scholarly journals An optimisation-based energy disaggregation algorithm for low frequency smart meter data

2019 ◽  
Vol 2 (S1) ◽  
Author(s):  
Cristina Rottondi ◽  
Marco Derboni ◽  
Dario Piga ◽  
Andrea Emilio Rizzoli
Keyword(s):  
Energy Consumption ◽  
State Of The Art ◽  
Low Frequency ◽  
Smart Meter ◽  
Washing Machine ◽  
Total Energy Consumption ◽  
Energy Disaggregation ◽  
Load Monitoring ◽  
Individual Contributions ◽  
The Individual

Abstract An algorithm for the non-intrusive disaggregation of energy consumption into its end-uses, also known as non-intrusive appliance load monitoring (NIALM), is presented. The algorithm solves an optimisation problem where the objective is to minimise the error between the total energy consumption and the sum of the individual contributions of each appliance. The algorithm assumes that a fraction of the loads present in the household is known (e.g. washing machine, dishwasher, etc.), but it also considers unknown loads, treating them as a single load. The performance of the algorithm is then compared to that obtained by two state of the art disaggregation approaches implemented in the publicly available NILMTK framework. The first one is based on Combinatorial Optimization, the second one on a Factorial Hidden Markov Model. The results show that the proposed algorithm performs satisfactorily and it even outperforms the other algorithms from some perspectives.

scholarly journals Nature-Based Thermal Insulation Materials From Renewable Resources – A State-Of-The-Art Review

2019 ◽  
Vol 27 (1) ◽  
pp. 52-59 ◽  
Author(s):  
David Bozsaky
Keyword(s):  
Energy Consumption ◽  
Thermal Insulation ◽  
Renewable Resources ◽  
State Of The Art ◽  
Global Climate ◽  
Total Energy Consumption ◽  
Insulation Materials ◽  
Thermal Insulating ◽  
High Level ◽  
By Products

Abstract In the 21st century, global climate change and the high level of fossil energy consumption have introduced changes affecting all sectors of the economy, including the building industry. Reducing energy consumption has become an important task for engineers because 30% of the total energy consumption is used for heating our buildings. Recycling the huge amount of industrial and agricultural by-products has also become urgent because due to their CO2 emissions, their combustion is not a state-of-the-art alternative. Besides rediscovering some long-known, nature-based insulating materials, there are also several research projects that have resulted in new products. In the last century it was relatively easy to review this product range, but nowadays there are so many kinds of nature-based thermal insulating products, there is a need for systematization, and more in-depth knowledge about them is required. The purpose of this paper is to develop a new systematization of nature-based thermal insulation materials, summarize the main knowledge about them, and indicate the direction of recent research and development.

scholarly journals Improving Non-Intrusive Load Disaggregation through an Attention-Based Deep Neural Network

Energies ◽  
2021 ◽  
Vol 14 (4) ◽  
pp. 847
Author(s):  
Veronica Piccialli ◽  
Antonio M. Sudoso
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Attention Mechanism ◽  
Power Demand ◽  
Generalization Capability ◽  
Experimental Conditions ◽  
Energy Disaggregation ◽  
Load Monitoring ◽  
High Power Consumption ◽  
The Individual

Energy disaggregation, known in the literature as Non-Intrusive Load Monitoring (NILM), is the task of inferring the power demand of the individual appliances given the aggregate power demand recorded by a single smart meter which monitors multiple appliances. In this paper, we propose a deep neural network that combines a regression subnetwork with a classification subnetwork for solving the NILM problem. Specifically, we improve the generalization capability of the overall architecture by including an encoder–decoder with a tailored attention mechanism in the regression subnetwork. The attention mechanism is inspired by the temporal attention that has been successfully applied in neural machine translation, text summarization, and speech recognition. The experiments conducted on two publicly available datasets—REDD and UK-DALE—show that our proposed deep neural network outperforms the state-of-the-art in all the considered experimental conditions. We also show that modeling attention translates into the network’s ability to correctly detect the turning on or off an appliance and to locate signal sections with high power consumption, which are of extreme interest in the field of energy disaggregation.

scholarly journals Identification of TV Channel Watching from Smart Meter Data Using Energy Disaggregation

