scholarly journals IMPEC: An Integrated System for Monitoring and Processing Electricity Consumption in Buildings

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1048 ◽  
Author(s):  
Mohamed Aymane Ahajjam ◽  
Daniel Bonilla Licea ◽  
Mounir Ghogho ◽  
Abdellatif Kobbane
Keyword(s):  
Power Consumption ◽  
Data Acquisition ◽  
Electricity Consumption ◽  
Integrated System ◽  
Load Prediction ◽  
Integrated Monitoring ◽  
Prediction Algorithms ◽  
Load Monitoring ◽  
Industrial Settings ◽  
Validation Tests

Non-intrusive Load Monitoring (NILM) systems aim at identifying and monitoring the power consumption of individual appliances using the aggregate electricity consumption. Many issues hinder their development. For example, due to the complexity of data acquisition and labeling, datasets are scarce; labeled datasets are essential for developing disaggregation and load prediction algorithms. In this paper, we introduce a new NILM system, called Integrated Monitoring and Processing Electricity Consumption (IMPEC). The main characteristics of the proposed system are flexibility, compactness, modularity, and advanced on-board processing capabilities. Both hardware and software parts of the system are described, along with several validation tests performed at residential and industrial settings.

scholarly journals Nonintrusive Load Monitoring Based on Advanced Deep Learning and Novel Signature

2017 ◽  
Vol 2017 ◽  
pp. 1-22 ◽  
Author(s):  
Jihyun Kim ◽  
Thi-Thu-Huong Le ◽  
Howon Kim
Keyword(s):  
Deep Learning ◽  
Power Consumption ◽  
Distribution System ◽  
Short Term Memory ◽  
Electrical Power ◽  
Electricity Consumption ◽  
Classification Performance ◽  
Robust Solution ◽  
Proposed Model ◽  
Load Monitoring

Monitoring electricity consumption in the home is an important way to help reduce energy usage. Nonintrusive Load Monitoring (NILM) is existing technique which helps us monitor electricity consumption effectively and costly. NILM is a promising approach to obtain estimates of the electrical power consumption of individual appliances from aggregate measurements of voltage and/or current in the distribution system. Among the previous studies, Hidden Markov Model (HMM) based models have been studied very much. However, increasing appliances, multistate of appliances, and similar power consumption of appliances are three big issues in NILM recently. In this paper, we address these problems through providing our contributions as follows. First, we proposed state-of-the-art energy disaggregation based on Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) model and additional advanced deep learning. Second, we proposed a novel signature to improve classification performance of the proposed model in multistate appliance case. We applied the proposed model on two datasets such as UK-DALE and REDD. Via our experimental results, we have confirmed that our model outperforms the advanced model. Thus, we show that our combination between advanced deep learning and novel signature can be a robust solution to overcome NILM’s issues and improve the performance of load identification.

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.

scholarly journals NILM techniques applied to a real-time monitoring system of the electricity consumption

ACTA IMEKO ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 139
Author(s):  
Barbara Cannas ◽  
Sara Carcangiu ◽  
Daniele Carta ◽  
Alessandra Fanni ◽  
Carlo Muscas ◽  
...  
Keyword(s):  
Power Consumption ◽  
Monitoring System ◽  
Event Detection ◽  
Electricity Consumption ◽  
Low Frequency ◽  
Detection Algorithm ◽  
Detection Approach ◽  
Consumption Data ◽  
Load Monitoring ◽  
Using Data

Non-Intrusive Load Monitoring (NILM) allows providing appliance-level electricity consumption information and decomposing the overall power consumption by using simple hardware (one sensor) with a suitable software. This paper presents a low-frequency NILM-based monitoring system suitable for a typical house. The proposed solution is a hybrid event-detection approach including an event-detection algorithm for devices with a finite number of states and an auxiliary algorithm for appliances characterized by complex patterns. The system was developed using data collected at households in Italy and tested also with data from BLUED, a widely used dataset of real-world power consumption data. Results show that the proposed approach works well in detecting and classifying what appliance is working and its consumption in complex household load dataset.

Seaway Load Prediction Algorithms for High-Speed Hull Forms

2004 ◽  
Author(s):  
Jerome P. Sikora ◽  
Nathan B. Klontz
Keyword(s):  
High Speed ◽  
Load Prediction ◽  
Prediction Algorithms ◽  
Hull Forms

scholarly journals Energetic Aspects of Turfgrass Mowing: Comparison of Different Rotary Mowing Systems

Agriculture ◽  
2019 ◽  
Vol 9 (8) ◽  
pp. 178
Author(s):  
Michel Pirchio ◽  
Marco Fontanelli ◽  
Fabio Labanca ◽  
Mino Sportelli ◽  
Christian Frasconi ◽  
...  
Keyword(s):  
Power Consumption ◽  
Tall Fescue ◽  
Energy Requirement ◽  
Electricity Consumption ◽  
Primary Energy ◽  
Electric Motors ◽  
Full Throttle ◽  
Nitrogen Rates ◽  
Over Time ◽  
Primary Energy Requirement

