Behavioural patterns in aggregated demand response developments for communities targeting renewables

Encouraging consumers to embrace renewable energies and energy-efficient technologies is at stake, and so the energy players such as utilities and policy-makers are opening up a range of new value propositions towards more sustainable communities. For instance, developments of turn-key demand response aggregation and optimisation of distributed loads are rapidly emerging across the globe in a variety of business models focused on maximising the inherent flexibility and diversity of the behind-the-meter assets. However, even though these developments" added value is understood and of wide interest, measurement of the desired levels of consumer engagement is still on demonstration stages and assessment of technology readiness. In this paper, we analyse the characteristics of the loads, the behaviour of parameters, and in a final extent, the behaviour of each kind of consumer participating in aggregated demand scheduling. We apply both non-automatic and machine learning methods to extract the relevant factors and to recognise the potential consumer behaviour on a series of scenarios that are drawn using both synthetic data and living labs datasets. Our experimentation showcases a number of three patterns in which factors like the community"s demand volume and the consumer"s flexibility dominate and impact the performance of the tested development. The experimentation also makes current limitations arise within the existing electricity consumption datasets and their potential for inference and forecasting demand flexibility analytics.Comunidad de Madri

Real-time optimization of working memory in autonomous reasoning for high-level control of cognitive robots deployed in dynamic environments

High-level, real-time mission control of autonomous and semi-autonomous robots, deployed in remote and dynamic environments, remains a research challenge. Robots operating in these environments require some cognitive ability, provided by a simple, but robust, cognitive architecture. The most important process in a cognitive architecture is the working memory, with core functions being memory representation, memory recall, action selection and action execution, performed by the central executive. The cognitive reasoning process uses a memory representation, based on state flows, governed by state transitions with simple, quantified propositional transition formulae. In this thesis, real-time working memory quantification and optimization is performed using a novel adaptive entropy-based fitness quantification (AEFQ) algorithm and particle swarm optimization (PSO), respectively. A cognitive architecture, using an improved set-based PSO is developed for real-time, high-level control of single-task robots and a novel coalitional games-theoretic PSO (CG-PSO) algorithm extends the cognitive architecture for real-time, high-level control in multi-task robots. The performance of the cognitive architecture is evaluated by simulation, where a UAV executesfour use cases: Firstly, for real-time high-level, single-task control: 1) relocating the UAV to a charging station and 2) collecting and delivering medical equipment. Performance is measured by inspecting the success and completeness of the mission and the accuracy of autonomous flight control. Secondly, for real-time high-level control of multi-task autonomous vehicle control: 3) delivering medical equipment to an incident and 4) provide aerial security surveillance support. The performance of the architecture is measured in terms of completeness and cognitive processing time and cue processing time. The results show that coalitions correctly represent optimal memory and action selection in real-time, while overall processing time is within a feasible time limit, arbitrarily set to 2 seconds in this study

Artificial Intelligence and Machine Learning Approaches to Energy Demand-Side Response: A Systematic Review

Recent years have seen an increasing interest in Demand Response (DR) as a means to provide flexibility, and hence improve the reliability of energy systems in a cost-effective way. Yet, the high complexity of the tasks associated with DR, combined with their use of large-scale data and the frequent need for near real-time de-cisions, means that Artificial Intelligence (AI) and Machine Learning (ML) — a branch of AI — have recently emerged as key technologies for enabling demand-side response. AI methods can be used to tackle various challenges, ranging from selecting the optimal set of consumers to respond, learning their attributes and pref-erences, dynamic pricing, scheduling and control of devices, learning how to incentivise participants in the DR schemes and how to reward them in a fair and economically efficient way. This work provides an overview of AI methods utilised for DR applications, based on a systematic review of over 160 papers, 40 companies and commercial initiatives, and 21 large-scale projects. The papers are classified with regards to both the AI/ML algorithm(s) used and the application area in energy DR. Next, commercial initiatives are presented (including both start-ups and established companies) and large-scale innovation projects, where AI methods have been used for energy DR. The paper concludes with a discussion of advantages and potential limitations of reviewed AI techniques for different DR tasks, and outlines directions for future research in this fast-growing area

