Oihane Kamara-Esteban

Identification of influencing municipal characteristics regarding household waste generation and their forecasting ability in Biscay.

The planning of waste management strategies needs tools to support decisions at all stages of the process. Accurate quantification of the waste to be generated is essential for both the daily management (short-term) and proper design of facilities (long-term). Designing without rigorous knowledge may have serious economic and environmental consequences. The present works aims at identifying relevant socio-economic features of municipalities regarding Household Waste (HW) generation by means of factor models. Factor models face two main drawbacks, data collection and identifying relevant explanatory variables within a heterogeneous group. Grouping similar characteristics observations within a group may favour the deduction of more robust models. The methodology followed has been tested with Biscay Province because it stands out for having very different municipalities ranging from very rural to urban ones. Two main models are developed, one for the overall province and a second one after clustering the municipalities. The results prove that relating municipalities with specific characteristics, improves the results in a very heterogeneous situation. The methodology has identified urban morphology, tourism activity, level of education and economic situation as the most influencing characteristics in HW generation.

Regression Based Emission Models for Vehicle Contribution to Climate Change

The reduction of carbon emissions within the transportation sector is one of the most important steps against the threat of global warming. Unless strict emissions-reduction and fuel economy policies are in place, the resulting pollution is expected to increase dramatically along with the amount of vehicles on the roads. An accurate quantification of the emissions produced by each type of vehicle is essential in order to evaluate the social and environmental impacts derived. The literature shows a wide range of pollutant emission models, whether empirical, database centric or regression based. In this paper, we propose and analyze 3 regression based models built on data from pollutant emission databases and knowledge models. The first model is based on an exponential regression that improves the results given in the state of the art. In contrast, the other two models are based on different Artificial Intelligence techniques, namely Artificial Neural Networks and Support Vector Regression, which further improve the results.

Bridging the Gap between Real and Simulated Environments: A Hybrid Agent-Based Smart Home Simulator Architecture for Complex Systems

Deployment, maintenance of Smart Homes, Smart Grids in real environments is an expensive, lengthy process. In this paradigm, simulations play an important role by providing means of emulating the behaviour of the aforementioned systems. However, these simulations may suffer from lack of accuracy due to the inability to properly reproduce the operation of complex technologies such as solar panels, Heating, Ventilating, Air Conditioning systems (HVAC), sewerage networks or water provisioning. Within this context, this paper presents a Smart-Home Simulation architecture that is able to carry out more representational simulations by merging agent-based simulation of human behaviour with real-world modelling capabilities such as those provided by the Simulink software. Based on the simulation of human behaviour, water, electricity consumption profiles are generated, sent to Simulink models using the TCP/IP communication protocol. The obtained results show that a synchronized connection of both platforms in feasible, enabling a more accurate representation of the systems involved.

On-demand energy monitoring and response architecture in a ubiquitous world

Energy demand is increasing globally, and in consequence greenhouse-gas emissions from this sector are on the rise as well. This trend is set to continue, driven primarily by the economic growth and the rising population. Solutions in this area go hand in hand with the worldwide deployment of policies that look forward a better management and usage of energy in both domestic and industrial scopes. In this line, load balancing through demand-response strategies comes out as one of the most effective and immediate actions aimed at achieving efficiency in the use of energy resources. We present GeoWorldSim, an agent-based simulation platform that integrates the development of a human activity model as well as the communication middleware known as FI-WARE in order to test the best communication architectures available for the implementation of demand-response strategies.

MASSHA: An agent-based approach for human activity simulation in intelligent environments

Abstract Human activity recognition has the potential to become a real enabler for ambient assisted living technologies. Research on this area demands the execution of complex experiments involving humans interacting with intelligent environments in order to generate meaningful datasets, both for development and validation. Running such experiments is generally expensive and troublesome, slowing down the research process. This paper presents an agent-based simulator for emulating human activities within intelligent environments: MASSHA. Specifically, MASSHA models the behaviour of the occupants of a sensorised environment from a single-user and multiple-user point of view. The accuracy of MASSHA is tested through a sound validation methodology, providing examples of application with three real human activity datasets and comparing these to the activity datasets produced by the simulator. Results show that MASSHA can reproduce behaviour patterns that are similar to those registered in the real datasets, achieving an overall accuracy of 93.52% and 88.10% in frequency and 98.27% and 99.09% in duration for the single-user scenario datasets; and a 99.3% and 88.25% in terms of frequency and duration for the multiple-user scenario.

GTPlat: Geosimulation for Assessing the Application of Incentives to Transport Planning

Motor vehicle abuse entails emitting large amounts of greenhouse gases to the atmosphere. In order to reduce climate change and life expectancy loss, authorities want to launch a set of sustainable travel policies which should be evaluated before their deployment. Although multi-agent systems for traffic analyses are very popular, they mainly focus on faithfully reproducing vehicle displacement and interaction between vehicles. It is therefore necessary to go one step further and integrate the transport choice factors that take place before starting everyday journeys. We present the baseline Geosimulation that integrates all the steps of citizens home-to-work commutes for assessing the impact green travelling policies would have.

Bio-inspired Approximation to MPPT Under Real Irradiation Conditions.

The aim of this paper is to study the possibilities of increasing the renewable power of a photovoltaic system through a barely tested bio-inspired algorithm. Photovoltaic energy has a high potential to grows but it has a strong dependence on climate conditions. Particularly, the power production of panels is reduced under partial shading conditions, which is a very common situation in several cities around the world. Therefore, the Maximum Power Point Tracker (MPPT) algorithm becomes critical to control the photovoltaic system. In this paper, we propose a novel MPPT algorithm based on the Artificial Bee Colony (ABC) bio-inspired method and we explicitly define a fitness function based on power production. The ABC algorithm is more attractive than any other bio-inspired methods due to its simplicity and ability to resolve the problem of choosing an ideal duty cycle. Specifically, it requires a reduced number of control parameters and the initial conditions have no influence over the convergence. The analysis has been carried out using real meteorological and consumption data and testing the behavior of the algorithm on a standalone photovoltaic system operating only with direct current.

GreenSoul: An IoT Platform for Empowering Users’ Energy Efficiency in Public Buildings

The GreenSoul (GS) framework aims to provide a low-cost energy-efficient Information and Communications Technology (ICT) platform which seamlessly augments a traditional public-use building with a set of assets (apps, interactive interfaces, device adaptors, smart meters and a Decision Support Engine), which mediate in the interactions of users with their environments and the energy consuming devices or systems present in them. GreenSoul envisions public use buildings as ecosystems of GreenSoul-ed devices which cooperate with other devices, standard Smart Meters and, very importantly, with eco-educated and eco-aware users to minimize the unnecessary energy consumption. GS architecture is supported by a socio-economic behavioural model, which aids on behaviour understanding to turn energy consuming devices into active pro-sustainability agents that manifest to their surrounding users how well or badly they are being manipulated (energy-wise), offer tips about how to use them more efficiently and even adapt their own functioning to avoid energy waste.