Francisco Prieto-Castrillo

Mobile Communication Signatures of Unemployment

The mapping of populations socio-economic well-being is highly constrained by the logistics of censuses and surveys. Consequently, spatially detailed changes across scales of days, weeks, or months, or even year to year, are difficult to assess; thus the speed of which policies can be designed and evaluated is limited. However, recent studies have shown the value of mobile phone data as an enabling methodology for demographic modeling and measurement. In this work, we investigate whether indicators extracted from mobile phone usage can reveal information about the socio-economical status of microregions such as districts (i.e., average spatial resolution \({<}2.7\) km). For this we examine anonymized mobile phone metadata combined with beneficiaries records from unemployment benefit program. We find that aggregated activity, social, and mobility patterns strongly correlate with unemployment. Furthermore, we construct a simple model to produce accurate reconstruction of district level unemployment from their mobile communication patterns alone. Our results suggest that reliable and cost-effective economical indicators could be built based on passively collected and anonymized mobile phone data. With similar data being collected every day by telecommunication services across the world, survey-based methods of measuring community socioeconomic status could potentially be augmented or replaced by such passive sensing methods in the future.

Organization-based Multi-Agent structure of the Smart Home Electricity System

This paper proposes a Building Energy Management System (BEMS) as part of an organization-based Multi-Agent system that models the Smart Home Electricity System (MASHES). The proposed BEMS consists of an Energy Management System (EMS) and a Prediction Engine (PE). The considered Smart Home Electricity System (SHES) consists of different agents, each with different tasks in the system. In this context, smart homes are able to connect to the power grid to sell/buy electrical energy to/from the Local Electricity Market (LEM), and manage electrical energy inside of the smart home. Moreover, a Modified Stochastic Predicted Bands (MSPB) interval optimization method is used to model the uncertainty in the Building Energy Management (BEM) problem. A demand response program (DRP) based on time of use (TOU) rate is also used. The performance of the proposed BEMS is evaluated using a JADE implementation of the proposed organization-based MASHES.

Organization-Based Multi-agent System of Local Electricity Market: Bottom-Up Approach

This work proposes a organization-based Multi-Agent System that models Local Electricity Market (MASLEM). A bottom-up approach is implemented to manage energy in this work. In this context, agents are able to connect to each other and the power grid to transact electrical energy, and manage their inside electrical energy independently. A Demand Response Program (DRP) based on Indirect Load Control (ILC) method is also used. The performance of our work is evaluated through an Agent Based Modeling (ABM) implementation.

A Review of Multi-agent Based Energy Management Systems

This paper proposes a review of Energy Management Systems (EMSs) based on Multi-Agent Systems (MASs). Also, goal, scale, strategy and software are discussed as different characteristics of the EMSs. Then, multi agent-based structure of the EMSs is described. Finally, challenges and future discussions related to the EMSs are presented in this paper.

Causal Scale of Rotors in a Cardiac System

Rotors of spiral waves are thought to be one of the potential mechanisms that maintain atrial fibrillation (AF). However, disappointing clinical outcomes of rotor mapping and ablation to eliminate AF raise a serious doubt on rotors as a macro-scale mechanism that causes the micro-scale behavior of individual cardiomyocytes to maintain spiral waves. In this study, we aimed to elucidate the causal relationship between rotors and spiral waves in a numerical model of cardiac excitation. To accomplish the aim, we described the system in a series of spatiotemporal scales by generating a renormalization group, and evaluated the causal architecture of the system by quantifying causal emergence. Causal emergence is an information-theoretic metric that quantifies emergence or reduction between micro- and macro-scale behaviors of a system by evaluating effective information at each scale. We found that the cardiac system with rotors has a spatiotemporal scale at which effective information peaks. A positive correlation between the number of rotors and causal emergence was observed only up to the scale of peak causation. We conclude that rotors are not the universal mechanism to maintain spiral waves at all spatiotemporal scales. This finding may account for the conflicting benefit of rotor ablation in clinical studies.

An Ising Spin-Based Model to Explore Efficient Flexibility in Distributed Power Systems

This paper analyses customers’ demand flexibility in a local power trading scenario through an Ising spin-based model. We look at quantitative information on the two-way relationships between power exchanges and spin dynamics. To this end, a modified version of the Metropolis-Hastings algorithm was implemented, including a gradient descent through the constraint surface. This allowed us to analyse the system on a large scale (considering the cumulated benefit of all the actors involved) and also from the perspective of total aggregation. In a maximum flexibility scenario, the total aggregation profit increases with the number of aggregators. We also investigate numerically the effect of aggregator boundaries on the spin dynamics.

An Evaluation of a Metaheuristic Artificial Immune System for Household Energy Optimization

Devices in a smart home should be connected in an optimal way; this helps save energy and money. Among numerous optimization models that can be found in the literature, we would like to highlight artificial immune systems, which use special bioinspired algorithms to solve optimization problems effectively. The aim of this work is to present the application of an artificial immune system in the context of different energy optimization problems. Likewise, a case study is performed in which an artificial immune system is incorporated in order to solve an energy management problem in a domestic environment. A thorough analysis of the different strategies is carried out to demonstrate the ability of an artificial immune system to find a successful optima which satisfies the problem constraints.

Economic Evaluation of Predictive Dispatch Model in MAS-Based Smart Home

This paper proposes a Predictive Dispatch System (PDS) as part of a Multi-Agent system that models the Smart Home Electricity System (MASHES). The proposed PDS consists of a Decision-Making System (DMS) and a Prediction Engine (PE). The considered Smart Home Electricity System (SHES) consists of different agents, each with different tasks in the system. A Modified Stochastic Predicted Bands (MSPB) interval optimization method is used to model the uncertainty in the Home Energy Management (HEM) problem. Moreover, the proposed method to solve HEM problem is based on the Moving Window Algorithm (MWA). The performance of the proposed Home Energy Management System (HEMS) is evaluated using a JADE implementation of the MASHES.

Electric Vehicle Urban Exploration by Anti-pheromone Swarm Based Algorithms

In this work we show how a simple anti-pheromone ant foraging based algorithm can be effective in urban navigation by reducing exploration times. We use a distributed multi agent architecture to test this algorithm. Swarm collaboration is analysed for different scenarios with varying number of units and map complexity. We show how an increase in the number of robots results in smaller exploration times. Also, we measure how the complexity of the map topology affects the navigability. We validate our approach through numerical tests with both synthetic random generated maps and real bicycle routes in four cities. Also, by monitoring the dynamics of three real prototypes built at the laboratory, we check both the feasibility of our approach and the robustness of the algorithm.

Distributed Sequential Consensus in Networks: Analysis of Partially Connected Blockchains with Uncertainty

This work presents a theoretical and numerical analysis of the conditions under which distributed sequential consensus is possible when the state of a portion of nodes in a network is perturbed. Specifically, it examines the consensus level of partially connected blockchains under failure/attack events. To this end, we developed stochastic models for both verification probability once an error is detected and network breakdown when consensus is not possible. Through a mean field approximation for network degree we derive analytical solutions for the average network consensus in the large graph size thermodynamic limit. The resulting expressions allow us to derive connectivity thresholds above which networks can tolerate an attack.