Home Electricity Data Generator (HEDGE): An open-access tool for the generation of electric vehicle, residential demand, and PV generation profiles

• URL: http://arxiv.org/abs/2310.01661v1
• Date: Mon, 2 Oct 2023 21:51:42 GMT
• Title: Home Electricity Data Generator (HEDGE): An open-access tool for the generation of electric vehicle, residential demand, and PV generation profiles
• Authors: Flora Charbonnier, Thomas Morstyn, Malcolm McCulloch
• Abstract summary: The Home Electricity Data Generator (HEDGE) is an open-access tool for the random generation of realistic residential energy data. It generates realistic daily profiles of residential PV generation, household electric loads, and electric vehicle consumption and at-home availability. Generative adversarial networks (GANs) are then trained to generate realistic synthetic data representative of each behaviour groups.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In this paper, we present the Home Electricity Data Generator (HEDGE), an open-access tool for the random generation of realistic residential energy data. HEDGE generates realistic daily profiles of residential PV generation, household electric loads, and electric vehicle consumption and at-home availability, based on real-life UK datasets. The lack of usable data is a major hurdle for research on residential distributed energy resources characterisation and coordination, especially when using data-driven methods such as machine learning-based forecasting and reinforcement learning-based control. A key issue is that while large data banks are available, they are not in a usable format, and numerous subsequent days of data for a given single home are unavailable. We fill these gaps with the open-access HEDGE tool which generates data sequences of energy data for several days in a way that is consistent for single homes, both in terms of profile magnitude and behavioural clusters. From raw datasets, pre-processing steps are conducted, including filling in incomplete data sequences and clustering profiles into behaviour clusters. Generative adversarial networks (GANs) are then trained to generate realistic synthetic data representative of each behaviour groups consistent with real-life behavioural and physical patterns.

Related papers

• Personalized Federated Learning via Active Sampling [50.456464838807115]
  This paper proposes a novel method for sequentially identifying similar (or relevant) data generators. Our method evaluates the relevance of a data generator by evaluating the effect of a gradient step using its local dataset. We extend this method to non-parametric models by a suitable generalization of the gradient step to update a hypothesis using the local dataset provided by a data generator.
  arXiv  Detail & Related papers  (2024-09-03T17:12:21Z)
• Differentially Private Publication of Electricity Time Series Data in Smart Grids [8.87717126222646]
  Time-series of power consumption over geographical areas are valuable data sources to study consumer behavior and guide energy policy decisions. However, publication of such data raises significant privacy issues, as it may reveal sensitive details about personal habits and lifestyles. We introduce emT (S Private Timeseries), a novel method for DP-compliant publication of electricity consumption data.
  arXiv  Detail & Related papers  (2024-08-24T23:30:09Z)
• Synthesizing Multimodal Electronic Health Records via Predictive Diffusion Models [69.06149482021071]
  We propose a novel EHR data generation model called EHRPD. It is a diffusion-based model designed to predict the next visit based on the current one while also incorporating time interval estimation. We conduct experiments on two public datasets and evaluate EHRPD from fidelity, privacy, and utility perspectives.
  arXiv  Detail & Related papers  (2024-06-20T02:20:23Z)
• SubjectDrive: Scaling Generative Data in Autonomous Driving via Subject Control [59.20038082523832]
  We present SubjectDrive, the first model proven to scale generative data production in a way that could continuously improve autonomous driving applications. We develop a novel model equipped with a subject control mechanism, which allows the generative model to leverage diverse external data sources for producing varied and useful data.
  arXiv  Detail & Related papers  (2024-03-28T14:07:13Z)
• TSGM: A Flexible Framework for Generative Modeling of Synthetic Time Series [61.436361263605114]
  Time series data are often scarce or highly sensitive, which precludes the sharing of data between researchers and industrial organizations. We introduce Time Series Generative Modeling (TSGM), an open-source framework for the generative modeling of synthetic time series.
  arXiv  Detail & Related papers  (2023-05-19T10:11:21Z)
• Smart Home Energy Management: VAE-GAN synthetic dataset generator and Q-learning [15.995891934245334]
  We propose a novel variational auto-encoder-generative adversarial network (VAE-GAN) technique for generating time-series data on energy consumption in smart homes. We tested the online performance of Q-learning-based HEMS with real-world smart home data.
  arXiv  Detail & Related papers  (2023-05-14T22:22:16Z)
• Leveraging the Potential of Novel Data in Power Line Communication of Electricity Grids [0.5399800035598186]
  Electricity grids have become an essential part of daily life, even if they are often not noticed in everyday life. We propose two first-of-its-kind datasets based on measurements in a broadband powerline communications infrastructure. Both datasets FiN-1 and FiN-2 were collected during real practical use in a part of the German low-voltage grid that supplies around 4.4 million people.
  arXiv  Detail & Related papers  (2022-09-23T11:37:02Z)
• TRoVE: Transforming Road Scene Datasets into Photorealistic Virtual Environments [84.6017003787244]
  This work proposes a synthetic data generation pipeline to address the difficulties and domain-gaps present in simulated datasets. We show that using annotations and visual cues from existing datasets, we can facilitate automated multi-modal data generation.
  arXiv  Detail & Related papers  (2022-08-16T20:46:08Z)
• Learning Task-Aware Energy Disaggregation: a Federated Approach [1.52292571922932]
  Non-intrusive load monitoring (NILM) aims to find individual devices' power consumption profiles based on aggregated meter measurements. Yet collecting such residential load datasets require both huge efforts and customers' approval on sharing metering data. We propose a decentralized and task-adaptive learning scheme for NILM tasks, where nested meta learning and federated learning steps are designed for learning task-specific models collectively.
  arXiv  Detail & Related papers  (2022-04-14T05:53:41Z)
• Investigating Underlying Drivers of Variability in Residential Energy Usage Patterns with Daily Load Shape Clustering of Smart Meter Data [53.51471969978107]
  Large-scale deployment of smart meters has motivated increasing studies to explore disaggregated daily load patterns. This paper aims to shed light on the mechanisms by which electricity consumption patterns exhibit variability.
  arXiv  Detail & Related papers  (2021-02-16T16:56:27Z)
• Building power consumption datasets: Survey, taxonomy and future directions [2.389598109913753]
  This work is proposed to survey, study and visualize the numerical and methodological nature of building energy consumption datasets. A total of thirty-one databases are examined and compared in terms of several features, such as the geographical location, period of collection, number of monitored households, sampling rate of collected data, number of sub-metered appliances, extracted features and release date. A novel dataset has been presented, namely Qatar university dataset, which is an annotated power consumption anomaly detection dataset.
  arXiv  Detail & Related papers  (2020-09-17T10:19:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.