Energies ◽  
2021 ◽  
Vol 14 (9) ◽  
pp. 2485
Author(s):  
Pascal A. Schirmer ◽  
Iosif Mporas ◽  
Akbar Sheikh-Akbari
Keyword(s):  
Energy Consumption ◽  
Identification Accuracy ◽  
Distance Measures ◽  
Smart Meter ◽  
Video Content ◽  
Smart Meters ◽  
Energy Disaggregation ◽  
Content Identification ◽  
Energy Signal ◽  
Monitor Device

Smart meters are used to measure the energy consumption of households. Specifically, within the energy consumption task, a smart meter must be used for load forecasting, the reduction in consumer bills as well as the reduction in grid distortions. Smart meters can be used to disaggregate the energy consumption at the device level. In this paper, we investigated the potential of identifying the multimedia content played by a TV or monitor device using the central house’s smart meter measuring the aggregated energy consumption from all working appliances of the household. The proposed architecture was based on the elastic matching of aggregated energy signal frames with 20 reference TV channel signals. Different elastic matching algorithms, which use symmetric distance measures, were used with the best achieved video content identification accuracy of 93.6% using the MVM algorithm.

scholarly journals Energy Disaggregation Using Principal Component Analysis Representation

2020 ◽  
Author(s):  
Pierre V. Dantas ◽  
Waldir Sabino S. Júnior ◽  
Celso B. Carvalho
Keyword(s):  
Principal Component Analysis ◽  
Energy Consumption ◽  
Smart Grids ◽  
Care Home ◽  
Principal Component ◽  
Audio Signals ◽  
Ubiquitous Systems ◽  
Energy Disaggregation ◽  
The Individual ◽  
Health Care Home

The main purpose of disaggregation is to decompose a signal into a set of other signals that together constitute it. This approach could be applied to audio signals, health care, home automation, ubiquitous systems and energy systems. It may be unworkable to individually measure the energy consumption of loads in a system simultaneously and, through disaggregation, we can make an inference using a main meter. The main contribution of this work is to use PCA to extract representativeness of an energy consumption signal we want to disaggregate, identifying its most relevant characteristics. The field of study is relevant because it allows information to be obtained in a simpler and cheaper way about the individual consumption of loads that make up a system. This opens up perspectives for other approaches such as smart grids and IoT. We demonstrate that when compared to other techniques, the proposal produces more accurate disaggregation results.

scholarly journals Developments in tyre design for lower rolling resistance: A state of the art review

2017 ◽  
Vol 232 (14) ◽  
pp. 1865-1882 ◽  
Author(s):  
Hamad Sarhan Aldhufairi ◽  
Oluremi Ayotunde Olatunbosun
Keyword(s):  
Energy Consumption ◽  
Fuel Consumption ◽  
Design Research ◽  
New Technologies ◽  
State Of The Art ◽  
Primary Source ◽  
Rolling Resistance ◽  
Road Transportation ◽  
Total Energy Consumption ◽  
Total Vehicle

Future sustainability of road transportation will require substantial improvement in the efficient use of energy by road vehicles. As new technologies being deployed reduce total vehicle energy consumption, the contribution of tyre rolling resistance to total energy consumption continues to increase. For this reason tyre rolling-resistance is starting to drive the focus of many tyre developments nowadays. This is because the rolling-resistance can be responsible for 20–30% of the total vehicle fuel consumption. Thus, lowering the rolling-resistance would help to reduce the fuel consumption (i.e. CO2, NOx and hydrocarbon emissions) and hence improve the environment greatly given the large number of vehicles used globally. It is found that the primary source of the rolling-resistance is the tyre deformational behaviour (i.e. hysteresis damping) which can account for 80–95% of the total rolling-resistance. This paper reviews the state of the art in tyre design, research and development for lower rolling-resistance, with focus on the primary source for the rolling-resistance (i.e. mechanical hysteresis damping), from three perspectives: the structural lay-up; the dimensional features; and the materials compound(s) of the tyre.

scholarly journals A Fusion Load Disaggregation Method Based on Clustering Algorithm and Support Vector Regression Optimization for Low Sampling Data

2019 ◽  
Vol 11 (2) ◽  
pp. 51 ◽  
Author(s):  
Quanbo Yuan ◽  
Huijuan Wang ◽  
Botao Wu ◽  
Yaodong Song ◽  
Hejia Wang
Keyword(s):  
Energy Consumption ◽  
Support Vector Regression ◽  
Clustering Algorithm ◽  
Low Frequency ◽  
Total Power ◽  
Support Vector ◽  
Smart Meters ◽  
Load Monitoring ◽  
Load Disaggregation ◽  
Economic Technology

