134 research outputs found
An Efficient Scheme for Determining the Power Loss in Wind-PV Based on Deep Learning
Power loss is a bottleneck in every power system and it has been in focus of majority of the researchers and industry. This paper proposes a new method for determining the power loss in wind-solar power system based on deep learning. The main idea of the proposed scheme is to freeze the feature extraction
layer of the deep Boltzmann network and deploy deep learning training model as the source model. The
sample data with closer distribution with the data under consideration is selected by defining the maximum mean discrepancy contribution coefficient. The power loss calculation model is developed by configuring the deep neural network through the sample data. The deep learning model is deployed to simulate the non-linear mapping relationship between the load data, power supply data, bus voltage data and the grid loss rate during power grid operation. The proposed algorithm is applied to an actual power grid to evaluate its effectiveness. Simulation results show that the proposed algorithm effectively improved the system performance in terms of accuracy, fault tolerance, nonlinear fitting and timeliness as compared with existing schemes.publishedVersio
A resilient and distributed near real-time traffic forecasting application for Fog computing environments
In this paper we propose an architecture for a city-wide traffic modeling and prediction service based on the Fog Computing paradigm. The work assumes an scenario in which a number of distributed antennas receive data generated by vehicles across the city. In the Fog nodes data is collected, processed in local and intermediate nodes, and finally forwarded to a central Cloud location for further analysis. We propose a combination of a data distribution algorithm, resilient to back-haul connectivity issues, and a traffic modeling approach based on deep learning techniques to provide distributed traffic forecasting capabilities. In our experiments, we leverage real traffic logs from one week of Floating Car Data (FCD) generated in the city of Barcelona by a road-assistance service fleet comprising thousands of vehicles. FCD was processed across several simulated conditions, ranging from scenarios in which no connectivity failures occurred in the Fog nodes, to situations with long and frequent connectivity outage periods. For each scenario, the resilience and accuracy of both the data distribution algorithm, and the learning methods were analyzed. Results show that the data distribution process running in the Fog nodes is resilient to back-haul connectivity issues and is able to deliver data to the Cloud location even in presence of severe connectivity problems. Additionally, the proposed traffic modeling and forecasting method exhibits better behavior when run distributed in the Fog instead of centralized in the Cloud, especially when connectivity issues occur that force data to be delivered out of order to the Cloud.This project is partially supported by the European Research Council (ERC), Spain under the European Unionâs Horizon 2020 research and innovation programme (grant agreement No 639595).
It is also partially supported by the Ministry of Economy of Spain under contract TIN2015-65316-P and Generalitat de Catalunya, Spain under contract 2014SGR1051, by the ICREA Academia program, and by the BSC-CNS Severo Ochoa program (SEV-2015-0493). The authors gratefully acknowledge the Reial Automvil Club
de Catalunya (RACC) for the dataset of Floating Car Data provided.Peer ReviewedPostprint (published version
A data-driven market simulator for small data environments
The 'signature method' refers to a collection of feature extraction techniques for multivariate time series, derived from the theory of controlled differential equations. There is a great deal of flexibility as to how this method can be applied. On the one hand, this flexibility allows the method to be tailored to specific problems, but on the other hand, can make precise application challenging. This paper makes two contributions. First, the variations on the signature method are unified into a general approach, the \emph{generalised signature method}, of which previous variations are special cases. A primary aim of this unifying framework is to make the signature method more accessible to any machine learning practitioner, whereas it is now mostly used by specialists. Second, and within this framework, we derive a canonical collection of choices that provide a domain-agnostic starting point. We derive these choices as a result of an extensive empirical study on 26 datasets and go on to show competitive performance against current benchmarks for multivariate time series classification. Finally, to ease practical application, we make our techniques available as part of the open-source [redacted] project
Automatic generation of workload profiles using unsupervised learning pipelines
The complexity of resource usage and power consumption on cloud-based applications makes the understanding of application behavior through expert examination difficult. The difficulty increases when applications are seen as âblack boxesâ, where only external monitoring can be retrieved. Furthermore, given the different amount of scenarios and applications, automation is required. Here we examine and model application behavior by finding behavior phases. We use Conditional Restricted Boltzmann Machines (CRBM) to model time-series containing resources traces measurements like CPU, Memory and IO. CRBMs can be used to map a given given historic window of trace behaviour into a single vector. This low dimensional and time-aware vector can be passed through clustering methods, from simplistic ones like k-means to more complex ones like those based on Hidden Markov Models (HMM). We use these methods to find phases of similar behaviour in the workloads. Our experimental evaluation shows that the proposed method is able to identify different phases of resource consumption across different workloads. We show that the distinct phases contain specific resource patterns that distinguish them.Peer ReviewedPostprint (published version
Generating drawdown-realistic financial price paths using path signatures
A novel generative machine learning approach for the simulation of sequences
of financial price data with drawdowns quantifiably close to empirical data is
introduced. Applications such as pricing drawdown insurance options or
developing portfolio drawdown control strategies call for a host of
drawdown-realistic paths. Historical scenarios may be insufficient to
effectively train and backtest the strategy, while standard parametric Monte
Carlo does not adequately preserve drawdowns. We advocate a non-parametric
Monte Carlo approach combining a variational autoencoder generative model with
a drawdown reconstruction loss function. To overcome issues of numerical
complexity and non-differentiability, we approximate drawdown as a linear
function of the moments of the path, known in the literature as path
signatures. We prove the required regularity of drawdown function and
consistency of the approximation. Furthermore, we obtain close numerical
approximations using linear regression for fractional Brownian and empirical
data. We argue that linear combinations of the moments of a path yield a
mathematically non-trivial smoothing of the drawdown function, which gives one
leeway to simulate drawdown-realistic price paths by including drawdown
evaluation metrics in the learning objective. We conclude with numerical
experiments on mixed equity, bond, real estate and commodity portfolios and
obtain a host of drawdown-realistic paths
Ensemble deep learning: A review
Ensemble learning combines several individual models to obtain better
generalization performance. Currently, deep learning models with multilayer
processing architecture is showing better performance as compared to the
shallow or traditional classification models. Deep ensemble learning models
combine the advantages of both the deep learning models as well as the ensemble
learning such that the final model has better generalization performance. This
paper reviews the state-of-art deep ensemble models and hence serves as an
extensive summary for the researchers. The ensemble models are broadly
categorised into ensemble models like bagging, boosting and stacking, negative
correlation based deep ensemble models, explicit/implicit ensembles,
homogeneous /heterogeneous ensemble, decision fusion strategies, unsupervised,
semi-supervised, reinforcement learning and online/incremental, multilabel
based deep ensemble models. Application of deep ensemble models in different
domains is also briefly discussed. Finally, we conclude this paper with some
future recommendations and research directions
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