scholarly journals Subtask Gated Networks for Non-Intrusive Load Monitoring

2019 ◽  
Vol 33 ◽  
pp. 1150-1157 ◽  
Author(s):  
Changho Shin ◽  
Sunghwan Joo ◽  
Jaeryun Yim ◽  
Hyoseop Lee ◽  
Taesup Moon ◽  
...  
Keyword(s):  
Electricity Consumption ◽  
Seminal Paper ◽  
Performance Improvements ◽  
Energy Disaggregation ◽  
Load Monitoring ◽  
Significant Performance ◽  
Standby Power ◽  
Measurement Devices ◽  
Classification Probability ◽  
The Cost

Non-intrusive load monitoring (NILM), also known as energy disaggregation, is a blind source separation problem where a household’s aggregate electricity consumption is broken down into electricity usages of individual appliances. In this way, the cost and trouble of installing many measurement devices over numerous household appliances can be avoided, and only one device needs to be installed. The problem has been well-known since Hart’s seminal paper in 1992, and recently significant performance improvements have been achieved by adopting deep networks. In this work, we focus on the idea that appliances have on/off states, and develop a deep network for further performance improvements. Specifically, we propose a subtask gated network that combines the main regression network with an on/off classification subtask network. Unlike typical multitask learning algorithms where multiple tasks simply share the network parameters to take advantage of the relevance among tasks, the subtask gated network multiply the main network’s regression output with the subtask’s classification probability. When standby-power is additionally learned, the proposed solution surpasses the state-of-the-art performance for most of the benchmark cases. The subtask gated network can be very effective for any problem that inherently has on/off states.

scholarly journals Energy Disaggregation Using Two-Stage Fusion of Binary Device Detectors

Energies ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 2148 ◽  
Author(s):  
Pascal A. Schirmer ◽  
Iosif Mporas ◽  
Akbar Sheikh-Akbari
Keyword(s):  
Power Consumption ◽  
Electricity Consumption ◽  
Estimation Accuracy ◽  
Two Stage ◽  
Data Set ◽  
Second Stage ◽  
Energy Disaggregation ◽  
Stage Classification ◽  
Load Monitoring ◽  
Detection Score

A data-driven methodology to improve the energy disaggregation accuracy during Non-Intrusive Load Monitoring is proposed. In detail, the method uses a two-stage classification scheme, with the first stage consisting of classification models processing the aggregated signal in parallel and each of them producing a binary device detection score, and the second stage consisting of fusion regression models for estimating the power consumption for each of the electrical appliances. The accuracy of the proposed approach was tested on three datasets—ECO (Electricity Consumption & Occupancy), REDD (Reference Energy Disaggregation Data Set), and iAWE (Indian Dataset for Ambient Water and Energy)—which are available online, using four different classifiers. The presented approach improves the estimation accuracy by up to 4.1% with respect to a basic energy disaggregation architecture, while the improvement on device level was up to 10.1%. Analysis on device level showed significant improvement of power consumption estimation accuracy especially for continuous and nonlinear appliances across all evaluated datasets.

A McKibben Type Sleeve Pneumatic Muscle and Integrated Mechanism for Improved Stroke Length

2017 ◽  
Vol 9 (1) ◽  
Author(s):  
Michael F. Cullinan ◽  
Eamonn Bourke ◽  
Kevin Kelly ◽  
Conor McGinn
Keyword(s):  
Energy Efficiency ◽  
Artificial Muscle ◽  
Force Output ◽  
Stroke Length ◽  
Pneumatic Muscle ◽  
Performance Improvements ◽  
Pneumatic Muscles ◽  
Significant Performance ◽  
Air Muscle ◽  
The Cost

Sleeve pneumatic muscles have shown significant performance improvements over conventional air muscle design, offering increased energy efficiency, force output, and stroke length, while allowing the actuator to become a structural component. However, there remain comparatively few studies involving sleeve muscles, and current applications have not focused on their potential advantages for joints actuated antagonistically with two muscles or their application to a more general class of pneumatic artificial muscle. This research presents a modular sleeve muscle design using the McKibben type construction, with a separate membrane and braid. To further increase stroke length, an internal pulley mechanism is implemented. The performance of the sleeve muscle is compared to an equivalent unaltered muscle and shows substantial improvements in force output, stroke length, and energy efficiency. Further testing shows that the internal pulley mechanism increased the effective stroke length by 82%, albeit at the cost of reduced maximum force output.

scholarly journals A Low Rate Energy Disaggregation using Non-Intrusive Load Monitoring

2019 ◽  
Vol 8 (11) ◽  
pp. 4204-4209
Keyword(s):  
Energy Conservation ◽  
Low Cost ◽  
Sampling Rate ◽  
Energy Usage ◽  
Energy Meter ◽  
High Sampling Rate ◽  
Energy Disaggregation ◽  
Load Monitoring ◽  
The Cost ◽  
Conservation And Management

