Previsão de consumos de energia elétrica em Portugal

Trabalho de projecto de mestrado, Matemática Aplicada à Economia e Gestão, Universidade de Lisboa, Faculdade de Ciências, 2016Atualmente vivemos num planeta onde a energia eléctrica é imprescindível. Diariamente, algumas das nossas rotinas Passam por acender luzes, ver televisão, andar de metro/comboio, carregar o telemóvel, entre outras. Mas o que acontece quando se carrega no interruptor e a lâmpada acende? Esta Simples tarefa só é possível depois de se ter produzido a energia necessária para esta acção. Neste projeto começa-se por enquadrar a origem da electricidade no Mundo e a sua evolução até aos dias de hoje. É também definida a cadeia de valor do sector eléctrico em Portugal e os vários consumos de energia eléctrica a nível nacional. Só é possível consumir energia eléctrica depois desta ter sido produzida. Uma vez que não existe informação de consumo de cada cliente, surge assim a necessidade de prever o volume de energia para que este possa ser comprado em mercado e, posteriormente produzido. Este projecto estuda modelos de regressão linear múltiplos que descrevem o consumo de energia eléctrica em Portugal através de variáveis históricas de consumos de energia, temperaturas diárias e velocidade média diária do vento. É de salientar as variáveis referentes a consumos históricos de energia elétrica têm uma especial atenção uma vez que seguem determinados pressupostos, ao contrário das restantes. Para ser possível compreender este estudo, alguns métodos estatísticos são descritos e explicados, bem como vários pressupostos assumidos. Após a definição das variáveis a serem utilizadas em estudo, é feita uma análise preliminar, bem com o estudo de multicolinearidade de cada uma delas. São Também utilizados diferentes métodos de selecção de variáveis na criação de modelos de regressão linear múltipla para garantir que sejam obtidos os modelos com melhores resultados. De modo a ser mais perceptível o modelo que fornece melhores resultados, é realizado um teste a cada um dos modelos com dados que não fazem parte do histórico considerado.Currently we live in a planet where the electric power is essential. Daily, some of our routines pass for lighting light, too see television, floor of underground/train, to load cell phone, among others. But what happens when it loads an interruptor and the light bulb lights? This simple task is possible after if having produced the necessary energy for this action. In this project it is started with the origin and the evolution of the electricity in the world until today. It is also deined the chain of value of the electric sector in Portugal and some consumptions of electric power considered the national level. It is only possible consume electric power after this having been produced. A time that does not exist information of consumptions of each customer, arises the necessity to predict the volume of energy so that this can be bought in market and, later produced. This project studies multiple models of regression linear that describe the consumption of the electric power in Portugal trough historical variables of the consumptions of power, daily average speed of the wind. It is important referring those variables dependents of consumption has a special treatment, because of their suppositions, in contrast of remains. To be possible to understand this study, some statiscal methods described and are explained, as well as the several estimated assumed. After the definition of variable to be used in study, it is made a preliminary analysis, well with the study of multicollinearity of each one of them. Also different methods of election of variable in the creation of models of multiple linear regression are used to guarantee that of election of variable in the creation of models of multiple linear regression are used to guarantee that the resulted models with better are gotten. In order to be perceptible the model that it supplies better resulted, it is carried through a test to each one of models with dates that are not part of historical

MAMMALS IN PORTUGAL : A data set of terrestrial, volant, and marine mammal occurrences in P ortugal

