Fast Multi-Period Security-Constrained Economic Dispatch Based on Deep Neural Networks

Deep Neural Network-Based Method for Economic Dispatch in Microgrid

Emission Constrained Economic Dispatch for Hydrothermal Coordination

This paper presents an efficient emission constrained economic dispatch algorithm that deals with nonlinear cost function and constraints. It is then incorporated into the dynamic programming based hydrothermal coordination program. The program has been tested on a practical utility system having 32 thermal and 12 hydro generating units. Test results show that a slight increase in production co...

Power System Economic Dispatch Using Traditional and Neural Networks Programs

The introduction of techniques of artificial intelligence in software of control and decision is an essential element in research and development of tomorrow’s power systems. Neural networks are among the techniques most used in the field of artificial intelligence. The economic dispatch is a key sector in the electricity networks, where it must generate less energy for the same demand with goo...

Fast algorithms for learning deep neural networks

With the increase in computation power and data availability in recent times, machine learning and statistics have seen an enormous development and widespread application in areas such as computer vision, computational biology and others. A focus of current research are deep neural nets: nested functions consisting of a hierarchy of layers of thousands of weights and nonlinear, hidden units. Th...

Fast Metric Learning For Deep Neural Networks

Similarity metrics are a core component of many information retrieval and machine learning systems. In this work we propose a method capable of learning a similarity metric from data equipped with a binary relation. By considering only the similarity constraints, and initially ignoring the features, we are able to learn target vectors for each instance using one of several appropriately designe...