Dynamic Core Community Detection and Information Diffusion Processes on Networks

Interest in network science has been increasingly shared among various research communities due to its broad range of applications. Many real world systems can be abstracted as networks, a group of nodes connected by pairwise edges, and examples include friendship networks, metabolic networks, and world wide web among others. Two of the main research areas in network science that have received a lot of focus are community detection and information diffusion. As for community detection, many well developed algorithms are available for such purposes in static networks, for example, spectral partitioning and modularity function based optimization algorithms. As real world data becomes richer, community detection in temporal networks becomes more and more desirable and algorithms such as tensor decomposition and generalized modularity function optimization are developed. One scenario not well investigated is when the core community structure persists over long periods of time with possible noisy perturbations and changes only over periods of small time intervals. The contribution of this thesis in this area is to propose a new algorithm based on low rank component recovery of adjacency matrices so as to identify the phase transition time points and improve the accuracy of core community structure recovery. As for information diffusion, traditionally it was studied using either threshold models or independent interaction models as an epidemic process. But information diffusion mechanism is different from epidemic process such as disease transmission because of the reluctance to tell stale news and to address this issue other models such as DK model was proposed taking into consideration of the reluctance of spreaders to diffuse the information as time goes by. However, this does not capture some cases such as the losing interest of information receivers as in viral marketing. The contribution of this thesis in this area is we proposed two new models coined susceptible-informed-immunized (SIM) model and exponentially time decaying susceptible-informed (SIT) model to successfully capture the intrinsic time value of information from both the spreader and receiver points of view. Rigorous analysis of the dynamics of the two models were performed based mainly on mean field theory. The third contribution of this thesis is on the information diffusion optimization. Controlling information diffusion has been widely studied because of its important applications in areas such as social census, disease control and marketing. Traditionally the problem is formulated as identifying the set of k seed nodes, informed initially, so as to maximize the diffusion size. Heuristic algorithms have been developed to find approximate solutions for this NP-hard problem, and measures such as k-shell, node degree and centrality have been used to facilitate the searching for optimal solutions. The contribution of this thesis in this field is to design a more realistic objective function and apply binary particle swarm optimization algorithm for this combinatorial optimization problem. Instead of fixating the seed nodes size and maximize the diffusion size, we maximize the profit defined as the revenue, which is simply the diffusion size, minus the cost of setting those seed nodes, which is designed as a function of degrees of the seed nodes or a measure that is similar to the centrality of nodes. Because of the powerful algorithm, we were able to study complex scenarios such as information diffusion optimization on multilayer networks.PHDPhysicsUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/145937/1/wbao_1.pd

Living analytics methods for the social web

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RESOURCE ALLOCATION FOR WIRELESS RELAY NETWORKS

In this thesis, we propose several resource allocation strategies for relay networks in the context of joint power and bandwidth allocation and relay selection, and joint power allocation and subchannel assignment for orthogonal frequency division multiplexing (OFDM) and orthogonal frequency division multiple access (OFDMA) systems. Sharing the two best ordered relays with equal power between the two users over Rayleigh flat fading channels is proposed to establish full diversity order for both users. Closed form expressions for the outage probability, and bit error probability (BEP) performance measures for both amplify and forward (AF) and decode and forward (DF) cooperative communication schemes are developed for different scenarios. To utilize the full potentials of relay-assisted transmission in multi user systems, we propose a mixed strategy of AF relaying and direct transmission, where the user transmits part of the data using the relay, and the other part is transmitted using the direct link. The resource allocation problem is formulated to maximize the sum rate. A recursive algorithm alternating between power allocation and bandwidth allocation steps is proposed to solve the formulated resource allocation problem. Due to the conflict between limited wireless resources and the fast growing wireless demands, Stackelberg game is proposed to allocate the relay resources (power and bandwidth) between competing users, aiming to maximize the relay benefits from selling its resources. We prove the uniqueness of Stackelberg Nash Equilibrium (SNE) for the proposed game. We develop a distributed algorithm to reach SNE, and investigate the conditions for the stability of the proposed algorithm. We propose low complexity algorithms for AF-OFDMA and DF-OFDMA systems to assign the subcarriers to the users based on high SNR approximation aiming to maximize the weighted sum rate. Auction framework is proposed to devise competition based solutions for the resource allocation of AF-OFDMA aiming tomaximize either vi the sum rate or the fairness index. Two auction algorithms are proposed; sequential and one-shot auctions. In sequential auction, the users evaluate the subcarrier based on the rate marginal contribution. In the one-shot auction, the users evaluate the subcarriers based on an estimate of the Shapley value and bids on all subcarriers at once

A Survey on Reservoir Computing and its Interdisciplinary Applications Beyond Traditional Machine Learning

Reservoir computing (RC), first applied to temporal signal processing, is a recurrent neural network in which neurons are randomly connected. Once initialized, the connection strengths remain unchanged. Such a simple structure turns RC into a non-linear dynamical system that maps low-dimensional inputs into a high-dimensional space. The model's rich dynamics, linear separability, and memory capacity then enable a simple linear readout to generate adequate responses for various applications. RC spans areas far beyond machine learning, since it has been shown that the complex dynamics can be realized in various physical hardware implementations and biological devices. This yields greater flexibility and shorter computation time. Moreover, the neuronal responses triggered by the model's dynamics shed light on understanding brain mechanisms that also exploit similar dynamical processes. While the literature on RC is vast and fragmented, here we conduct a unified review of RC's recent developments from machine learning to physics, biology, and neuroscience. We first review the early RC models, and then survey the state-of-the-art models and their applications. We further introduce studies on modeling the brain's mechanisms by RC. Finally, we offer new perspectives on RC development, including reservoir design, coding frameworks unification, physical RC implementations, and interaction between RC, cognitive neuroscience and evolution.Comment: 51 pages, 19 figures, IEEE Acces

