Wind energy forecasting with neural networks: a literature review

Renewable energy is intermittent by nature and to integrate this energy into the Grid while assuring safety and stability the accurate forecasting of there newable energy generation is critical. Wind Energy prediction is based on the ability to forecast wind. There are many methods for wind forecasting based on the statistical properties of the wind time series and in the integration of meteorological information, these methods are being used commercially around the world. But one family of new methods for wind power fore castingis surging based on Machine Learning Deep Learning techniques. This paper analyses the characteristics of the Wind Speed time series data and performs a literature review of recently published works of wind power forecasting using Machine Learning approaches (neural and deep learning networks), which have been published in the last few years.Peer ReviewedPostprint (published version

Ensemble Committees for Stock Return Classification and Prediction

This paper considers a portfolio trading strategy formulated by algorithms in the field of machine learning. The profitability of the strategy is measured by the algorithm's capability to consistently and accurately identify stock indices with positive or negative returns, and to generate a preferred portfolio allocation on the basis of a learned model. Stocks are characterized by time series data sets consisting of technical variables that reflect market conditions in a previous time interval, which are utilized produce binary classification decisions in subsequent intervals. The learned model is constructed as a committee of random forest classifiers, a non-linear support vector machine classifier, a relevance vector machine classifier, and a constituent ensemble of k-nearest neighbors classifiers. The Global Industry Classification Standard (GICS) is used to explore the ensemble model's efficacy within the context of various fields of investment including Energy, Materials, Financials, and Information Technology. Data from 2006 to 2012, inclusive, are considered, which are chosen for providing a range of market circumstances for evaluating the model. The model is observed to achieve an accuracy of approximately 70% when predicting stock price returns three months in advance.Comment: 15 pages, 4 figures, Neukom Institute Computational Undergraduate Research prize - second plac

Transform Diabetes - Harnessing Transformer-Based Machine Learning and Layered Ensemble with Enhanced Training for Improved Glucose Prediction.

Type 1 diabetes is a common chronic disease characterized by the body’s inability to regulate the blood glucose level, leading to severe health consequences if not handled manually. Accurate blood glucose level predictions can enable better disease management and inform subsequent treatment decisions. However, predicting future blood glucose levels is a complex problem due to the inherent complexity and variability of the human body. This thesis investigates using a Transformer model to outperform a state-of-the-art Convolutional Recurrent Neural Network model by forecasting blood glucose levels on the same dataset. The problem is structured, and the data is preprocessed as a multivariate multi-step time series. A unique Layered Ensemble technique that Enhances the Training of the final model is introduced. This technique manages missing data and counters potential issues from other techniques by employing both a Long Short-Term Memory model and a Transformer model together. The experimental results show that this novel ensemble technique reduces the root mean squared error by approximately 14.28% when predicting the blood glucose level 30 minutes in the future compared to the state-of-the-art model. This improvement highlights the potential of this approach to assist diabetes patients with effective disease management

Transform Diabetes - Harnessing Transformer-Based Machine Learning and Layered Ensemble with Enhanced Training for Improved Glucose Prediction.

Type 1 diabetes is a common chronic disease characterized by the body’s inability to regulate the blood glucose level, leading to severe health consequences if not handled manually. Accurate blood glucose level predictions can enable better disease management and inform subsequent treatment decisions. However, predicting future blood glucose levels is a complex problem due to the inherent complexity and variability of the human body. This thesis investigates using a Transformer model to outperform a state-of-the-art Convolutional Recurrent Neural Network model by forecasting blood glucose levels on the same dataset. The problem is structured, and the data is preprocessed as a multivariate multi-step time series. A unique Layered Ensemble technique that Enhances the Training of the final model is introduced. This technique manages missing data and counters potential issues from other techniques by employing both a Long Short-Term Memory model and a Transformer model together. The experimental results show that this novel ensemble technique reduces the root mean squared error by approximately 14.28% when predicting the blood glucose level 30 minutes in the future compared to the state-of-the-art model. This improvement highlights the potential of this approach to assist diabetes patients with effective disease management

Development of Grid e-Infrastructure in South-Eastern Europe

Over the period of 6 years and three phases, the SEE-GRID programme has established a strong regional human network in the area of distributed scientific computing and has set up a powerful regional Grid infrastructure. It attracted a number of user communities and applications from diverse fields from countries throughout the South-Eastern Europe. From the infrastructure point view, the first project phase has established a pilot Grid infrastructure with more than 20 resource centers in 11 countries. During the subsequent two phases of the project, the infrastructure has grown to currently 55 resource centers with more than 6600 CPUs and 750 TBs of disk storage, distributed in 16 participating countries. Inclusion of new resource centers to the existing infrastructure, as well as a support to new user communities, has demanded setup of regionally distributed core services, development of new monitoring and operational tools, and close collaboration of all partner institution in managing such a complex infrastructure. In this paper we give an overview of the development and current status of SEE-GRID regional infrastructure and describe its transition to the NGI-based Grid model in EGI, with the strong SEE regional collaboration.Comment: 22 pages, 12 figures, 4 table