method makes visible both processes and their components. It enables the detection and better understanding of processes that vary energy flows. The newly obtained process knowledge is shared grid-wide using communication channels. The changes in energy exchanges (processes) originate new information flows in smart grid. We use internal metering data to obtain meta-information needed to reduce said data flows. The paper explains the use of an event-driven communication method in metering.

Recent developments in smart metering applications have led to the conceptualization and construction of a new type of energy meter, operating on the basis of event-driven principles. The event-driven metering concepts are applied to represent the information on the electrical load patterns, which have an integral value. This paper explains why these concepts are different from the ones used for event-based applications in other domains, discusses the principles used in the new type of electricity meter, presents the data formats structured in such a way to provide detailed knowledge representation, and shows a number of results on real-case applications. A specific index is defined in order to represent the effectiveness of the event-driven metering scheme illustrated to represent the details of the metered pattern, comparing the results with the ones that could be reached in the most favorable case through regular timer-driven metering. The presentation of specific applications based on real-life datasets highlights the advantages of the event-driven energy metering over the traditional timer-driven metering scheme.

Event-driven metering is an emergent paradigm enabling significant data compression enhancement with respect to the conventional time domain metering techniques. This paper first discusses the representation of energy-based data in low-voltage segments of smart grids by using a process-oriented approach, providing an original interpretation in terms of accumulated energy. This interpretation is used to construct an overall framework containing multiple options for representing energy-based data, encompassing uniform and nonuniform linear time finite elements. A specific representation of the information defined in the space of digital events is then presented under the established framework. An enhanced representation of energy-based data identified as digital events is introduced, leading to the formulation of the final event-based data gathering (EBDG) scheme. Dedicated tests are carried out on exemplificative data sets for residential customers, including a new challenging benchmark developed by using a fuzzy-controlled load for air conditioning. The results obtained validate the use of the EBDG scheme to mine the energy-varying processes occurring at the remote nodes of the distribution system. Finally, the approach leading to the EBDG scheme is discussed to show how it falls within the realm of Big Data and to highlight how the EBDG results can be used to enable analytic accounting of the energy consumed in the processes occurring in a given time interval.

The event-driven energy metering paradigm has been recently introduced to make the management of the metered data more effective while reducing the overall amount of data to be transmitted or stored. This paper addresses the specific advantages of reconstructing the results of the aggregation of a number of loads in order to obtain an effective representation of the load aggregation that maintains detailed information on the individual and aggregate power. Examples are provided on real data gathered from individual and aggregate residential users.

This paper discusses the conceptual and practical implications of adopting a new type of energy meter, based on the identification of events from measuring the power pattern and on sending information on the actual energy used in the time interval between two events. The event-driven metering (EDM) approach enables gathering data useful for setting up effective representations of the knowledge that can be extracted about the prosumers' energy usage during time. Specific comparisons between the EDM effectiveness in reconstructing the power patterns with respect to the classical time-driven metering at regular time intervals are introduced to show the remarkable advantages of the EDM approach. An enhanced average power pattern reconstruction mechanism is also presented, based on the direct reproduction of the average power at the fastest time step occurring before the event generation, and on averaging the power pattern for the remaining part of the corresponding time interval. The case study application is illustrated by using the highly variable power pattern of a residential user.

Since energy use is a type of consumer behavior reflecting the interests to maximize some objective function, the human being activities seen in energy terms might be used to create the social aggregations or groups. Electric energy generated from ecologic sources brings some unpredictability. Authors model the unpredictability of the distributed generation in order to create a tool for minimization. Authors propose the novel method to build real life smart micro grids in the distributed generation context characterized by zero emissions. The proposed tool becomes an instrument to create the social aggregation of users and negotiate locally the “social” energy in real time, strengthening and mastering a virtual neighborhood of the local community.

This article discusses about the information protection aspects in Lebesgue-based smart meters that provide real-time stream of compressive sensing data directed to the Advanced Metering Infrastructure (AMI). In smart grid scenario, Supervisory Control And Data Acquisition (SCADA) systems start using real time metering information. Lebesgue-based method achieves higher data compression rates than the Shannon-based approach by encoding facts in the Mutual Information (MI) or inter-sampling. Part of the TCP/IP data packets remains unencrypted. Stream analytics can uncover knowledge about real life events and their timing by making inference on top of the inter-sampling data. The Rate Of Information (RoI) can be derived from the information left in clear form. It can disclose periods of higher vulnerability of smart grid. In turn, undesirable exploits could be set up. This article presents the ongoing work about unconventional mechanisms applicable to the protection of Mutual Dependence (MD) between pairs of random variables in real-time smart metering scenario.

