Context Aware Energy Disaggregation Using Adaptive Bidirectional LSTM Models

Kaselimi, Maria; Doulamis, Nikolaos; Voulodimos, Athanasios; Protopapadakis, Eftychios

doi:10.1109/tsg.2020.2974347

Cited by 123 publications

(70 citation statements)

References 31 publications

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“…Load event matching-based methods are proposed in [44], [45], [47] to perform load disaggregation. Moreover, several NILM algorithms were presented in [48], [49] based on deep neural networks. The external weather and environmental conditions were used to improve the NILM performance in [48].…”

Section: B Nilm Methodsmentioning

confidence: 99%

“…Moreover, several NILM algorithms were presented in [48], [49] based on deep neural networks. The external weather and environmental conditions were used to improve the NILM performance in [48]. Furthermore, generalized NILM algorithms across houses were proposed in [48], [50].…”

Section: B Nilm Methodsmentioning

confidence: 99%

“…The external weather and environmental conditions were used to improve the NILM performance in [48]. Furthermore, generalized NILM algorithms across houses were proposed in [48], [50]. In [40], a NILM algorithm has been proposed based on aided linear integer programming.…”

Section: B Nilm Methodsmentioning

confidence: 99%

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Residential Power Forecasting Based on Affinity Aggregation Spectral Clustering

2020

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Power utility companies rely on forecasting to anticipate future consumption needs, plan power production, and schedule the selling/purchasing of power. We present a novel method to forecast the power consumption of a single house based on non-intrusive load monitoring (NILM) and affinity aggregation spectral clustering, with the idea of extending it to forecasting consumption in a larger set of houses like a microgrid. First, we use a graph to model statistical relationships between appliances. Specifically, the ON/OFF time-of-day and state duration probabilities are used to compute graph edge weights and establish statistical relationships among appliances. Then, leveraging on our previous work on NILM, we disaggregate the smart meter aggregate power profile into individual appliance power profiles. With the disaggregated individual power profiles and the corresponding ON/OFF time-of-day and state duration probabilities, we next propose a method to forecast each appliance's power profiles using affinity aggregation spectral clustering. For the proposed method, we incorporate human behaviour and environmental influence in terms of calendar and seasonal contexts in order to enhance the forecasting performance. Finally, the results of appliance-level forecasting are aggregated to perform house-level forecasting. To test our proposed forecasting method, we use four publicly available datasets and compare our method against several existing approaches such as autoregressive integrated moving average, similar profile load forecast, artificial neural network, and recent NILM-based forecasting. Experimentally, we examine how well the proposed forecasting method can generalize appliance behaviours from one house to another. Results clearly show that our method is more accurate than existing approaches. INDEX TERMS Appliance identification, demand side management, non-intrusive load monitoring, NILM, power forecasting, smart grid, spectral clustering.

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Section: B Nilm Methodsmentioning

confidence: 99%

Section: B Nilm Methodsmentioning

confidence: 99%

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Residential Power Forecasting Based on Affinity Aggregation Spectral Clustering

2020

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“…LSTM has achieved state-ofthe-art performance in many research areas associated with time sequence analysis. In the field of power system, it has been successfully applied for load forecasting [34], solar generation forecasting [35], transient stability analysis [36] and energy disaggregation [37]. The detailed knowledge of RNN is given in [38].…”

Section: B Lstm Autoencodermentioning

confidence: 99%

Reconstruction Residuals Based Long-term Voltage Stability Assessment Using Autoencoders

Yang

Qiu

Tong

2020

Journal of Modern Power Systems and Clean Energy

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Real-time voltage stability assessment (VSA) has long been an extensively research topic. In recent years, rapidly mounting deep learning methods have pushed online VSA to a new height that large amounts of learning algorithms are applied for VSA from the perspective of measurement data. Deep learning methods generally require a large dataset which contains measurements in both secure and insecure states, or even unstable state. However, in practice, the data of insecure or unstable state is very rare, as the power system should be guaranteed to operate far away from voltage collapse. Under this circumstance, this paper proposes an autoencoder based method which merely needs data of secure state to evaluate voltage stability of a power system. The principle of this method is that an autoencoder purely trained by secure data is expected to only create precise reconstruction for secure data, while it fails to rebuild data of insecure states. Thus, the residual of reconstruction is effective in indicating VSA. Besides, to develop a more accurate and robust algorithm, long short-term memory (LSTM) networks combined with fully-connected (FC) layers are used to build the autoencoder, and a moving strategy is introduced to bias the features of testing data toward the secure feature domain. Numerous experiments and comparison with traditional machine learning algorithms demonstrate the effectiveness and high accuracy of the proposed method.

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“…1(c) for an illustration). Another potential application is energy disaggregation of appliances by non-intrusive load monitoring (NILM) [8,11,12,19], namely disaggregating the total consumption readings into the consumption patterns of each individual appliance, where the total consumption reading of a house represents a class and the appliances in a house are the modalities within this class. Therefore, dividing a class into multiple modalities is similar to disaggregating the total consumption of all appliance into the consumption of each appliance.…”

Section: Introductionmentioning

confidence: 99%

Within-class multimodal classification

Wan

Wang

Scotney

et al. 2020

Multimed Tools Appl

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In many real-world classification problems there exist multiple subclasses (or clusters) within a class; in other words, the underlying data distribution is within-class multimodal. One example is face recognition where a face (i.e. a class) may be presented in frontal view or side view, corresponding to different modalities. This issue has been largely ignored in the literature or at least under studied. How to address the within-class multimodality issue is still an unsolved problem. In this paper, we present an extensive study of within-class multimodality classification. This study is guided by a number of research questions, and conducted through experimentation on artificial data and real data. In addition, we establish a case for within-class multimodal classification that is characterised by the concurrent maximisation of between-class separation, between-subclass separation and within-class compactness. Extensive experimental results show that within-class multimodal classification consistently leads to significant performance gains when within-class multimodality is present in data. Furthermore, it has been found that within-class multimodal classification offers a competitive solution to face recognition under different lighting and face pose conditions. It is our opinion that the case for within-class multimodal classification is established, therefore there is a milestone to be achieved in some machine learning algorithms (e.g. Gaussian mixture model) when within-class multimodal classification, or part of it, is pursued.

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Context Aware Energy Disaggregation Using Adaptive Bidirectional LSTM Models

Cited by 123 publications

References 31 publications

Residential Power Forecasting Based on Affinity Aggregation Spectral Clustering

Residential Power Forecasting Based on Affinity Aggregation Spectral Clustering

Reconstruction Residuals Based Long-term Voltage Stability Assessment Using Autoencoders

Within-class multimodal classification

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