Time series of useful energy consumption patterns for energy system modeling

Priesmann, Jan; Nolting, Lars; Kockel, Christina; Praktiknjo, Aaron

doi:10.1038/s41597-021-00907-w

Cited by 29 publications

(7 citation statements)

References 13 publications

(11 reference statements)

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“…• JERICHO-E-usage dataset. This is an energy consumption time series dataset provided in [26]. This dataset covers the hourly energy consumption of 38 regions in German with different energy categories (e.g., residential, industrial, commerce, and mobility sectors) in the whole year of 2019.…”

Section: Appendix a Additional Experimentsmentioning

confidence: 99%

Nonstationary Temporal Matrix Factorization for Multivariate Time Series Forecasting

Chen¹,

Zhang²,

Zhao³

et al. 2022

Preprint

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Modern time series datasets are often high-dimensional, incomplete/sparse, and nonstationary. These properties hinder the development of scalable and efficient solutions for time series forecasting and analysis. To address these challenges, we propose a Nonstationary Temporal Matrix Factorization (NoTMF) model, in which matrix factorization is used to reconstruct the whole time series matrix and vector autoregressive (VAR) process is imposed on a properly differenced copy of the temporal factor matrix. This approach not only preserves the low-rank property of the data but also offers consistent temporal dynamics. The learning process of NoTMF involves the optimization of two factor matrices and a collection of VAR coefficient matrices. To efficiently solve the optimization problem, we derive an alternating minimization framework, in which subproblems are solved using conjugate gradient and least squares methods. In particular, the use of conjugate gradient method offers an efficient routine and allows us to apply NoTMF on large-scale problems. Through extensive experiments on Uber movement speed dataset, we demonstrate the superior accuracy and effectiveness of NoTMF over other baseline models. Our results also confirm the importance of addressing the nonstationarity of real-world time series data such as spatiotemporal traffic flow/speed.

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Section: Appendix a Additional Experimentsmentioning

confidence: 99%

Nonstationary Temporal Matrix Factorization for Multivariate Time Series Forecasting

Chen¹,

Zhang²,

Zhao³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…where T w is the length of weekly segment along time axis. The reason for considering the long-term correlations is that the work of [36] and [37] have found that electricity usage shows weekly patterns periodically in one certain period . The framework of the proposed MG-ASTGCN is illustrated in Fig.…”

Section: A Spatial-temporal Correlations Extraction Via Mg-astgcnmentioning

confidence: 99%

Deep Reinforcement Learning for Optimal Power Flow with Renewables Using Graph Information

Li¹,

Zhang²,

Wang³

et al. 2021

Preprint

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Renewable energy resources (RERs) have been increasingly integrated into modern power systems, especially in large-scale distribution networks (DNs). In this paper, we propose a deep reinforcement learning (DRL)-based approach to dynamically search for the optimal operation point, i.e., optimal power flow (OPF), in DNs with a high uptake of RERs. Considering uncertainties and voltage fluctuation issues caused by RERs, we formulate OPF into a multi-objective optimization (MOO) problem. To solve the MOO problem, we develop a novel DRL algorithm leveraging the graphical information of the distribution network. Specifically, we employ the state-ofthe-art DRL algorithm, i.e., deep deterministic policy gradient (DDPG), to learn an optimal strategy for OPF. Since power flow reallocation in the DN is a consecutive process, where nodes are self-correlated and interrelated in temporal and spatial views, to make full use of DNs' graphical information, we develop a multi-grained attention-based spatial-temporal graph convolution network (MG-ASTGCN) for spatial-temporal graph information extraction, preparing for its sequential DDPG. We validate our proposed DRL-based approach in modified IEEE 33, 69, and 118-bus radial distribution systems (RDSs) and show that our DRL-based approach outperforms other benchmark algorithms. Our experimental results also reveal that MG-ASTGCN can significantly accelerate the DDPG training process and improve DDPG's capability in reallocating power flow for OPF. The proposed DRL-based approach also promotes DNs' stability in the presence of node faults, especially for large-scale DNs.

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“…The great success of DNNs in such domains can in part be explained by the huge amount of available data to train complex models. Following the trend, the last few years have also seen an explosion in the amount of time series data of various modalities such as electrocardiogram [3], power consumption [4], human motion [5] and satellite images [6] among others. Due to its wide range of applications, time series analysis has attracted researchers who developed deep learning-based models for time series clustering [7], averaging [8], forecasting [9] and classification [10].…”

Section: Introductionmentioning

confidence: 99%

A study of Knowledge Distillation in Fully Convolutional Network for Time Series Classification

Devanne

Weber

et al. 2022

2022 International Joint Conference on Neural Networks (IJCNN)

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In recent years, deep learning revolutionized the field of machine learning. While many applications of deep learning are observed in computer vision, other domains like natural language processing (NLP) or speech recognition also benefited from advances in deep learning research. More recently, the field of time series analysis and more especially time series classification (TSC) also witnessed the emergence of deep neural networks providing competitive results. Through the years, the proposed network architectures became deeper and deeper pushing the performance higher. While these very deep models achieve impressive accuracy, their training and deployment became challenging. Indeed, a large number of GPUs is often required to train state-of-the-art networks and obtain high performances. While the requirements needed for the training step can be acceptable, deploying very deep neural networks can be difficult especially in embedded systems (e.g. robots) or devices with limited resources (e.g. web browsers, smartphones). In this context, knowledge distillation is a machine learning task consisting in transferring knowledge from a large model to a smaller one with fewer parameters. The goal is to create a lighter model mimicking the predictions of a larger one in order to obtain similar performances with a fraction of the computational cost. In this paper, we introduce and explore the concept of knowledge distillation for the specific task of TSC. We also present a first experimental study showing promising results on several datasets of the UCR time series archive. As current state-of-the-art models for TSC are deep and sometimes ensemble of models, we believe that knowledge distillation could become an important research area in the coming years.

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Time series of useful energy consumption patterns for energy system modeling

Cited by 29 publications

References 13 publications

Nonstationary Temporal Matrix Factorization for Multivariate Time Series Forecasting

Nonstationary Temporal Matrix Factorization for Multivariate Time Series Forecasting

Deep Reinforcement Learning for Optimal Power Flow with Renewables Using Graph Information

A study of Knowledge Distillation in Fully Convolutional Network for Time Series Classification

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