info:eu-repo/semantics/publishedVersion

[eng] In order to mitigate the climate change effects, the world is undergoing an energy transition from polluting sources towards renewable energies. This transition is turning the electricity system more dependent on atmospheric conditions and more prone to suffer the effects of climate variability. The atmospheric circulation is changing in certain aspects due to increasing concentrations of greenhouse gases in the atmosphere, but it also varies from year to year due to natural variability processes occurring in the Earth system at timescales of weeks, months and years. The atmosphere interacts with other components of the Earth System such as the ocean, the cryosphere or the continental surface, that evolve more slowly than the atmosphere and drive the low-frequency variability. The natural climate oscillations that occur at those timescales impact wind speed and wind power generation. Therefore a better knowledge of how the wind resource varies at sub-seasonal, seasonal and decadal time scales is key to understand the risks that the electricity system is facing. Anticipating this variability would also be helpful to many stakeholders in the energy sector to take precautionary actions. Forecasts at sub-seasonal, seasonal and decadal timescales are starting to be possible recently thanks to advances in climate modelling capabilities. Because climate variability is partly driven by coupled physical processes occurring in the Earth, numerical models that represent the interaction between different components of the Earth system can be employed to produce forecasts at these scales. The science of climate prediction deals with the challenge of producing predictions beyond meteorological timescales (i.e. weeks, months and years ahead) although not reaching the centennial timescales, which are studied with scenario-based climate projections. Climate predictions employ the current state of the atmosphere, the ocean, the cryosphere, and the land surface to produce numerical integrations of each component and the forcings and interactions between them to model the evolution of the Earth system as a whole. However, the usage of climate predictions in the wind power sector (or more generally in any specific decision-making context) poses a series of difficulties due to many complex aspects of this type of predictions. The efforts devoted in many initiatives to bring the needs of the users to the center of the discussion have given rise to the field of climate services. In order to assist decision-making, it is not only desirable to have the best predictions available but also to tailor them to the specific needs of each user. To achieve this goal, a dialogue with stakeholders needs to be established, and a trans- disciplinary approach needs to be set up to take advantage of the developments in many research fields regarding knowledge transfer and communication. The work presented in this dissertation advances the knowledge required to produce and successfully apply climate predictions to decision-making in the wind power sector and deals with the three aforementioned challenges: a) understanding the impact of climate oscillations at sub-seasonal and seasonal timescales on wind resource; b) developing methods to produce forecasts of wind speed and wind power generation at this scales; and c) facilitating the uptake of those predictions by means of a climate-services-based approach.[cat] Per tal de mitigar els efectes del canvi climàtic, tots els països del món estan duent a terme una transició energètica de fonts contaminants cap a energies renovables. Aquesta transició està incrementant la sensibilitat del sistema elèctric a les condicions atmosfèriques i fent-lo més vulnerable als efectes de la variabilitat climàtica. A escales de setmanes, mesos i anys, l'atmosfera interacciona amb altres components del sistema Terra com l'oceà, la criosfera o la superfície continental, que evolucionen més lentament que l'atmosfera, condicionant-ne la seva variabilitat a baixa freqüència. Al seu torn, les oscil·lacions que tenen lloc a aquestes escales temporals impacten el vent i la generació d'energia eòlica. Per tant, un millor coneixement de com varia el recurs eòlic a escales sub-estacionals, estacionals i decadals permetrà anticipar els riscs a què el sistema elèctric està sotmès. En segon lloc, anticipar aquesta variabilitat climàtica seria de gran utilitat a diversos actors del sistema energètic. L'ús de models climàtics que representen les interaccions entre les diferents components del sistema Terra permet abordar el repte de produir pronòstics més enllà de l'escala meteorològica (és a dir, a setmanes, mesos i anys vista). Malgrat tot, l'ús de les prediccions climàtiques en el sector de l'energia eòlica presenta una sèrie de dificultats degut a les complexitats d'aquest tipus de previsions. Per tal d'assistir la presa de decisions, no només és necessari disposar de les millors prediccions possibles sinó que cal també ajustar-les a les necessitats específiques de cada ús. Aquest objectiu només es pot assolir amb un diàleg constant i transdisciplinari entre els científics i les parts interessades que integri els avenços en diferents àmbits respecte la transferència de coneixement i la comunicació. Aquesta tesi avança el coneixement necessari per tal de produir i aplicar prediccions climàtiques a la presa de decisions per part de la indústria eòlica, abordant tres reptes: a) avaluar l'impacte d'oscil·lacions climàtiques sub-estacionals i estacional en el recurs eòlic; b) desenvolupar mètodes per produir prediccions de vent o de generació eòlica a aquestes escales; i c) facilitar l'adopció d'aquestes previsions mitjançant una aproximació basada en els serveis climàtics

Towards verifying correctness of wireless sensor network applications using Insense and Spin

The design and implementation of wireless sensor network applications often require domain experts, who may lack expertise in software engineering, to produce resource-constrained, concurrent, real-time software without the support of high-level software engineering facilities. The Insense language aims to address this mismatch by allowing the complexities of synchronisation, memory management and event-driven programming to be borne by the language implementation rather than by the programmer. The main contribution of this paper is all initial step towards verifying the correctness of WSN applications with a focus on concurrency. We model part of the synchronisation mechanism of the Insense language implementation using Promela constructs and verify its correctness using SPIN. We demonstrate how a previously published version of the mechanism is shown to be incorrect by SPIN, and give complete verification results for the revised mechanism.Preprin