Planning and Scheduling Optimization

Although planning and scheduling optimization have been explored in the literature for many years now, it still remains a hot topic in the current scientific research. The changing market trends, globalization, technical and technological progress, and sustainability considerations make it necessary to deal with new optimization challenges in modern manufacturing, engineering, and healthcare systems. This book provides an overview of the recent advances in different areas connected with operations research models and other applications of intelligent computing techniques used for planning and scheduling optimization. The wide range of theoretical and practical research findings reported in this book confirms that the planning and scheduling problem is a complex issue that is present in different industrial sectors and organizations and opens promising and dynamic perspectives of research and development

MODELLING & SIMULATION HYBRID WARFARE Researches, Models and Tools for Hybrid Warfare and Population Simulation

The Hybrid Warfare phenomena, which is the subject of the current research, has been framed by the work of Professor Agostino Bruzzone (University of Genoa) and Professor Erdal Cayirci (University of Stavanger), that in June 2016 created in order to inquiry the subject a dedicated Exploratory Team, which was endorsed by NATO Modelling & Simulation Group (a panel of the NATO Science & Technology organization) and established with the participation as well of the author. The author brought his personal contribution within the ET43 by introducing meaningful insights coming from the lecture of \u201cFight by the minutes: Time and the Art of War (1994)\u201d, written by Lieutenant Colonel US Army (Rtd.) Robert Leonhard; in such work, Leonhard extensively developed the concept that \u201cTime\u201d, rather than geometry of the battlefield and/or firepower, is the critical factor to tackle in military operations and by extension in Hybrid Warfare. The critical reflection about the time - both in its quantitative and qualitative dimension - in a hybrid confrontation it is addressed and studied inside SIMCJOH, a software built around challenges that imposes literally to \u201cFight by the minutes\u201d, echoing the core concept expressed in the eponymous work. Hybrid Warfare \u2013 which, by definition and purpose, aims to keep the military commitment of both aggressor and defender at the lowest - can gain enormous profit by employing a wide variety of non-military tools, turning them into a weapon, as in the case of the phenomena of \u201cweaponization of mass migrations\u201d, as it is examined in the \u201cDies Irae\u201d simulation architecture. Currently, since migration it is a very sensitive and divisive issue among the public opinions of many European countries, cynically leveraging on a humanitarian emergency caused by an exogenous, inducted migration, could result in a high level of political and social destabilization, which indeed favours the concurrent actions carried on by other hybrid tools. Other kind of disruption however, are already available in the arsenal of Hybrid Warfare, such cyber threats, information campaigns lead by troll factories for the diffusion of fake/altered news, etc. From this perspective the author examines how the TREX (Threat network simulation for REactive eXperience) simulator is able to offer insights about a hybrid scenario characterized by an intense level of social disruption, brought by cyber-attacks and systemic faking of news. Furthermore, the rising discipline of \u201cStrategic Engineering\u201d, as envisaged by Professor Agostino Bruzzone, when matched with the operational requirements to fulfil in order to counter Hybrid Threats, it brings another innovative, as much as powerful tool, into the professional luggage of the military and the civilian employed in Defence and Homeland security sectors. Hybrid is not the New War. What is new is brought by globalization paired with the transition to the information age and rising geopolitical tensions, which have put new emphasis on hybrid hostilities that manifest themselves in a contemporary way. Hybrid Warfare is a deliberate choice of an aggressor. While militarily weak nations can resort to it in order to re-balance the odds, instead military strong nations appreciate its inherent effectiveness coupled with the denial of direct responsibility, thus circumventing the rules of the International Community (IC). In order to be successful, Hybrid Warfare should consist of a highly coordinated, sapient mix of diverse and dynamic combination of regular forces, irregular forces (even criminal elements), cyber disruption etc. all in order to achieve effects across the entire DIMEFIL/PMESII_PT spectrum. However, the owner of the strategy, i.e. the aggressor, by keeping the threshold of impunity as high as possible and decreasing the willingness of the defender, can maintain his Hybrid Warfare at a diplomatically feasible level; so the model of the capacity, willingness and threshold, as proposed by Cayirci, Bruzzone and Gunneriusson (2016), remains critical to comprehend Hybrid Warfare. Its dynamicity is able to capture the evanescent, blurring line between Hybrid Warfare and Conventional Warfare. In such contest time is the critical factor: this because it is hard to foreseen for the aggressor how long he can keep up with such strategy without risking either the retaliation from the International Community or the depletion of resources across its own DIMEFIL/PMESII_PT spectrum. Similar discourse affects the defender: if he isn\u2019t able to cope with Hybrid Threats (i.e. taking no action), time works against him; if he is, he can start to develop