BUSCADOR RECOLECTA

Dades de recerca associades a la tesi doctoral: Garrido Baserba, Manel. (2013). Development of an environmental decision support system for the selection and integrated assessment of process flow diagrams in wastewater treatment. [Tesi doctoral, Universitat de Girona, Catalunya]. Recuperat de http://hdl.handle.net/10803/108953, The wastewater treatment plays an important role in the maintenance of natural water resources. However, regardless of the technology used or the level of treatment required, the treatment plants of the XXI century are highly complex systems that not only need to meet technical requirements, but also environmental and economic criteria. In this context, decision support systems for environmental domains (English, Environmental Decision Support Systems or EDSS) are configured as an effective tool to support the selection and evaluation of integrated water treatment alternatives. The EDSS designed can be defined as interactive software, flexible and adaptable, which links the numerical models / algorithms, techniques and environmental ontologies, knowledge-based environment, and is capable of supporting decision making, either in choosing between different alternatives, improving potential solutions, or in the integrated assessment using methodologies ranging from environmental (Life Cycle Analysis) to economic, La depuració d’aigües residuals juga un paper fonamental en el manteniment dels recursos hídrics naturals. Tanmateix, sigui quina sigui la tecnologia emprada o el nivell de depuració requerit, les plantes de tractament del segle XXI són sistemes d’alta complexitat, que no només han de satisfer requeriments de tipus tècnic, sinó també de tipus ambiental i econòmic. En aquest context, els sistemes de suport a la decisió en dominis ambientals (en anglès, Environmental Decision Support Systems o EDSS) es configuren com una eina eficaç per donar suport a la selecció i a l’avaluació integrada de diferents alternatives de depuració d’aigües. El EDSS dissenyat pot definir-se com un programari interactiu, flexible i adaptable, que vincula els models numèrics/algoritmes amb tècniques basades en el coneixement i ontologies ambientals, i que és capaç de donar suport a la presa de decisió, ja sigui en l’elecció entre diferents alternatives, millorant una solució, o bé en l’avaluació integrada a través de metodologies ambientals (Anàlisi de Cicle de Vida) i econòmiques

Dades primàries associades a una comunicació a congrés presentada a CIRED 2019. 25th International Conference on Electricity Distribution: Madrid, 3-6 June 2019, The files are in CSV, no special software required to interpret the data, The dataset includes two files: one called 'demand', with the active power demand from 10 different households, in Watts units and hourly resolution, and another called 'generation', with the solar generation considering the current installed photovoltaic power in each household, in Watts units and hourly resolution. The total peak power installed is 30.5 kWp. The demand dataset was provided by the DSO (Distribution System Operator). No collection/generation work done by the researchers. The solar generation profile was download from PVGIS portal, considering the location and the installed peak power of each household. The study multiplies the data profiles (demand and generation) from the Dataset by different factors according to the scenario. Not other preprocessing than scaling the data. Energy consumption profiles from file 'demand.csv' correspond to the same households than the energy production profiles from file 'generation.csv'. For example, 'C1' column in 'demand.csv' and 'G1' column in 'generation.csv' correspond to consumption and generation profiles from Household 1, respectively. In the study the authors talk about two scenarios. The Scenario 1 considers the current demand profiles, from file 'demand.csv'. The Scenario 2, considers a future high electrification of households final demand, so the demand profiles in file 'demand.csv' are scaled by a factor of 14. In both scenarios, all generation profiles in 'generation.csv' are scaled by a factor depending on the total installed photovoltaic power, considering than the original generation profiles correspond to a total peak power of 30.5 kWp. The dataset contains the data collected from 0:00 am to 11:00 pm on July 1, 2018