This work is about the in-process visualization of multi-dimensional datasets recorded by compressed event-driven energy metering methods. The main goal of this work is to provide easy visual form to better understand energy transformations caused by real-life processes.

This article presents an industrial application in which event-based smart meters supplies datasets feeding the Advanced Metering Infrastructure, legacy billing procedures, and Supervisory Control and Data Acquisition systems of smart grid. New generation hybrid meters interoperate by using the FI-WARE architectural framework personalized by FINESCE team in the context of Cloud-based Service-Oriented Architecture made available by the Future Internet Public-Private-Partnership. Authors illustrate an opportunity to make observable each node of the Low Voltage energy distribution topology by using new event-based real time toolkit. In this industrial use case, event-based meters fed billing subsystem at regular time intervals, and supplies statefull events in real-time to the control system. This way, both event-based and timer-based datasets are conveyed to the FI-WARE cloud first, that directs them to the respective recipients by using publish-subscribe features and Big Data tools.

Event-driven energy metering has emerged recently with conceptual and practical implications, as well as with new manufactured technological solutions. This paper recalls the underlying concepts that have led to these improvements. The event-driven energy metering paradigm is addressed in terms of the definition of the major aspects leading to its interpretation, the type of information provided, and the prospects for its use. The characteristics of the approach are shown through specific application cases.

This article presents software framework that integrates Automated Meter Reading (AMR) and Advanced Metering Infrastructure (AMI) components in the context of Cloud-based Service-Oriented Architecture made available by the Future Internet Public-Private-Partnership. Authors used standard DLMS-COSEM interoperability protocol to convey metering datasets to the FI-WARE cloud. Newly developed framework supports timer-driven metering schemes from the known art. Additionally, it is fully compatible with the new coarse-grained method of analytical accounting of energy.

This article reviews different approaches to electricity metering and proposes novel coarse-grained but cycle-accurate measurement method which preserves the existing Legacy billing applications but supplies historical series of true RMS measurements. New method traces energy variations with cycle-accuracy. To reduce the quantity of data but keep high precision, it uses geometric integration. It transmits accumulated time-series upon timer-driven events. The method is designed in a way to remain compliant with the open APIs developed by Future Internet PPP projects FINESCE and FI-WARE. The proposed approach bridges the Legacy approach and the service-oriented one.

This article describes geometrical numeric integration applied to coarse-grained cycle-precise event-driven measurements of electrical energy. The proposed simplectic method draws from Stormer-Verlet and leapfrog algorithms in order to provide operational intelligence tools to analyze the amounts of analytical energy data. The proposed tool is extremely quick. Being very simple in computational terms, it suites real-time usages. This work extends the event-driven measurement approaches.

Modern energy grid monitoring tools use data from different nodes on a daily basis and under system contingencies. The asynchronous data retrieval modality does not provide accurate and timely difference information from pairs of nodes on the network. The synchronous data retrieval at very high data rates is not yet achievable in smart grid. The simultaneity is not an absolute property between events, but depending on the frame of reference. What is simultaneous in the physical media of smart grid (one frame of reference) will not necessarily be simultaneous in the cyber component (another frame of reference). This paper analyzes the role of true measurements in space-time. It proposes a special modality to cope with the off-nominal frequencies issue.

SummaryIntroduction: This article is part of the Focus Theme of Methods of Information in Medicine on “New Methodologies for Patients Rehabilitation”.Background: The article presents the approach in which the rehabilitative exercise prepared by healthcare professional is encoded as formal knowledge and used by humanoid robot to assist patients without involving other care actors.Objectives: The main objective is the use of humanoids in rehabilitative care. An example is pulmonary rehabilitation in COPD patients. Another goal is the automated judgment functionality to determine how the rehabilitation exercise matches the pre-programmed correct sequence.Methods: We use the Aldebaran Robotics’ NAO humanoid to set up artificial cognitive application. Pre-programmed NAO induces elderly patient to undertake humanoid-driven rehabilitation exercise, but needs to evaluate the human actions against the correct template. Patient is observed using NAO’s eyes. We use the Microsoft Kinect SDK to extract motion path from the humanoid’s recorded video. We compare human- and humanoid-operated process sequences by using the Dynamic Time Warping (DTW) and test the prototype.Results: This artificial cognitive software showcases the use of DTW algorithm to enable humanoids to judge in near real-time about the correctness of rehabilitative exercises performed by patients following the robot’s indications.Conclusion: One could enable better sustainable rehabilitative care services in remote residential settings by combining intelligent applications piloting humanoids with the DTW pattern matching algorithm applied at run time to compare humanoid- and human-operated process sequences. In turn, it will lower the need of human care.