Turfgrass mowing is one of the most important operations concerning turfgrass maintenance. Over time, different mowing machines have been developed, such as reel mowers, rotary mowers, and flail mowers. Rotary mowers have become the most widespread mowers for their great versatility and easy maintenance. Modern rotary mowers can be equipped with battery-powered electric motors and precise settings, such as blade rpm. The aim of this trial was to evaluate the differences in power consumption of a gasoline-powered rotary mower and a battery-powered rotary mower. Each mower worked on two different turfgrass species (bermudagrass and tall fescue) fertilized with two different nitrogen rates (100 and 200 kg ha−1). The battery-powered mower was set at its lowest and highest blade rpm value, while the gasoline-powered mower was set at full throttle. From the data acquired, it was possible to see that the gasoline-powered mower had a much higher primary energy requirement, independent of the turf species. Moreover, comparing the electricity consumption of the battery-powered mower over time, it was possible to see that the power consumption varied according to the growth rate of both turf species. These results show that there is a partial waste of energy when using a gasoline-powered mower compared to a battery-powered mower.

scholarly journals High speed multi-channel data acquisition technique for efficient hardware utilization using quad data rate approach

2018 ◽  
Vol 7 (4) ◽  
pp. 2569
Author(s):  
Priyanka Chauhan ◽  
Dippal Israni ◽  
Karan Jasani ◽  
Ashwin Makwana
Keyword(s):  
Power Consumption ◽  
Data Acquisition ◽  
Resource Utilization ◽  
Field Programmable Gate Array ◽  
High Speed ◽  
State Of The Art ◽  
Data Rate ◽  
Field Programmable ◽  
Sensor Signals ◽  
Acquisition Technique

Data acquisition is the most demanding application for the acquisition and monitoring of various sensor signals. The data received are processed in real-time environment. This paper proposes a novel Data Acquisition (DAQ) technique for better resource utilization with less power consumption. Present work has designed and compared advanced Quad Data Rate (QDR) technique with traditional Dual Data Rate (DDR) technique in terms of resource utilization and power consumption of Field Programmable Gate Array (FPGA) hardware. Xilinx ISE is used to verify results of FPGA resource utilization by QDR with state of the art DDR approach. The paper ratiocinates that QDR technique outperforms traditional DDR technique in terms of FPGA resource utilization.  

scholarly journals Development of a new seismic-data acquisition station based on system-on-a-programmable-chip technology

2013 ◽  
Vol 56 (3) ◽  
Author(s):  
Zhang Qi-sheng ◽  
Deng Ming ◽  
Guo Jian ◽  
Luo Wei-bing ◽  
Wang Qi ◽  
...  
Keyword(s):  
Power Consumption ◽  
Data Acquisition ◽  
Digital Filter ◽  
Data Transmission ◽  
Seismic Data ◽  
Low Noise ◽  
High Definition ◽  
Control Data ◽  
Seismic Data Acquisition ◽  
Transmission Speed

<p>There has been considerable development of seismic detectors over the last 80 years. However, there is still a need to further develop new earthquake exploration and data acquisition systems with high precision. In particular, for China to keep up with the latest technology of these systems, it is important to be involved in the research and development, instead of importing systems that soon fall behind the latest technology. In this study, the features of system-on-a-programmable-chip (SoPC) technology are analyzed and used to design a new digital seismic-data acquisition station. The hardware circuit of the station was developed, and the analog board and the main control data-transmission board were designed according to the needs of digital seismic-data acquisition stations. High-definition analog-to-digital converter sequential digital filter technology of the station (cascade integrator comb filter, finite impulse response digital filter) were incorporated to provide advantages to the acquisition station, such as high definition, large dynamic scope, and low noise. A specific data-transmission protocol was designed for the station, which ensured a transmission speed of 16 Mbps along a 55-m wire with low power consumption. Synchronic acquisition was researched and developed, so as to achieve accuracy better than 200 ns. The key technologies were integrated into the SoPC of the main control data-transmission board, so as to ensure high-resolution acquisition of the station, while improving the accuracy of the synchronic acquisition and data-transmission speed, lowering the power consumption, and preparing for the follow-up efforts to tape out.</p>

scholarly journals Smart grids, prosumers and energy management within a smart city integrated system

2020 ◽  
Vol 14 (1) ◽  
pp. 1121-1134
Author(s):  
Marco Savastano ◽  
Marta-Christina Suciu ◽  
Irina Gorelova ◽  
Gheorghe-Alexandru Stativă
Keyword(s):  
Smart Grids ◽  
Smart Cities ◽  
Electricity Consumption ◽  
Energy Transition ◽  
Integrated System ◽  
Renewable Sources ◽  
Electricity Grid ◽  
New Type ◽  
Alternative Sources ◽  
Energy Prosumers

