A Deep Learning Technique using Low Sampling rate for residential Non Intrusive Load Monitoring

• URL: http://arxiv.org/abs/2111.05120v1
• Date: Sun, 7 Nov 2021 23:01:36 GMT
• Title: A Deep Learning Technique using Low Sampling rate for residential Non Intrusive Load Monitoring
• Authors: Ronak Aghera, Sahil Chilana, Vishal Garg, Raghunath Reddy
• Abstract summary: Non-intrusive load monitoring (NILM) or energy disaggregation, is a blind source separation problem. We propose a novel deep neural network-based approach for performing load disaggregation on low frequency power data. Our neural network is capable of generating detailed feedback of demand-side, providing vital insights to the end-user.
• Score: 0.19662978733004596
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Individual device loads and energy consumption feedback is one of the important approaches for pursuing users to save energy in residences. This can help in identifying faulty devices and wasted energy by devices when left On unused. The main challenge is to identity and estimate the energy consumption of individual devices without intrusive sensors on each device. Non-intrusive load monitoring (NILM) or energy disaggregation, is a blind source separation problem which requires a system to estimate the electricity usage of individual appliances from the aggregated household energy consumption. In this paper, we propose a novel deep neural network-based approach for performing load disaggregation on low frequency power data obtained from residential households. We combine a series of one-dimensional Convolutional Neural Networks and Long Short Term Memory (1D CNN-LSTM) to extract features that can identify active appliances and retrieve their power consumption given the aggregated household power value. We used CNNs to extract features from main readings in a given time frame and then used those features to classify if a given appliance is active at that time period or not. Following that, the extracted features are used to model a generation problem using LSTM. We train the LSTM to generate the disaggregated energy consumption of a particular appliance. Our neural network is capable of generating detailed feedback of demand-side, providing vital insights to the end-user about their electricity consumption. The algorithm was designed for low power offline devices such as ESP32. Empirical calculations show that our model outperforms the state-of-the-art on the Reference Energy Disaggregation Dataset (REDD).

Related papers

• Time-Series Forecasting and Sequence Learning Using Memristor-based Reservoir System [2.6473021051027534]
  We develop a memristor-based echo state network accelerator that features efficient temporal data processing and in-situ online learning. The proposed design is benchmarked using various datasets involving real-world tasks, such as forecasting the load energy consumption and weather conditions. It is observed that the system demonstrates reasonable robustness for device failure below 10%, which may occur due to stuck-at faults.
  arXiv  Detail & Related papers  (2024-05-22T05:07:56Z)
• Neuromorphic Split Computing with Wake-Up Radios: Architecture and Design via Digital Twinning [97.99077847606624]
  This work proposes a novel architecture that integrates a wake-up radio mechanism within a split computing system consisting of remote, wirelessly connected, NPUs. A key challenge in the design of a wake-up radio-based neuromorphic split computing system is the selection of thresholds for sensing, wake-up signal detection, and decision making.
  arXiv  Detail & Related papers  (2024-04-02T10:19:04Z)
• Low-Frequency Load Identification using CNN-BiLSTM Attention Mechanism [0.0]
  Non-intrusive Load Monitoring (NILM) is an established technique for effective and cost-efficient electricity consumption management. This paper presents a hybrid learning approach, consisting of a convolutional neural network (CNN) and a bidirectional long short-term memory (BILSTM) CNN-BILSTM model is adept at extracting both temporal (time-related) and spatial (location-related) features, allowing it to precisely identify energy consumption patterns at the appliance level.
  arXiv  Detail & Related papers  (2023-11-14T21:02:27Z)
• Braille Letter Reading: A Benchmark for Spatio-Temporal Pattern Recognition on Neuromorphic Hardware [50.380319968947035]
  Recent deep learning approaches have reached accuracy in such tasks, but their implementation on conventional embedded solutions is still computationally very and energy expensive. We propose a new benchmark for computing tactile pattern recognition at the edge through letters reading. We trained and compared feed-forward and recurrent spiking neural networks (SNNs) offline using back-propagation through time with surrogate gradients, then we deployed them on the Intel Loihimorphic chip for efficient inference. Our results show that the LSTM outperforms the recurrent SNN in terms of accuracy by 14%. However, the recurrent SNN on Loihi is 237 times more energy
  arXiv  Detail & Related papers  (2022-05-30T14:30:45Z)
• Domain Knowledge Aids in Signal Disaggregation; the Example of the Cumulative Water Heater [68.8204255655161]
  We present an unsupervised low-frequency method aimed at detecting and disaggregating the power used by Cumulative Water Heaters (CWH) in residential homes. Our model circumvents the inherent difficulty of unsupervised signal disaggregation by using both the shape of a power spike and its time of occurrence. Our model, despite its simplicity, offers promising applications: detection of mis-configured CWHs on off-peak contracts and slow performance degradation.
  arXiv  Detail & Related papers  (2022-03-22T10:39:19Z)
• Appliance Level Short-term Load Forecasting via Recurrent Neural Network [6.351541960369854]
  We present an STLF algorithm for efficiently predicting the power consumption of individual electrical appliances. The proposed method builds upon a powerful recurrent neural network (RNN) architecture in deep learning.
  arXiv  Detail & Related papers  (2021-11-23T16:56:37Z)
• Adversarial Energy Disaggregation for Non-intrusive Load Monitoring [78.47901044638525]
  Energy disaggregation, also known as non-intrusive load monitoring (NILM), challenges the problem of separating the whole-home electricity usage into appliance-specific individual consumptions. Recent advances reveal that deep neural networks (DNNs) can get favorable performance for NILM. We introduce the idea of adversarial learning into NILM, which is new for the energy disaggregation task.
  arXiv  Detail & Related papers  (2021-08-02T03:56:35Z)
• Energy-Efficient Model Compression and Splitting for Collaborative Inference Over Time-Varying Channels [52.60092598312894]
  We propose a technique to reduce the total energy bill at the edge device by utilizing model compression and time-varying model split between the edge and remote nodes. Our proposed solution results in minimal energy consumption and $CO$ emission compared to the considered baselines.
  arXiv  Detail & Related papers  (2021-06-02T07:36:27Z)
• A Novel Hybrid Deep Learning Approach for Non-Intrusive Load Monitoring of Residential Appliance Based on Long Short Term Memory and Convolutional Neural Networks [0.0]
  Energy disaggregation or nonintrusive load monitoring (NILM), is a single-input blind source discrimination problem. This article presents a new approach for power disaggregation by using a deep recurrent long short term memory (LSTM) network combined with convolutional neural networks (CNN)
  arXiv  Detail & Related papers  (2021-04-15T22:34:20Z)
• Energy Drain of the Object Detection Processing Pipeline for Mobile Devices: Analysis and Implications [77.00418462388525]
  This paper presents the first detailed experimental study of a mobile augmented reality (AR) client's energy consumption and the detection latency of executing Convolutional Neural Networks (CNN) based object detection. Our detailed measurements refine the energy analysis of mobile AR clients and reveal several interesting perspectives regarding the energy consumption of executing CNN-based object detection.
  arXiv  Detail & Related papers  (2020-11-26T00:32:07Z)
• PowerGAN: Synthesizing Appliance Power Signatures Using Generative Adversarial Networks [26.247345639292668]
  Non-intrusive load monitoring (NILM) allows users and energy providers to gain insight into home appliance electricity consumption. Current techniques for NILM are trained using significant amounts of labeled appliances power data. We present the first truly synthetic appliance power signature generator, PowerGAN.
  arXiv  Detail & Related papers  (2020-07-20T05:10:40Z)

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.