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.
Deploying energy disaggregation models in the real-world is a challenging task. These models are usually deep neural networks and can be costly when running on a server or prohibitive when the target device has limited resources. Deep learning models are usually computationally expensive and they have large storage requirements. Reducing the computational cost and the size of a neural network, without trading off any performance is not a trivial task. This paper suggests a novel neural architecture that has less learning parameters, smaller size and fast inference time without trading off performance. The proposed architecture performs on par with two popular strong baseline models. The key characteristic is the Fourier transformation which has no learning parameters and it can be computed efficiently.
Non-intrusive load monitoring is a blind source separation task that has been attracting significant interest from researchers working in the field of energy informatics. However, despite the considerable progress, there are a very limited number of tools and libraries dedicated to the problem of energy disaggregation. Herein, we report the development of a novel open-source framework named Torch-NILM in order to help researchers and engineers take advantage of the benefits of Pytorch. The aim of this research is to tackle the comparability and reproducibility issues often reported in NILM research by standardising the experimental setup, while providing solid baseline models by writing only a few lines of code. Torch-NILM offers a suite of tools particularly useful for training deep neural networks in the task of energy disaggregation. The basic features include: (i) easy-to-use APIs for running new experiments, (ii) a benchmark framework for evaluation, (iii) the implementation of popular architectures, (iv) custom data loaders for efficient training and (v) automated generation of reports.
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