Mammals are threatened worldwide, with 26% of all species being includedin the IUCN threatened categories. This overall pattern is primarily associatedwith habitat loss or degradation, and human persecution for terrestrial mam-mals, and pollution, open net fishing, climate change, and prey depletion formarine mammals. Mammals play a key role in maintaining ecosystems func-tionality and resilience, and therefore information on their distribution is cru-cial to delineate and support conservation actions. MAMMALS INPORTUGAL is a publicly available data set compiling unpublishedgeoreferenced occurrence records of 92 terrestrial, volant, and marine mam-mals in mainland Portugal and archipelagos of the Azores and Madeira thatincludes 105,026 data entries between 1873 and 2021 (72% of the data occur-ring in 2000 and 2021). The methods used to collect the data were: live obser-vations/captures (43%), sign surveys (35%), camera trapping (16%),bioacoustics surveys (4%) and radiotracking, and inquiries that represent lessthan 1% of the records. The data set includes 13 types of records: (1) burrowsjsoil moundsjtunnel, (2) capture, (3) colony, (4) dead animaljhairjskullsjjaws, (5) genetic confirmation, (6) inquiries, (7) observation of live animal (8),observation in shelters, (9) photo trappingjvideo, (10) predators dietjpelletsjpine cones/nuts, (11) scatjtrackjditch, (12) telemetry and (13) vocalizationjecholocation. The spatial uncertainty of most records ranges between 0 and100 m (76%). Rodentia (n=31,573) has the highest number of records followedby Chiroptera (n=18,857), Carnivora (n=18,594), Lagomorpha (n=17,496),Cetartiodactyla (n=11,568) and Eulipotyphla (n=7008). The data setincludes records of species classified by the IUCN as threatened(e.g.,Oryctolagus cuniculus[n=12,159],Monachus monachus[n=1,512],andLynx pardinus[n=197]). We believe that this data set may stimulate thepublication of other European countries data sets that would certainly contrib-ute to ecology and conservation-related research, and therefore assisting onthe development of more accurate and tailored conservation managementstrategies for each species. There are no copyright restrictions; please cite thisdata paper when the data are used in publications.info:eu-repo/semantics/publishedVersio

Mammals in Portugal: a data set of terrestrial, volant, and marine mammal occurrences in Portugal

Mammals are threatened worldwide, with ~26% of all species being included in the IUCN threatened categories. This overall pattern is primarily associated with habitat loss or degradation, and human persecution for terrestrial mammals, and pollution, open net fishing, climate change, and prey depletion for marine mammals. Mammals play a key role in maintaining ecosystems functionality and resilience, and therefore information on their distribution is crucial to delineate and support conservation actions. MAMMALS IN PORTUGAL is a publicly available data set compiling unpublished georeferenced occurrence records of 92 terrestrial, volant, and marine mammals in mainland Portugal and archipelagos of the Azores and Madeira that includes 105,026 data entries between 1873 and 2021 (72% of the data occurring in 2000 and 2021). The methods used to collect the data were: live observations/captures (43%), sign surveys (35%), camera trapping (16%), bioacoustics surveys (4%) and radiotracking, and inquiries that represent less than 1% of the records. The data set includes 13 types of records: (1) burrows | soil mounds | tunnel, (2) capture, (3) colony, (4) dead animal | hair | skulls | jaws, (5) genetic confirmation, (6) inquiries, (7) observation of live animal (8), observation in shelters, (9) photo trapping | video, (10) predators diet | pellets | pine cones/nuts, (11) scat | track | ditch, (12) telemetry and (13) vocalization | echolocation. The spatial uncertainty of most records ranges between 0 and 100 m (76%). Rodentia (n =31,573) has the highest number of records followed by Chiroptera (n = 18,857), Carnivora (n = 18,594), Lagomorpha (n = 17,496), Cetartiodactyla (n = 11,568) and Eulipotyphla (n = 7008). The data set includes records of species classified by the IUCN as threatened (e.g., Oryctolagus cuniculus [n = 12,159], Monachus monachus [n = 1,512], and Lynx pardinus [n = 197]). We believe that this data set may stimulate the publication of other European countries data sets that would certainly contribute to ecology and conservation-related research, and therefore assisting on the development of more accurate and tailored conservation management strategies for each species. There are no copyright restrictions; please cite this data paper when the data are used in publications

Characterisation of microbial attack on archaeological bone

As part of an EU funded project to investigate the factors influencing bone preservation in the archaeological record, more than 250 bones from 41 archaeological sites in five countries spanning four climatic regions were studied for diagenetic alteration. Sites were selected to cover a range of environmental conditions and archaeological contexts. Microscopic and physical (mercury intrusion porosimetry) analyses of these bones revealed that the majority (68%) had suffered microbial attack. Furthermore, significant differences were found between animal and human bone in both the state of preservation and the type of microbial attack present. These differences in preservation might result from differences in early taphonomy of the bones. © 2003 Elsevier Science Ltd. All rights reserved