AbstractDue to a significant increase in electricity consumption globally, governments have to look and to identify better, more efficient and effective alternatives and sustainable energy sources to meet this high demand. This becomes more and more important in the context of implementing modern approaches such as those that might be applied in cases of smart cities and cultural and creative communities. Electricity can be produced based on conventional sources, but also on an emergent use of renewable sources. The electricity grid is usually designed as unidirectional. We consider that in case of smart cities and creative-innovative communities there is a need to implement mostly new smart grids that are bidirectional. This may allow and support the emergency of a new type of electricity user, called “prosumers”, who produces electricity from renewable sources, next uses & shares them smartly within the smart grid and finally stores them. Globally, photovoltaic energy prosumers are considered one of the most important actors in the energy transition and seem to be ready to introduce significant amounts of electricity within the grid. We anticipate that people living in households in smart cities and communities among most regions of the world will tend in the future to improve their self-consumption from the production of smart energy. This paper supports the idea that using mostly electricity from renewable alternative sources, especially solar, can be also developed with the help of households acting within smart cities and communities. The paper will also present briefly an overview of the scientific literature dedicated to this topic. We will also provide further interesting insights through a number of case studies representing good practices regarding prosumers in Italy and Romania.

Development of a Latching Valve for Micro-Chem-Lab™

1999 ◽  
Author(s):  
C. Channy Wong ◽  
Douglas R. Adkins ◽  
Ronald P. Manginell ◽  
Gregory C. Frye-Mason ◽  
Peter J. Hesketh ◽  
...  
Keyword(s):  
Power Consumption ◽  
Gas Phase ◽  
Flow Rate ◽  
Integrated System ◽  
Dead Volume ◽  
Design Parameters ◽  
Working Fluid ◽  
Driving Voltage ◽  
Magnetic Actuation ◽  
Phase Detection

Abstract An integrated microsystem to detect traces of chemical agents (μChemLab™) is being developed at Sandia for counter-terrorism and nonproliferation applications. This microsystem has two modes of operation: liquid and gas phase detection. For the gas phase detection, we are integrating these critical components: a preconcentrator for sample collection, a gas chromatographic (GC) separator, a chemically selective flexural plate wave (FPW) array mass detector, and a latching valve onto a single chip. By fabricating these components onto a single integrated system (μChemLab™ on a chip), the advantages of reduced dead volume, lower power consumption, and smaller physical size can be realized. In this paper, the development of a latching valve will be presented. The key design parameters for this latching valve are: a volumetric flow rate of 1 mL/min, a maximum hold-off pressure of 40 kPa (6 psi), a relatively low power, and a fast response time. These requirements have led to the design of a magnetically actuated latching relay diaphragm valve. Magnetic actuation is chosen because it can achieve sufficient force to effectively seal against back pressure and its power consumption is relatively low. The actuation time is rapid, and valve can latch in either an open or closed state. A corrugated parylene membrane is used to separate the working fluid from internal components of the valve. Corrugations in the parylene ensure that the diaphragm presents minimum resistance to the actuator for a relativley large deflection. Two different designs and their performance of the magnetic actuation have been evaluated. The first uses a linear magnetic drive mechanism, and the second uses a relay mechanism. Preliminary results of the valve performance indicates that the required driving voltage is about 10 volts, the measured flow rate is about 50 mL/min, and it can hold off pressure of about 5 psi (34 kPa). Latest modifications of the design show excellent performance improvements.

scholarly journals A Deep Recurrent Neural Network for Non-Intrusive Load Monitoring Based on Multi-Feature Input Space and Post-Processing

Energies ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 2195
Author(s):  
Hasan Rafiq ◽  
Xiaohan Shi ◽  
Hengxu Zhang ◽  
Huimin Li ◽  
Manesh Kumar Ochani
Keyword(s):  
Neural Network ◽  
Power Consumption ◽  
Real Time ◽  
Recurrent Neural Network ◽  
Short Term Memory ◽  
Estimation Accuracy ◽  
Post Processing ◽  
Input Space ◽  
Deep Recurrent Neural Network ◽  
Load Monitoring

Non-intrusive load monitoring (NILM) is a process of estimating operational states and power consumption of individual appliances, which if implemented in real-time, can provide actionable feedback in terms of energy usage and personalized recommendations to consumers. Intelligent disaggregation algorithms such as deep neural networks can fulfill this objective if they possess high estimation accuracy and lowest generalization error. In order to achieve these two goals, this paper presents a disaggregation algorithm based on a deep recurrent neural network using multi-feature input space and post-processing. First, the mutual information method was used to select electrical parameters that had the most influence on the power consumption of each target appliance. Second, selected steady-state parameters based multi-feature input space (MFS) was used to train the 4-layered bidirectional long short-term memory (LSTM) model for each target appliance. Finally, a post-processing technique was used at the disaggregation stage to eliminate irrelevant predicted sequences, enhancing the classification and estimation accuracy of the algorithm. A comprehensive evaluation was conducted on 1-Hz sampled UKDALE and ECO datasets in a noised scenario with seen and unseen test cases. Performance evaluation showed that the MFS-LSTM algorithm is computationally efficient, scalable, and possesses better estimation accuracy in a noised scenario, and generalized to unseen loads as compared to benchmark algorithms. Presented results proved that the proposed algorithm fulfills practical application requirements and can be deployed in real-time.

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