Nowadays, Energy conservation and management are a must practice due to the exponentially increasing energy usage. One solution for providing for energy conservation is appliance load monitoring. Load monitoring approach should be simple and of low cost in order to be massively deployable. Non-Intrusive load monitoring is a better approach since it can disaggregate energy at the cost of single energy meter. A low sampling rate energy meter incurs low cost compared to a high sampling rate energy meter. In this paper a less complex, low cost energy disaggregation approach has been proposed

TCAD Modeling of Strain-Engineered MOSFETs

2006 ◽  
Vol 913 ◽  
Author(s):  
Lee Smith
Keyword(s):  
Carrier Transport ◽  
Device Simulation ◽  
Rapid Rise ◽  
Performance Improvements ◽  
Significant Performance ◽  
Engineer Strain ◽  
Standby Power ◽  
The Common ◽  
Nanoscale Mosfets ◽  
Tcad Simulations

AbstractThe rapid rise of standby power in nanoscale MOSFETs is slowing classical scaling and threatening to derail continued improvements in MOSFET performance. Strain-enhancement of carrier transport in the MOSFET channel has emerged as a particularly effective approach to enable significant performance improvements at similar off-state leakage. In this paper we describe how strain effects are modeled within the context of TCAD process and device simulation. We also use TCAD simulations to review some of the common approaches to engineer strain in MOSFETs and to explain how strain impacts device and circuit characteristics.

scholarly journals MC-NILM: A Multi-Chain Disaggregation Method for NILM

Energies ◽  
2021 ◽  
Vol 14 (14) ◽  
pp. 4331
Author(s):  
Hao Ma ◽  
Juncheng Jia ◽  
Xinhao Yang ◽  
Weipeng Zhu ◽  
Hong Zhang
Keyword(s):  
Time Complexity ◽  
Electricity Consumption ◽  
High Time ◽  
Research Focus ◽  
Energy Disaggregation ◽  
Load Monitoring

Non-intrusive load monitoring (NILM) is an approach that helps residents obtain detailed information about household electricity consumption and has gradually become a research focus in recent years. Most of the existing algorithms on NILM build energy disaggregation models independently for an individual appliance while neglecting the relation among them. For this situation, this article proposes a multi-chain disaggregation method for NILM (MC-NILM). MC-NILM integrates the models generated by existing algorithms and considers the relation among these models to improve the performance of energy disaggregation. Given the high time complexity of searching for the optimal MC-NILM structure, this article proposes two methods to reduce the time complexity, the k-length chain method and the graph-based chain generation method. Finally, we use the Dataport and UK-DALE datasets to evaluate the feasibility, effectiveness, and generality of the MC-NILM.

Standby-Energy-Saving Socket

2014 ◽  
Vol 563 ◽  
pp. 356-361
Author(s):  
Yu Chi Wu ◽  
Meng Jen Chen ◽  
Hsien Min Liao ◽  
Bo Huei Yang ◽  
Jing Yuan Lin
Keyword(s):  
Energy Saving ◽  
Electricity Consumption ◽  
Current Sensor ◽  
Sleep Mode ◽  
Idle Mode ◽  
Start Up ◽  
Standby Power ◽  
The Cost ◽  
Microcontroller Unit ◽  
Standby Mode

Most home electric appliances have standby energy waste when they are at idle mode. This standby energy is about 3% to 11% of total household electricity consumption. For several millions of households in Taiwan, this standby energy would cause billion dollars of waste per year. Therefore, in this paper we propose a standby-energy-saving socket using a microcontroller unit (MCU) to reduce the standby-energy waste. The user can start up this standby-energy-saving socket to supply power to appliances through the appliances infrared remote control or the button on the socket. When the appliances enter into the standby mode, the current sensor in the socket automatically detects it and the MCU turns off the power through a relay to reduce the standby power to zero. Based on the test, the MCU in the proposed standby-energy-saving socket only consumes about 3 mW when the socket is at sleep mode, 6 mW at working mode. The cost of this proposed socket is $9. The standby-energy saving tested on a PC is reduced by 99.93%.

scholarly journals Data Requirements for Applying Machine Learning to Energy Disaggregation

Energies ◽  
2019 ◽  
Vol 12 (9) ◽  
pp. 1696 ◽  
Author(s):  
Changho Shin ◽  
Seungeun Rho ◽  
Hyoseop Lee ◽  
Wonjong Rhee
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Sampling Rate ◽  
Electricity Consumption ◽  
Parameter Tuning ◽  
Data Sampling ◽  
Energy Disaggregation ◽  
Load Monitoring ◽  
Data Sampling Rate ◽  
Data Requirements

