Statistical models can be utilised for ozone forecasting at the microlocations of a complex terrain, i.e., a terrain with a large geographical diversity or urban terrain. We developed a modelling and prediction algorithm that can be used in, or in accordance with, a mobile air-quality measurement station. Such a mobile station would enable the set-up of a statistical model and a relatively rapid access to the model's predictions for a specific geographical micro-location without a large quantity of historical of measurements. Uncertainty information about the model's predictions is also usually required. In addition, such a model can adapt to long-term changes, such as climate changes. We proposed Gaussian process models for the described modelling and prediction. In particular, we selected evolving Gaussian-process models that update on-line with the incoming measurement data. The proposed algorithm for the mobile air-quality measurement and the forecasting station was evaluated on measurements from five locations in Slovenia with different topographical and geographical properties. The obtained evaluation results confirm the feasibility of the concept.
COBISS.SI-ID: 29306919
Many meteorological parameters present a natural diurnal cycle because they are directly or indirectly dependent on sunshine exposure. The solar radiation diurnal pattern is important to energy production, agriculture, prognostic models, health and general climatology. This article aims at introducing a new type of radial frequency diagram – hereafter called sunflower – for the analysis of solar radiation data in connection with energy production and also for climatological studies. The diagram is based on two-dimensional data sorting. Firstly data are sorted into classes representing hours in a day. Then the data in each hourly class is sorted into classes of the observed variable values. The relative frequencies of the value classes are shown as sections on each hour’s segment in a radial diagram. The radial diagram forms a unique pattern for each analysed dataset. Therefore it enables the quick detection of features and the comparison of several such patterns belonging to the different datasets being analysed. The sunflower diagram enables a quick and comprehensive understanding of the information about diurnal cycle of the solar radiation data. It enables in a graphical form, quick screening and long-term statistics of huge data quantities when searching for their diurnal features and finding the differences between the data for several locations. The results of the data analysis using the sunflower diagram show how daily or monthly-based patterns are identified within small or huge data sets. The paper demonstrates the sunflower diagram usefulness over a wide range of applications from green energy production to weather analysis and air pollution. For air-pollution data the variant called pollution-flower is created.
COBISS.SI-ID: 28621607
We deal with the problem of forecasting the maximum ozone concentrations in urban microlocations, where reliable alerting of the local population when thresholds have been surpassed is necessary. To improve the forecast, the methodology of integrated models is proposed. The model is based on multilayer perceptron neural networks that use as inputs all available information from QualeAria air-quality model, WRF numerical weather prediction model and onsite measurements of meteorology and air pollution. While air-quality and meteorological models cover large geographical 3-dimensional space, their local resolution is often not satisfactory. On the other hand, empirical methods have the advantage of good local forecasts. Integrated models are used for improved 1-day-ahead forecasting of the maximum hourly value of ozone within each day for representative locations in Slovenia. The WRF meteorological model is used for forecasting meteorological variables and the QualeAria air-quality model for gas concentrations. Their predictions, together with measurements from ground stations, are used as inputs to a neural network. The model validation results show that integrated models noticeably improve ozone forecasts and provide better alert systems.
COBISS.SI-ID: 29554471
Being able to predict high concentrations of tropospheric ozone is important because of its negative impact on human health. In this paper eight regressor-selection methods are utilised in a case study for ozone prediction in the city of Nova Gorica, Slovenia. The comparison of the selected methods proved to be useful for building models that successfully predict the ozone concentrations for the treated case. Different regressors are selected for different models, with different methods based on the validation procedure’s cost functions. Namely, for the model to predict the maximum daily ozone concentration, ten regressors are selected; for the average concentration of ozone between 8.00 and 20.00 h, fifteen regressors are selected; and for the average daily concentration, ten regressors are selected. The result of the study is a regressor selection that is specific for a particular geographical location. Moreover, the study reveals that regressor selection, as well as the obtained models, differ depending on the kind of averaging interval of the ozone concentration.
COBISS.SI-ID: 28481319
Many dynamic systems can be characterized as complex since they have a nonlinear behaviour incorporating a stochastic uncertainty. It has been shown that one of the most appropriate methods for modelling of such systems is based on the application of Gaussian processes (GPs). The GP models provide a probabilistic non-parametric modelling approach for blackbox identification of nonlinear stochastic systems. This book chapter reviews the methods for modelling and control of complex stochastic systems based on GP models. The GP-based modelling method is applied in a process engineering case study, which represents the dynamic modelling and control of a laboratory gas–liquid separator. GP models with different regressors and different covariance functions are obtained and evaluated. A selected GP model of the gas–liquid separator is further used to design an explicit stochastic model predictive controller to ensure the optimal control of the separator.
COBISS.SI-ID: 27718183