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Found 8 papers, 1 papers with code
Tensor Power Flow Formulations for Multidimensional Analyses in Distribution Systems
In this paper, we present two multidimensional power flow formulations based on a fixed-point iteration (FPI) algorithm to efficiently solve hundreds of thousands of power flows in distribution systems.
Sparse Training Theory for Scalable and Efficient Agents
A fundamental task for artificial intelligence is learning.
Enhancement of Distribution System State Estimation Using Pruned Physics-Aware Neural Networks
Realizing complete observability in the three-phase distribution system remains a challenge that hinders the implementation of classic state estimation algorithms.
On-line Building Energy Optimization using Deep Reinforcement Learning
Unprecedented high volumes of data are becoming available with the growth of the advanced metering infrastructure.
Scalable Training of Artificial Neural Networks with Adaptive Sparse Connectivity inspired by Network Science
Through the success of deep learning in various domains, artificial neural networks are currently among the most used artificial intelligence methods.
Energy Disaggregation for Real-Time Building Flexibility Detection
Energy is a limited resource which has to be managed wisely, taking into account both supply-demand matching and capacity constraints in the distribution grid.
A topological insight into restricted Boltzmann machines
Thirdly, we show that, for a fixed number of weights, our proposed sparse models (which by design have a higher number of hidden neurons) achieve better generative capabilities than standard fully connected RBMs and GRBMs (which by design have a smaller number of hidden neurons), at no additional computational costs.
Support Vector Machine in Prediction of Building Energy Demand Using Pseudo Dynamic Approach
Thus the relevant days data selection method based on Dynamic Time Warping is used to train SVM model.
