Dataset Information

SECURES-Met: A European meteorological data set suitable for electricity modelling applications.

ABSTRACT: The modelling of electricity production and demand requires highly specific and comprehensive meteorological data. One challenge is the high temporal frequency as electricity production and demand modelling typically is done with hourly data. On the other side the European electricity market is highly connected, so that a pure country-based modelling is not expedient and at least the whole European Union (EU) area has to be considered. Additionally, the spatial resolution of the data set must be able to represent the thermal conditions, which requires high spatial resolution at least in mountainous regions. All these requirements lead to huge data amounts for historic observations and even more for climate change projections for the whole 21^st century. Thus, we have developed the aggregated European wide climate data set SECURES-Met that has a temporal resolution of one hour, covers the whole EU area and other selected European countries, has a reasonable size but considers the high spatial variability.

SUBMITTER: Formayer H

PROVIDER: S-EPMC10484998 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature

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SECURES-Met: A European meteorological data set suitable for electricity modelling applications.

Formayer Herbert H Nadeem Imran I Leidinger David D Maier Philipp P Schöniger Franziska F Suna Demet D Resch Gustav G Totschnig Gerhard G Lehner Fabian F

Scientific data 20230907 1

The modelling of electricity production and demand requires highly specific and comprehensive meteorological data. One challenge is the high temporal frequency as electricity production and demand modelling typically is done with hourly data. On the other side the European electricity market is highly connected, so that a pure country-based modelling is not expedient and at least the whole European Union (EU) area has to be considered. Additionally, the spatial resolution of the data set must be ...[more]

PMID: 37679367

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Project description:BackgroundStatistical models are regularly used in the forecasting and surveillance of infectious diseases to guide public health. Variable selection assists in determining factors associated with disease transmission, however, often overlooked in this process is the evaluation and suitability of the statistical model used in forecasting disease transmission and outbreaks. Here we aim to evaluate several modelling methods to optimise predictive modelling of Ross River virus (RRV) disease notifications and outbreaks in epidemiological important regions of Victoria and Western Australia.Methodology/principal findingsWe developed several statistical methods using meteorological and RRV surveillance data from July 2000 until June 2018 in Victoria and from July 1991 until June 2018 in Western Australia. Models were developed for 11 Local Government Areas (LGAs) in Victoria and seven LGAs in Western Australia. We found generalised additive models and generalised boosted regression models, and generalised additive models and negative binomial models to be the best fit models when predicting RRV outbreaks and notifications, respectively. No association was found with a model's ability to predict RRV notifications in LGAs with greater RRV activity, or for outbreak predictions to have a higher accuracy in LGAs with greater RRV notifications. Moreover, we assessed the use of factor analysis to generate independent variables used in predictive modelling. In the majority of LGAs, this method did not result in better model predictive performance.Conclusions/significanceWe demonstrate that models which are developed and used for predicting disease notifications may not be suitable for predicting disease outbreaks, or vice versa. Furthermore, poor predictive performance in modelling disease transmissions may be the result of inappropriate model selection methods. Our findings provide approaches and methods to facilitate the selection of the best fit statistical model for predicting mosquito-borne disease notifications and outbreaks used for disease surveillance.

Dataset Information

SECURES-Met: A European meteorological data set suitable for electricity modelling applications.

Publications

SECURES-Met: A European meteorological data set suitable for electricity modelling applications.

Similar Datasets

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