An alternative approach to estimating the parameters of a generalised Grey Verhulst model: An application to steel intensity of use in the UK

Being able to forecast time series accurately has been quite a popular subject for researchers both in the past and at present. However, researchers have resorted to various forecasting models that have different mathematical backgrounds, such as statistical time series models, causal econometric models, artificial neural networks, fuzzy predictors, evolutionary and genetic algorithms. In this paper, a brief review of a relatively new approach, known as grey system theory is provided. The paper offers an alternative approach to estimating the unknown parameters of the well know GM(1,1) and it is shown that this alternative procedure provides more reliable parameter estimates together with a simple visual framework for assessing whether the properties of the chosen GM(1,1) model are consistent with the actual data. In this paper a flexible generalisation of the Grey–Verhulst model is put forward which when applied to UK steel intensity of use produces very reliable multi step ahead predictions

Five-Year Energy Consumption Perspective in Iran and Required Scenarios for Its Supply

In this century, with increasing society's population and Gross Domestic Product (GDP) trend, energy demand is increased in the countries of the whole world. Nowadays, the use of different Renewable Energy Sources (RESs) in the network has become commonplace and, of course, has been challenged. In this way, forecasting energy demand plays a key role in the development of different parts of a country. In this study, firstly a prediction of consumption and fluctuations in the sources of energy is made, and secondly, regarding different parts of the industry, agriculture, and households, two different scenarios have been analyzed to provide this demand in the future. An Artificial Neural Network (ANN) method has been used to predict energy consumption level, and also the two factors of the increase in population and GDP have been considered. Prediction of population increase rate, with respect to its statistical complexities, is derived from a literature review of other references; the GDP prediction is derived with a conventional method of the Grey method. Then, with the prediction of the aforementioned factors, energy consumption is predicted by a metaheuristic algorithm. Afterwards, scenarios related to the energy consumption are predicted and priorities are given, such as environmental impacts, in order to provide the predicted consumption level. Scenarios will considerably show that the supply and demand should be managed by fossil fuel energy production replaced with RESs in the supply side, and providing products with higher energy efficiency in the demand side

Monitoring and predicting railway subsidence using InSAR and time series prediction techniques

Improvements in railway capabilities have resulted in heavier axle loads and higher speed operations, which increase the dynamic loads on the track. As a result, railway subsidence has become a threat to good railway performance and safe railway operation. The author of this thesis provides an approach for railway performance assessment through the monitoring and prediction of railway subsidence. The InSAR technique, which is able to monitor railway subsidence over a large area and long time period, was selected for railway subsidence monitoring. Future trends of railway subsidence should also be predicted using subsidence prediction models based on the time series deformation records obtained by InSAR. Three time series prediction models, which are the ARMA model, a neural network model and the grey model, are adopted in this thesis. Two case studies which monitor and predict the subsidence of the HS1 route were carried out to assess the performance of HS1. The case studies demonstrate that except for some areas with potential subsidence, no large scale subsidence has occurred on HS1 and the line is still stable after its 10 years' operation. In addition, the neural network model has the best performance in predicting the subsidence of HS1