A Gaussian Process Regression for Natural Gas Consumption Prediction Based on Time Series Data

Laib, Oussama; Khadir, Mohamed Tarek; Mihaylova, Lyudmila

doi:10.23919/icif.2018.8455447

Cited by 10 publications

(4 citation statements)

References 15 publications

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“…Consequently, the time series literature has increasingly been dominated by models such as the Gaussian Process [5,6,7,8,9] which enable the inference of full predictive distributions in a principled manner, even in the presence of little data. Indeed, even summarised predictions, such as those given by the predictive posterior mean, have been shown to produce smaller deviations than those provided by competitors such as neural networks in a variety of application domains, for example in [10,11,12].…”

Section: Machine Learning Methods For Time Series Forecastingmentioning

confidence: 99%

MOrdReD: Memory-based Ordinal Regression Deep Neural Networks for Time Series Forecasting

Orozco,

Abbati,

Roberts

2018

Preprint

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Time series forecasting is ubiquitous in the modern world. Applications range from health care, astronomy and include climate modelling, to financial trading and monitoring of critical engineering equipment. To offer robust value over this wide range of activities, models must not only provide accurate forecasts, but also quantify and adjust their uncertainty over time. In this work, we directly tackle both tasks with a novel, fully end-to-end deep learning method. By recasting time series prediction as an ordinal regression task, we develop a principled methodology to assess long-term predictive uncertainty and describe the rich, multi-modal, non-Gaussian behaviour which arises regularly in many problem domains.Notably, our framework is a wholly general-purpose approach that requires little to no user intervention to be used. We showcase this key feature in a large-scale benchmark test with 45 datasets drawn from both a wide range of realworld applications and a comprehensive list of synthetic data. This wide comparison uses, as benchmark methods, state-ofthe-art models from both Machine Learning and Statistics literature, such as the Gaussian Process. We find that our approach not only provides excellent predictive forecasts and associated uncertainty bounds, but also allows us to infer

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Section: Machine Learning Methods For Time Series Forecastingmentioning

confidence: 99%

MOrdReD: Memory-based Ordinal Regression Deep Neural Networks for Time Series Forecasting

Orozco,

Abbati,

Roberts

2018

Preprint

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“…Hotness prediction methods are often associated with time series. By summarizing the time series of hot spots for a long time, scholars trained the stable hotness change trend of hot spots, which has been applied in many fields such as traffic flow and network traffic prediction [14]- [21]. Karimpour [22] proposed a time series model to predict the traffic flow for a certain intersection.…”

Section: Related Workmentioning

confidence: 99%

A Flood-Discharge-Based Spatio-Temporal Diffusion Method for Multi-Target Traffic Hotness Construction From Trajectory Data

Zhang

Qin

et al. 2020

IEEE Access

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“…Nevertheless, There are more than a unique answer to both questions as it is dependent on sensitive factors. In the light of previous conducted work on the Algerian natural gas consumption dataset [39], where three different clustering methods were applied to non-supervisely classify the load profiles, namely: K-means, Mixture of Hierarchical Gaussian Process (MHGP) method [40], which combines Gaussian processes (GPs) approach to model the time series and Dirichlet processes (DPs) to perform clustering. The third is a Hierarchical Based Clustering with Noise (HDBSCAN) [41].…”

Section: Day Consumption Profiles Classification 311 Available Datamentioning

confidence: 99%

Toward efficient energy systems based on natural gas consumption prediction with LSTM Recurrent Neural Networks

Laib

Khadir

Mihaylova

2019

Energy

Self Cite

113

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Finding suitable forecasting methods for an effective management of energy resources is of paramount importance for improving the efficiency in energy consumption and decreasing its impact on the environment. Natural gas is one of the main sources of electrical energy in Algeria and worldwide. To address this demand, this paper introduces a novel hybrid forecasting approach that resolves the two-stage method's deficiency, by designing a Multi Layered Perceptron (MLP) neural network as a nonlinear forecasting model. This model estimates the next day gas consumption profile and selects one of several local models to perform the forecast. The study focuses firstly on an analysis and clustering of natural gas daily consumption profiles, and secondly on building a comprehensive Long Short Term Memory (LSTM) recurrent models according to load behavior. The results are compared with four benchmark approaches: the MLP neural network approach, LSTM, seasonal time series with exogenous variables models and multiple linear regression models. Compared with these alternative approaches and their high dependence on historical loads, the proposed approach presents a new efficient functionality. It estimates the next day consumption profile, which leads to a significant improvement of the forecasting accuracy, especially for days with exceptional customers consumption behavior change.

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A Gaussian Process Regression for Natural Gas Consumption Prediction Based on Time Series Data

Abstract: This is a repository copy of A Gaussian process regression for natural gas consumption prediction based on time series data.

Cited by 10 publications

References 15 publications

MOrdReD: Memory-based Ordinal Regression Deep Neural Networks for Time Series Forecasting

MOrdReD: Memory-based Ordinal Regression Deep Neural Networks for Time Series Forecasting

A Flood-Discharge-Based Spatio-Temporal Diffusion Method for Multi-Target Traffic Hotness Construction From Trajectory Data

Toward efficient energy systems based on natural gas consumption prediction with LSTM Recurrent Neural Networks

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