Attention in Recurrent Neural Networks for Energy Disaggregation

Gkalinikis, Nikolaos Virtsionis; Nalmpantis, Christoforos; Vrakas, Dimitris

doi:10.1007/978-3-030-61527-7_36

Cited by 8 publications

(9 citation statements)

References 16 publications

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“…In particular, the method presents the third-best MAE, being inferior only to PCNN AE and PCNN LSTM. Regarding energy estimation, the RE metric is low (equal to 0.19), thus the proposed method is outperformed only by the CNN [19] and the WGRU [22] algorithms. Finally, the proposed solution presents the highest precision in terms of status estimation.…”

Section: Comparison With Other Methodsmentioning

confidence: 99%

“…The selected appliances represent a larger group of appliances since both single-state and multi-state appliances are considered. Additionally, detailed results regarding the analysis of such appliances can be found in several relevant works [19,20,22,[25][26][27]; thus, a comprehensive comparative analysis can be performed. Finally, low energy-consuming appliances such as game consoles and phone chargers have not been investigated, being of trivial importance and hard to be identified in terms of NILM algorithm application [19].…”

Section: Heat Pump 202mentioning

confidence: 99%

“…On the other hand, state-based approaches [19][20][21][22][23][24][25][26][27] mainly require lower frequency data. These approaches do not detect state transitions.…”

Section: Introductionmentioning

confidence: 99%

“…On the contrary, they parse all available data of a time-series, even if no events occur. In such approaches, the appliance must operate for at least some minutes to determine if it is on [20,22]. There are even cases where the appliance end-use has to be fully completed to estimate the power consumption [19,24].…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Scalable Real-Time Non-Intrusive Load Monitoring System for the Estimation of Household Appliance Power Consumption

Athanasiadis

Doukas²,

Papadopoulos

et al. 2021

Energies

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Smart-meter technology advancements have resulted in the generation of massive volumes of information introducing new opportunities for energy services and data-driven business models. One such service is non-intrusive load monitoring (NILM). NILM is a process to break down the electricity consumption on an appliance level by analyzing the total aggregated data measurements monitored from a single point. Most prominent existing solutions use deep learning techniques resulting in models with millions of parameters and a high computational burden. Some of these solutions use the turn-on transient response of the target appliance to calculate its energy consumption, while others require the total operation cycle. In the latter case, disaggregation is performed either with delay (in the order of minutes) or only for past events. In this paper, a real-time NILM system is proposed. The scope of the proposed NILM algorithm is to detect the turning-on of a target appliance by processing the measured active power transient response and estimate its consumption in real-time. The proposed system consists of three main blocks, i.e., an event detection algorithm, a convolutional neural network classifier and a power estimation algorithm. Experimental results reveal that the proposed system can achieve promising results in real-time, presenting high computational and memory efficiency.

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Section: Comparison With Other Methodsmentioning

confidence: 99%

Section: Heat Pump 202mentioning

confidence: 99%

“…On the other hand, state-based approaches [19][20][21][22][23][24][25][26][27] mainly require lower frequency data. These approaches do not detect state transitions.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Scalable Real-Time Non-Intrusive Load Monitoring System for the Estimation of Household Appliance Power Consumption

Athanasiadis

Doukas²,

Papadopoulos

et al. 2021

Energies

View full text Add to dashboard Cite

show abstract

“…In contrast, Odysseas et al still used low-frequency data, but used the total power over a period of time before moment t as input for predicting the power consumption of the appliance at moment t. Three network architectures that use sliding windows for real-time energy disaggregation were proposed, namely, Long-Short Term Memory (LSTM) networks, Gated Recurrent Units (GRU) networks and Short Sequence-to-Point (Short Seq2point) networks, which were more effective on multi-state appliances than on two-state appliances [30]. Similarly, Virtsionis et al [31] took only past data as input. They proposed a deep neural network based on attentional mechanisms, which is called Self-Attentive-Energy-Disaggregation (SAED), using additive and dot attention mechanisms for experiments, respectively.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Energy Disaggregation Algorithm Based on Multi-Channels DCNN and Autoregressive Model

et al. 2022

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Energy disaggregation refers to the process of obtaining the energy consumption of several appliances in a house by disaggregating the aggregate power consumption measured by an electrical meter. Currently, deep learning methods are widely applied in this field. Real-time energy disaggregation is an important branch of energy disaggregation. Based on the Short Sequence-to-Point (Short Seq2point) (Odysseas) network structure, a real-time energy disaggregation algorithm based on multi-channels deep convolutional neural networks(MC-DCNN) and autoregressive model(AR) is proposed in this paper, which obtains the energy consumption of appliances at the current time point by disaggregating the historical aggregate power consumption to achieve delivering disaggregation results in real-time. The proposed method takes the original aggregate power sequence and differential power signal as the input of the network, and extracts the information of different time lengths in the sequence using multi-channels deep convolutional neural networks with a modified concatenate layer, so that the network can adapt to different appliances with different operating modes. In addition, the traditional autoregressive model is added as the linear component for solving the problem that the scale of the output is insensitive to the scale of the input in the neural network model. Finally, the proposed method was tested on the UK-DALE and REDD datasets, and the experimental results show that the method has good disaggregation performance on both datasets, has a small number of parameters and achieves fast inference.

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SAED: self-attentive energy disaggregation

2021

Self Cite

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The field of energy disaggregation deals with the approximation of appliance electric consumption using only the aggregate consumption measurement of a mains meter. Recent research developments have used deep neural networks and outperformed previous methods based on Hidden Markov Models. On the other hand, deep learning models are computationally heavy and require huge amounts of data. The main objective of the current paper is to incorporate the attention mechanism into neural networks in order to reduce their computational complexity. For the attention mechanism two different versions are utilized, named Additive and Dot Attention. The experiments show that they perform on par, while the Dot mechanism is slightly faster. The two versions of self-attentive neural networks are compared against two state-of-the-art energy disaggregation deep learning models. The experimental results show that the proposed architecture achieves faster or equal training and inference time and with minor performance drop depending on the device or the dataset.

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Attention in Recurrent Neural Networks for Energy Disaggregation

Cited by 8 publications

References 16 publications

A Scalable Real-Time Non-Intrusive Load Monitoring System for the Estimation of Household Appliance Power Consumption

A Scalable Real-Time Non-Intrusive Load Monitoring System for the Estimation of Household Appliance Power Consumption

Real-Time Energy Disaggregation Algorithm Based on Multi-Channels DCNN and Autoregressive Model

SAED: self-attentive energy disaggregation

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