National Repository of Grey Literature 4 records found  Search took 0.00 seconds. 
Forecasting models of solar powerplants and wind farms production based on numerical weather prediction models
Farda, Aleš ; Štěpánek, Petr ; Zahradníček, Pavel ; Rajdl, Kamil ; Meitner, Jan
Performance and potential of daily based forecasts of renewable energy sources power production have been investigated for the Czech Republic. The role of individual numerical weather prediction model errors has been researched and ensemble based technique has been studied as the mean to obtain more precise and reliable results providing a benefit for end users.\n\n
Prediction of crop yields using satellite remote sensing
Lukas, V. ; Trnka, Miroslav ; Semerádová, Daniela ; Rajdl, Kamil ; Balek, Jan ; Štěpánek, Petr ; Zahradníček, Pavel ; Hlavinka, Petr ; Žalud, Zdeněk
Knowledge of the crop yield with sufficient lead time prior to harvest is crucial for both the farm management and the agro-food sector policy. The aim of this study was to test feasibility of developing crop yield forecasting model in Czech Republic for winter wheat, spring barley and oilseed rape based on 2000-2014 database of vegetation indices Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI2) from Moderate Resolution Imaging Spectroradiometer (MODIS) on Terra satellite in form of 16-days composites. An average yield data were collected for 14 selected districts in the Czech Republic that represent the regions with more intensive agricultural production among varying climate and topographic conditions. The viability of the concept was proven in years with significant yield decline i.e. 2000, 2003, 2006 and 2012, when yields of cereals were significantly affected by occurred drought periods. More stable regression results were achieved in the most productive areas such as Olomouc and Prerov, whilst models in highland regions were influenced by lower acreage of three modelled crops and higher prevalence of fodder crops. In most cases, EVI2 showed higher correlations to the crop yield together with using an average value of all composites during vegetation period.
Testing a statistical forecasting model of electric energy consumption for two regions in the Czech Republic
Rajdl, Kamil ; Farda, Aleš ; Štěpánek, Petr ; Zahradníček, Pavel
Precise forecasting of electric energy consumption is of great importance for the electric power industry. It helps system operators optimally schedule and control power systems, and even slight improvements in prediction accuracy might yield large savings or profits. For these reasons, many forecasting models based on various principles have been developed and studied. Because of energy consumption’s strong dependence on weather conditions, such models often utilize outputs from numerical weather prediction models. In this study, we present and analyse a statistical model for forecasting hourly electrical energy consumption by customers of E.ON Energie, a.s. in two regions of the Czech Republic. The aim of this model is to create hourly predictions up to several days in advance. The model uses hourly data of consumed energy from 2011–2014 and corresponding predictions of temperature and cloudiness provided by the ALADIN/ CZ model. The statistical model is based on a regression analysis applied to appropriate data samples and supplemented by several optional post-processing methods. Specifically, we use a robust linear regression algorithm to identify energy consumption’s dependence on temperature, the meteorological variable with the largest influence on consumption. Our post-processing methods focused on removing prediction bias resulting from economic situations (represented by the goss domestic product, GDP) and sudden temperature changes. We analysed the presented model from the point of view of the hourly predictions’ accuracy for 2013 and 2014. Accuracy was primarily measured by mean absolute error. It was evaluated for individual months, and the effects of individual parts of the model on accuracy value are shown. Introduction
Predictability of wind famrs operated by Amper-Market company
Farda, Aleš ; Rajdl, Kamil ; Štěpánek, Petr
This report contains an analysis of the electric energy production of wind power plants of the company Amper Market a.s. from the point of view of its predictability, with the main objective of determining the potential accuracy of hourly predictions of the power production. To calculate the analyzed predictions a regression forecasting model utilizing wind speed and direction in two height levels is used. The necessary values of these meteorological variables are obtained from three numerical weather prediction models – Aladin, EPS Aladin and IFS. The accuracy of the energy production predictions is optimized regarding the choice of suitable geographical points for obtaining outputs of the numerical models, the combine utilization of the models and individual setting of the regression model for the single power plants. The analysis was performed based on data from the period January 2013 to April 2014.

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