Energy disaggregation, or nonintrusive load monitoring (NILM), is a technology for separating a household’s aggregate electricity consumption information. Although this technology was developed in 1992, its practical usage and mass deployment have been rather limited, possibly because the commonly used datasets are not adequate for NILM research. In this study, we report the findings from a newly collected dataset that contains 10 Hz sampling data for 58 houses. The dataset not only contains the aggregate measurements, but also individual appliance measurements for three types of appliances. By applying three classification algorithms (vanilla DNN (Deep Neural Network), ML (Machine Learning) with feature engineering, and CNN (Convolutional Neural Network) with hyper-parameter tuning) and a recent regression algorithm (Subtask Gated Network) to the new dataset, we show that NILM performance can be significantly limited when the data sampling rate is too low or when the number of distinct houses in the dataset is too small. The well-known NILM datasets that are popular in the research community do not meet these requirements. Our results indicate that higher quality datasets should be used to expedite the progress of NILM research.

scholarly journals Characteristics of the cost of electricity consumption in agricultural production

2021 ◽  
Vol 677 (3) ◽  
pp. 032087
Author(s):  
G S Kudryashev ◽  
A N Tretyakov ◽  
S V Batishchev ◽  
V A Bochkarev ◽  
V D Ochirov
Keyword(s):  
Agricultural Production ◽  
Electricity Consumption ◽  
Cost Of Electricity ◽  
The Cost

scholarly journals An Attention-Based Multilayer GRU Model for Multistep-Ahead Short-Term Load Forecasting

Sensors ◽  
2021 ◽  
Vol 21 (5) ◽  
pp. 1639
Author(s):  
Seungmin Jung ◽  
Jihoon Moon ◽  
Sungwoo Park ◽  
Eenjun Hwang
Keyword(s):  
Power Consumption ◽  
Prediction Models ◽  
Short Term Memory ◽  
Load Forecasting ◽  
Input Sequence ◽  
Short Term ◽  
Performance Improvements ◽  
Short Term Load Forecasting ◽  
Significant Performance ◽  
Input Variables

Recently, multistep-ahead prediction has attracted much attention in electric load forecasting because it can deal with sudden changes in power consumption caused by various events such as fire and heat wave for a day from the present time. On the other hand, recurrent neural networks (RNNs), including long short-term memory and gated recurrent unit (GRU) networks, can reflect the previous point well to predict the current point. Due to this property, they have been widely used for multistep-ahead prediction. The GRU model is simple and easy to implement; however, its prediction performance is limited because it considers all input variables equally. In this paper, we propose a short-term load forecasting model using an attention based GRU to focus more on the crucial variables and demonstrate that this can achieve significant performance improvements, especially when the input sequence of RNN is long. Through extensive experiments, we show that the proposed model outperforms other recent multistep-ahead prediction models in the building-level power consumption forecasting.

scholarly journals Efficient Design of Energy Disaggregation Model with BERT-NILM Trained by AdaX Optimization Method for Smart Grid

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4649
Author(s):  
İsmail Hakkı ÇAVDAR ◽  
Vahit FERYAD
Keyword(s):  
Smart Grids ◽  
Energy Demand ◽  
Optimization Technique ◽  
Cost Effective ◽  
Optimization Method ◽  
Long Term Memory ◽  
Successful Implementation ◽  
Efficient Design ◽  
Energy Disaggregation ◽  
Load Monitoring

One of the basic conditions for the successful implementation of energy demand-side management (EDM) in smart grids is the monitoring of different loads with an electrical load monitoring system. Energy and sustainability concerns present a multitude of issues that can be addressed using approaches of data mining and machine learning. However, resolving such problems due to the lack of publicly available datasets is cumbersome. In this study, we first designed an efficient energy disaggregation (ED) model and evaluated it on the basis of publicly available benchmark data from the Residential Energy Disaggregation Dataset (REDD), and then we aimed to advance ED research in smart grids using the Turkey Electrical Appliances Dataset (TEAD) containing household electricity usage data. In addition, the TEAD was evaluated using the proposed ED model tested with benchmark REDD data. The Internet of things (IoT) architecture with sensors and Node-Red software installations were established to collect data in the research. In the context of smart metering, a nonintrusive load monitoring (NILM) model was designed to classify household appliances according to TEAD data. A highly accurate supervised ED is introduced, which was designed to raise awareness to customers and generate feedback by demand without the need for smart sensors. It is also cost-effective, maintainable, and easy to install, it does not require much space, and it can be trained to monitor multiple devices. We propose an efficient BERT-NILM tuned by new adaptive gradient descent with exponential long-term memory (Adax), using a deep learning (DL) architecture based on bidirectional encoder representations from transformers (BERT). In this paper, an improved training function was designed specifically for tuning of NILM neural networks. We adapted the Adax optimization technique to the ED field and learned the sequence-to-sequence patterns. With the updated training function, BERT-NILM outperformed state-of-the-art adaptive moment estimation (Adam) optimization across various metrics on REDD datasets; lastly, we evaluated the TEAD dataset using BERT-NILM training.

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