This article describes an approach in which the rehabilitative exercise prepared by health care professional (human) is encoded as formal knowledge and used by humanoid robot to assist patients in residential settings without involving other care actors. The authors are researching on the new cognitive capability enabling robots to judge about the correctness of the rehabilitative exercise performed by patients following the robot's indications. The proposed method uses the Dynamic Time Warping functionality comparing the correct sequence (encoded in the Knowledge Base) with the human actions being observed by the robot's eyes. The proposed approach is an enabler of better sustainable rehabilitative care services in remote residential settings because of lowering the need of human care.

Electric CAR Recharge Optimization Toolkit is an integrated system for modeling and optimization of Electric Vehicle recharge processes. It uses semantic technology to conceptualize, model, and optimize battery recharge processes occurring in smart grid. The new tool attempts the time shifting postponing the demand exceeding the available energy flows. It is useful for operational planning of saturated electricity grid when the electricity demand exceeds the available resources during some time slots.

In this paper, we propose a new efficient technique for forecasting the bandwidth of digital access links to the Internet. This technique can be used for calculating the throughput of access links in implementing electronic document management systems and in organizing information portals. The main idea is to estimate the bandwidth of digital access links to the Internet using the statistics of the service load. The main result is the derivation of analytical expressions to estimate the required bandwidth.

Event-driven energy metering brings the possibility to detect and understand the non-unique electrical events. Events, preceding the use of the vehicle, give the anticipatory knowledge relevant to the electrical grid balancing and energy optimization. Author discusses about the anticipatory knowledge exchanged between the stakeholders improving the electric vehicle's integration into smart grid.

Nursing science deals with human-to-human interaction delivering care service, showing several peculiarities compared with other life science domains. Semantic technologies can be applied to clinical nursing, where the risk management forms a particular application class. Bone marrow transplantation is a specific sub-domain in which nursing process lacks the quality of service’s predictability at run time, requiring error detection before their happening and semantic technologies complementing best nursing strategies. The intelligent mix of technologies delivering proactive feedback to human actors in a natural way is a challenge. We investigate on possible risk control measures in the above-said nursing, suggesting a combination of ICT and knowledge technologies.

This work suggests an effective approach for information management in smart power grids based on the introduction of a suitable theory of digital energy. It shows a possible way to effectively manage energy dynamics in real life systems in real time. Power grids hold real time information flows already, but the control systems currently adopted use other information sources. We discuss the use of the information and semantic technologies in order to balance the loads in storage-less electric energy domain, and the changes brought by Future Internet and its entities.

Individual travelling is the most energy consuming day-by-day activity. Since fuel use is a type of consumer behaviour reflecting the interests to maximize some objective function, the human being activities seen in energy terms might be used to create the social aggregations or groups. Energy minimization in mobility conflicts with the objective function being maximized, however the virtual social networking by service-oriented architectures might improve the ecologic latter optimising the overall resources used by the community. Authors propose a method to build real life communities of connected travellers with the context awareness permitting to achieve some cooperative behaviour among the above-said virtual community networked.

A critical success factor in Insurance business is its ability to use information sources and contained knowledge in the most effective way. Its profitability is obtained through the Technical management plus Financial management of the funds gathered on the market. The profitability of a given customer can be evaluated through its Life Time Value (LTV). We aim at applying evolutionary algorithms to the problem of forecasting the future LTV in the Insurance Business. The Framework developed within the Eureka cofunded research projects HPPC/SEA and IKF has been adapted to the Insurance Domain through a dedicated Genetic Engine. The solution uses RDF and XMLcompliant standard. The idea of using evolutionary algorithms to design fuzzy systems date from the beginning of the Nineties and a fair body of work has been carried out throughout the past decade. The approach we followed uses an evolutionary algorithm to evolve fuzzy classifiers of the data set.

The Event-Driven Metering (EDM) method for electricity enabled the detection and better understanding of variable energy-transforming processes from real life. This article generalizes the above-mentioned Lebesgue-based approach by translating it to the accounting of other energy vectors in the form of gases or liquids. Given the behavioral difference between the physical forms of vectors of energy, authors made an effort to keep newly proposed method compatible with the process knowledge being extracted and shared grid-wide by the EDM method previously declined to electricity. Authors exploit the symplectic properties deriving from the energy conservation law. Authors set up new compressive and sparse sensing method by moving from the point-like data to the segment-like entities remaining valid over longer finite time intervals.