Residential Power Traces for Five Houses: The iHomeLab RAPT Dataset

Huber, Patrick; Ott, Melvin; Friedli, Martin; Rumsch, Andreas; Paice, Andrew

doi:10.3390/data5010017

Cited by 15 publications

(9 citation statements)

References 10 publications

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“…We use a real-world dataset, the iHomeLab RAPT dataset, to conduct our research [18]. This dataset includes residential electrical consumption data in appliance-level and aggregated household-level and solar panel (PV) energy production for five households in Switzerland spanning a period of 1.5 to 3.5 years with 5 minutes sampling frequency.…”

Section: Evaluation Study a Experiments Setupmentioning

confidence: 99%

Variational Autoencoder Generative Adversarial Network for Synthetic Data Generation in Smart Home

Razghandi¹,

Zhang²,

Erol‐Kantarci³

et al. 2022

Preprint

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Data is the fuel of data science and machine learning techniques for smart grid applications, similar to many other fields. However, the availability of data can be an issue due to privacy concerns, data size, data quality, and so on. To this end, in this paper, we propose a Variational AutoEncoder Generative Adversarial Network (VAE-GAN) as a smart grid data generative model which is capable of learning various types of data distributions and generating plausible samples from the same distribution without performing any prior analysis on the data before the training phase. We compared the Kullback-Leibler (KL) divergence, maximum mean discrepancy (MMD), and Wasserstein distance between the synthetic data (electrical load and PV production) distribution generated by the proposed model, vanilla GAN network, and the real data distribution, to evaluate the performance of our model. Furthermore, we used five key statistical parameters to describe the smart grid data distribution and compared them between synthetic data generated by both models and real data. Experiments indicate that the proposed synthetic data generative model outperforms the vanilla GAN network. The distribution of VAE-GAN synthetic data is the most comparable to that of real data.

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Section: Evaluation Study a Experiments Setupmentioning

confidence: 99%

Variational Autoencoder Generative Adversarial Network for Synthetic Data Generation in Smart Home

Razghandi¹,

Zhang²,

Erol‐Kantarci³

et al. 2022

Preprint

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show abstract

“…The developed market framework is tested on the iHomeLab RAPT dataset, which consists of the electricity consumption and power production of 5 domestic houses, monitored spanning 1.5 to 3.5 years in 5‐min intervals [32]. The PV produced power and total consumed power columns in house 2 are utilized to shape the net‐load (

p^{\text{load}}-p^{\text{solar}}

) of the prosumer.…”

Section: Computational Experimentsmentioning

confidence: 99%

Forecast‐aided power and flexibility trading of prosumers in peer to peer markets

Yazdani

Doostizadeh

Aminifar

2022

IET Renewable Power Gen

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The ever‐evolving electricity distribution network calls for new effective, transparent and fair market procedures to be implemented. This paper offers a systematic framework for real‐time trading in prosumer communities. A data‐driven robust optimization model that deals with uncertainty is proposed using the flexibility requirement envelope concept. Using machine learning techniques, high quality point and quantile forecasts are prepared to form the envelope. Next, this data is fed into the optimization problem to match the peers and the distribution grid, trading power, and flexibility in a short‐term look‐ahead horizon. To increase the quality of forecasts, a clustering approach accompanied by a pattern recognition machine is proposed to determine the cluster to which the recently observed net‐load data belongs. To provide forecasts, a forecasting machine is developed using a hybrid of tree‐based ensemble learning models, which provides intuitive, robust, and accurate results. Finally, the peer matching is done in a decentralized fashion using the alternating direction method of multipliers (ADMM), while each agent calculates its battery storage aging cost. Case studies demonstrate the hybrid model's capability in providing high quality power and flexibility forecasts compared to single models and ARIMA models and its positive impact on reducing the overall cost of the system.

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“…The number of NILM datasets has been increasing over the last years, see [127,128] for recent overviews and [129][130][131] for the most recent published datasets we are aware of. In Table 3, we characterize only the publicly available datasets that have been used in the reviewed studies.…”

Section: Datasetsmentioning

confidence: 99%

Review on Deep Neural Networks Applied to Low-Frequency NILM

et al. 2021

Self Cite

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This paper reviews non-intrusive load monitoring (NILM) approaches that employ deep neural networks to disaggregate appliances from low frequency data, i.e., data with sampling rates lower than the AC base frequency. The overall purpose of this review is, firstly, to gain an overview on the state of the research up to November 2020, and secondly, to identify worthwhile open research topics. Accordingly, we first review the many degrees of freedom of these approaches, what has already been done in the literature, and compile the main characteristics of the reviewed publications in an extensive overview table. The second part of the paper discusses selected aspects of the literature and corresponding research gaps. In particular, we do a performance comparison with respect to reported mean absolute error (MAE) and F1-scores and observe different recurring elements in the best performing approaches, namely data sampling intervals below 10 s, a large field of view, the usage of generative adversarial network (GAN) losses, multi-task learning, and post-processing. Subsequently, multiple input features, multi-task learning, and related research gaps are discussed, the need for comparative studies is highlighted, and finally, missing elements for a successful deployment of NILM approaches based on deep neural networks are pointed out. We conclude the review with an outlook on possible future scenarios.

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Residential Power Traces for Five Houses: The iHomeLab RAPT Dataset

Cited by 15 publications

References 10 publications

Variational Autoencoder Generative Adversarial Network for Synthetic Data Generation in Smart Home

Variational Autoencoder Generative Adversarial Network for Synthetic Data Generation in Smart Home

Forecast‐aided power and flexibility trading of prosumers in peer to peer markets

Review on Deep Neural Networks Applied to Low-Frequency NILM

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