In the study of geographical patterns of disease, multivariate areal data models proposed so far in the literature (Ma and Carlin, 2007; Carlin and Banerjee, 2003; Knorr-Held and Best, 2001) have allowed to handle several features of a phenomenon at the same time. In this paper, we propose a new model for areal data, the Spatial Temporal Conditional Auto-Regressive (STCAR) model, that allows to handle the spatial dependence between sites as well as the temporal dependence among the realizations, in the presence of<br />measurements recorded at each spatial location in a time interval. Inspired by the Generalized Multivariate Conditional Auto-Regressive (GMCAR) model published by Jin, Carlin, and Banerjee (2005), the STCAR model reduces the unknown parameters to the single parameter of spatial association estimated at every period considered. Unlike the Vector Auto-Regressive (VAR) model proposed by Sims (1980), in addition, its space-time autoregressive matrix takes into account the spatial localization of the realizations sampled. Moreover, we already know that the main areas of application of these models<br />relate to disease mapping, disease clustering, ecological analysis (Lawson, Browne, and Vidal Rodeiro, 2003). In this work, however, the STCAR model is applied in business, exploiting the analogy between the danger of contracting a particular disease and the risk of falling into bankruptcy, in order to “reconstruct” the spatial temporal distribution of expected bankruptcies of small and medium enterprises of the province of Lecce (Italy).
Depending on their input, wind power forecasting models are classified as physical or statistical approaches or a combination of both. Physical models use physical considerations, as meteorological information (Numerical Weather Prediction) and technical characteristics of the wind turbines (hub height, power curve, thrust coefficient). Statistical models use explanatory variables and online measurements, usually employing recursive techniques, like recursive least squares or artificial neural networks (ANNs) which perform a non-linear mapping and provide a robust approach for wind prediction. In this paper a new hybrid method (mixing physical and statistical approaches) is proposed, based on the wavelet decomposition technique and on artificial neural networks, in order to predict power production of a wind farm in different time horizons: 1, 3, 6, 12 and 24 hours. In particular, two approaches are compared, both based on the time series of on-line measured wind power and on the Numerical Weather Predictions; in the first approach, the forecast is carried out only through the training of a neural network which, in the second approach is, instead, used in combination with the wavelet decomposition technique, improving the performance especially over the short time horizons. The error of the different forecast systems is investigated for various forecasting horizons and statistical distributions of the error are calculated and presented.
Wind forecasting models are divided in two main categories, physical and statistical. The former are based on Numerical Weather Prediction (NWP). The statistical models, on the other hand, use on-line measurements. In this paper we use hybrid models, which combine elements of both types. In particular three forecast systems based on Artificial Neural Networks have been developed in order to predict power production of a wind farm in different time horizons: 1, 3, 6, 12 and 24 hours. In the first forecast system, the neural network has been used only as a statistic model based on time series of on-line measured wind power, while in the second and third forecast systems different combinations of measured data and numerical weather predictions have been used, improving the performance in the predictions, especially over long time horizons. The error of the different forecast systems is investigated for various forecasting horizons and statistical distributions of the error are calculated and presented.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.