Demand Response For Residential Uses: A Data Analytics Approach
- URL: http://arxiv.org/abs/2008.02908v1
- Date: Fri, 7 Aug 2020 00:06:38 GMT
- Title: Demand Response For Residential Uses: A Data Analytics Approach
- Authors: Abdelkareem Jaradat, Hanan Lutfiyya, Anwar Haque
- Abstract summary: We introduce a smart system foundation that is applied to user's disaggregated power consumption data.
This system encourages the users to apply Demand Response (DR) by changing their behaviour of using heavier operation modes to lighter modes.
- Score: 5.107653758514456
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In the Smart Grid environment, the advent of intelligent measuring devices
facilitates monitoring appliance electricity consumption. This data can be used
in applying Demand Response (DR) in residential houses through data analytics,
and developing data mining techniques. In this research, we introduce a smart
system foundation that is applied to user's disaggregated power consumption
data. This system encourages the users to apply DR by changing their behaviour
of using heavier operation modes to lighter modes, and by encouraging users to
shift their usages to off-peak hours. First, we apply Cross Correlation (XCORR)
to detect times of the occurrences when an appliance is being used. We then use
The Dynamic Time Warping (DTW) to recognize the operation mode used.
Related papers
- Scaling Wearable Foundation Models [54.93979158708164]
We investigate the scaling properties of sensor foundation models across compute, data, and model size.
Using a dataset of up to 40 million hours of in-situ heart rate, heart rate variability, electrodermal activity, accelerometer, skin temperature, and altimeter per-minute data from over 165,000 people, we create LSM.
Our results establish the scaling laws of LSM for tasks such as imputation, extrapolation, both across time and sensor modalities.
arXiv Detail & Related papers (2024-10-17T15:08:21Z) - Time Series Anomaly Detection in Smart Homes: A Deep Learning Approach [4.340040784481499]
We analyze the patterns pertaining to the power consumption of dishwashers used in two houses of the REFIT dataset.
Two autoencoders with 1D-CNN and TCN as backbones are trained to differentiate the normal patterns from the abnormal ones.
Our results indicate that TCN outperforms CNN1D in detecting anomalies in energy consumption.
arXiv Detail & Related papers (2023-02-28T17:26:27Z) - Federated Privacy-preserving Collaborative Filtering for On-Device Next
App Prediction [52.16923290335873]
We propose a novel SeqMF model to solve the problem of predicting the next app launch during mobile device usage.
We modify the structure of the classical matrix factorization model and update the training procedure to sequential learning.
One more ingredient of the proposed approach is a new privacy mechanism that guarantees the protection of the sent data from the users to the remote server.
arXiv Detail & Related papers (2023-02-05T10:29:57Z) - Automatic Data Augmentation via Invariance-Constrained Learning [94.27081585149836]
Underlying data structures are often exploited to improve the solution of learning tasks.
Data augmentation induces these symmetries during training by applying multiple transformations to the input data.
This work tackles these issues by automatically adapting the data augmentation while solving the learning task.
arXiv Detail & Related papers (2022-09-29T18:11:01Z) - Modeling Dynamic User Preference via Dictionary Learning for Sequential
Recommendation [133.8758914874593]
Capturing the dynamics in user preference is crucial to better predict user future behaviors because user preferences often drift over time.
Many existing recommendation algorithms -- including both shallow and deep ones -- often model such dynamics independently.
This paper considers the problem of embedding a user's sequential behavior into the latent space of user preferences.
arXiv Detail & Related papers (2022-04-02T03:23:46Z) - 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) - A Deep Learning Technique using Low Sampling rate for residential Non
Intrusive Load Monitoring [0.19662978733004596]
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.
arXiv Detail & Related papers (2021-11-07T23:01:36Z) - Avoiding Occupancy Detection from Smart Meter using Adversarial Machine
Learning [0.7106986689736826]
We introduce an Adversarial Machine Learning Occupancy Detection Avoidance (AMLODA) framework as a counter attack.
Essentially, the proposed privacy-preserving framework is designed to mask real-time or near real-time electricity usage information.
Our results show that the proposed privacy-aware billing technique upholds users' privacy strongly.
arXiv Detail & Related papers (2020-10-23T20:02:48Z) - Reshaping consumption habits by exploiting energy-related micro-moment
recommendations: A case study [2.741120981602367]
This work builds on the detection of repeated usage patterns from consumption logs.
It presents the structure and operation of an energy consumption reduction system, which employs a set of sensors, smart-meters and actuators.
The system recommends to the user the proper energy saving action at the right moment and gradually shapes user's habits.
arXiv Detail & Related papers (2020-10-09T17:29:56Z) - Improving time use measurement with personal big data collection -- the
experience of the European Big Data Hackathon 2019 [62.997667081978825]
This article assesses the experience with i-Log at the European Big Data Hackathon 2019, a satellite event of the New Techniques and Technologies for Statistics (NTTS) conference, organised by Eurostat.
i-Log is a system that allows to capture personal big data from smartphones' internal sensors to be used for time use measurement.
arXiv Detail & Related papers (2020-04-24T18:40:08Z) - Energy Disaggregation with Semi-supervised Sparse Coding [0.0]
Energy disaggregation research aims to decompose the aggregated energy consumption data into its component appliances.
In this paper, a discriminative disaggregation model based on sparse coding has been evaluated on large-scale household power usage dataset for energy conservation.
arXiv Detail & Related papers (2020-04-20T21:05:25Z)
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.