Evaluation and forecasting of gas consumption by statistical analysis

This study includes an approach to understand the factors affecting gas demand and to forecast gas consumption by multivariable regression analysis for the capital city of Ankara, Turkey. The process of the study is developing a statistical model and testing the model for the past years to understand how accurate it is. After obtaining the most reliable model, forecasting the gas consumption for the remaining days of 2002 and the year 2005 is performed. During the project, by the means of economical conditions, two scenarios, optimistic and pessimistic, are developed to get the idea of how the input variables are going to be changed in the following years. The model yields very satisfactory results and the range of gas consumption for the years 2002 and 2005 for the city Ankara is obtained

Artificial neural network modeling for forecasting gas consumption

This study includes an approach to evaluate and forecast gas consumption by Artificial Neural Network (ANN) modeling for the capital city of Ankara, Turkey. ANN models have been trained to perform complex functions in various fields of application including the forecasting process. The process of the study is examining the factors affecting the output and training the ANNs to decide the optimum parameters to be used in forecasting the gas consumption for the remaining days of 2002 and the year 2005. During the project, some optimistic (assuming the stable economical conditions) and the pessimistic (considering an economical crisis) scenarios are handled to get the idea for the following years' gas consumption amount in a range that will remain between these two scenarios

Benchmark analysis of forecasted seasonal temperature over different climatic areas

From a long-term perspective, an improvement of seasonal forecasting, which is often exclusively based on climatology, could provide a new capability for the management of energy resources in a time scale of just a few months. This paper regards a benchmark analysis in relation to long-term temperature forecasts over Italy in the year 2010, comparing the eni-kassandra meteo forecast (e-kmf®) model, the Climate Forecast System–National Centers for Environmental Prediction (CFS-NCEP) model, and the climatological reference (based on 25-year data) with observations. Statistical indexes are used to understand the reliability of the prediction of 2-m monthly air temperatures with a perspective of 12 weeks ahead. The results show how the best performance is achieved by the e-kmf® system which improves the reliability for long-term forecasts compared to climatology and the CFS-NCEP model. By using the reliable high-performance forecast system, it is possible to optimize the natural gas portfolio and management operations, thereby obtaining a competitive advantage in the European energy market

Effects Of Different Cavity Disinfectants On Shear Bond Strength Of A Silorane-Based Resin Composite

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