counter narrative and address physical countermeasures. However, this can lead, in the medium long period, to an unforeseen (both for the attacker and the defender) escalation into a large, conventional, armed conflict. The performance of operations that required more than kinetic effects drove the development of DIMEFIL/PMESII_PT models and in turn this drive the development of Human Social Culture Behavior Modelling (HCSB), which should stand at the core of the Hybrid Warfare modelling and simulation efforts. Multi Layers models are fundamental to evaluate Strategies and Support Decisions: currently there are favourable conditions to implement models of Hybrid Warfare, such as Dies Irae, SIMCJOH and TREX, in order to further develop tools and war-games for studying new tactics, execute collective training and to support decisions making and analysis planning. The proposed approach is based on the idea to create a mosaic made by HLA interoperable simulators able to be combined as tiles to cover an extensive part of the Hybrid Warfare, giving the users an interactive and intuitive environment based on the \u201cModelling interoperable Simulation and Serious Game\u201d (MS2G) approach. From this point of view, the impressive capabilities achieved by IA-CGF in human behavior modeling to support population simulation as well as their native HLA structure, suggests to adopt them as core engine in this application field. However, it necessary to highlight that, when modelling DIMEFIL/PMESII_PT domains, the researcher has to be aware of the bias introduced by the fact that especially Political and Social \u201cscience\u201d are accompanied and built around value judgement. From this perspective, the models proposed by Cayirci, Bruzzone, Guinnarson (2016) and by Balaban & Mileniczek (2018) are indeed a courageous tentative to import, into the domain of particularly poorly understood phenomena (social, politics, and to a lesser degree economics - Hartley, 2016), the mathematical and statistical instruments and the methodologies employed by the pure, hard sciences. Nevertheless, just using the instruments and the methodology of the hard sciences it is not enough to obtain the objectivity, and is such aspect the representations of Hybrid Warfare mechanics could meet their limit: this is posed by the fact that they use, as input for the equations that represents Hybrid Warfare, not physical data observed during a scientific experiment, but rather observation of the reality that assumes implicitly and explicitly a value judgment, which could lead to a biased output. Such value judgement it is subjective, and not objective like the mathematical and physical sciences; when this is not well understood and managed by the academic and the researcher, it can introduce distortions - which are unacceptable for the purpose of the Science - which could be used as well to enforce a narrative mainstream that contains a so called \u201ctruth\u201d, which lies inside the boundary of politics rather than Science. Those observations around subjectivity of social sciences vs objectivity of pure sciences, being nothing new, suggest however the need to examine the problem under a new perspective, less philosophical and more leaned toward the practical application. The suggestion that the author want make here is that the Verification and Validation process, in particular the methodology used by Professor Bruzzone in doing V&V for SIMCJOH (2016) and the one described in the Modelling & Simulation User Risk Methodology (MURM) developed by Pandolfini, Youngblood et all (2018), could be applied to evaluate if there is a bias and the extent of the it, or at least making clear the value judgment adopted in developing the DIMEFIL/PMESII_PT models. Such V&V research is however outside the scope of the present work, even though it is an offspring of it, and for such reason the author would like to make further inquiries on this particular subject in the future. Then, the theoretical discourse around Hybrid Warfare has been completed addressing the need to establish a new discipline, Strategic Engineering, very much necessary because of the current a political and economic environment which allocates diminishing resources to Defense and Homeland Security (at least in Europe). However, Strategic Engineering can successfully address its challenges when coupled with the understanding and the management of the fourth dimension of military and hybrid operations, Time. For the reasons above, and as elaborated by Leonhard and extensively discussed in the present work, addressing the concern posed by Time dimension is necessary for the success of any military or Hybrid confrontation. The SIMCJOH project, examined under the above perspective, proved that the simulator has the ability to address the fourth dimension of military and non-military confrontation. In operations, Time is the most critical factor during execution, and this was successfully transferred inside the simulator; as such, SIMCJOH can be viewed as a training tool and as well a dynamic generator of events for the MEL/MIL execution during any exercise. In conclusion, SIMCJOH Project successfully faces new challenging aspects, allowed to study and develop new simulation models in order to support decision makers, Commanders and their Staff. Finally, the question posed by Leonhard in terms of recognition of the importance of time management of military operations - nowadays Hybrid Conflict - has not been answered yet; however, the author believes that Modelling and Simulation tools and techniques can represent the safe \u201ctank\u201d where innovative and advanced scientific solutions can be tested, exploiting the advantage of doing it in a synthetic environment