In order to achieve more efficient energy consumption, it is crucial that accurate detailed information is given on how power is consumed. Electricity details benefit both market utilities and also power consumers. Non-intrusive load monitoring (NILM), a novel and economic technology, obtains single-appliance power consumption through a single total power meter. This paper, focusing on load disaggregation with low hardware costs, proposed a load disaggregation method for low sampling data from smart meters based on a clustering algorithm and support vector regression optimization. This approach combines the k-median algorithm and dynamic time warping to identify the operating appliance and retrieves single energy consumption from an aggregate smart meter signal via optimized support vector regression (OSVR). Experiments showed that the technique can recognize multiple devices switching on at the same time using low-frequency data and achieve a high load disaggregation performance. The proposed method employs low sampling data acquired by smart meters without installing extra measurement equipment, which lowers hardware cost and is suitable for applications in smart grid environments.

scholarly journals Explaining daily total energy demand in British housing using linked smart meter and socio-technical data in a bottom-up statistical model

2021 ◽  
Author(s):  
Eoghan James McKenna ◽  
Jessica Few ◽  
Ellen Webborn ◽  
Ben Anderson ◽  
Simon Elam ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Total Energy ◽  
Energy Demand ◽  
Mean Squared Error ◽  
Heating Temperature ◽  
P Value ◽  
Smart Meter ◽  
Total Energy Consumption ◽  
Technical Data ◽  
Daily Total

This paper investigates factors associated with variation in daily total energy consumption in domestic buildings using linked pre-COVID-19 smart meter, weather, building thermal characteristics, and socio-technical survey data covering appliance ownership, demographics, behaviours, and attitudes for a sub-sample of 617 British households selected from the Smart Energy Research Laboratory (SERL) Observatory panel.Linear mixed effects modelling resulted in marginal/conditional R2 of 0.68/0.83 and root mean squared error of 17.7 kWh/day, for daily gas and electricity use combined. Increased daily energy consumption was significantly associated (p-value<0.05) with: households living in buildings with larger floor area, more rooms, that are older, have lower energy efficiency, and experience colder or less sunny weather; households with more adult occupants, more children, older adult occupants, fewer adults with qualifications, higher heating temperature setpoints, that do not try to save energy, and that do not put on more clothes rather than turning the heating on. The results demonstrate the value of smart meter data linked with contextual data for improving understanding of energy demand in British housing. Accredited UK researchers are invited to apply to access the data which has recently been updated to include over 13,000 households from across Great Britain.

scholarly journals NILM-based approach for energy efficiency assessment of household appliances

2020 ◽  
Vol 3 (1) ◽  
Author(s):  
Fernando D. Garcia ◽  
Wesley A. Souza ◽  
Ivando S. Diniz ◽  
Fernando P. Marafão
Keyword(s):  
Energy Efficiency ◽  
Microscopic Analysis ◽  
Energy Usage ◽  
Household Appliances ◽  
Efficiency Assessment ◽  
Nearest Neighbours ◽  
Energy Disaggregation ◽  
Load Monitoring ◽  
The Individual ◽  
Operational Issues

Abstract This paper presents a novel Non-Intrusive Load Monitoring (NILM) approach focusing on the Energy Efficiency (EE) assessment of residential appliances. This approach (NILMEE) is able to identify the individual consumption of several household devices, providing proper information for evaluating energy efficiency and pointing out the operational issues or labelling mismatches of appliances, while recommending better practices for energy usage in specific consumer installations. The proposed approach was developed and evaluated by embedding the NILM engine on an electronic power meter, which performs a microscopic analysis on measured voltages and currents and provides the load disaggregation using the Conservative Power Theory for the feature extraction, K-Nearest Neighbours for the appliance classification, and the Power Signature Blob for the energy disaggregation. The disaggregation algorithm performance evaluation is carried out using NILMTK. Results show that NILM transcends the regular energy usage calculation, serving as a tool that enables the diagnosis of household appliances using the energy efficiency indexes provided by labels and standards.

Geographical aspects of the diffusion of passive houses

2013 ◽  
Vol 4 (2) ◽  
pp. 151-156 ◽  
Author(s):  
G. Kozma ◽  
E. Molnár ◽  
K. Czimre ◽  
J. Pénzes
Keyword(s):  
Energy Consumption ◽  
Energy Resources ◽  
Substantial Part ◽  
Renewable Energy Resources ◽  
Passive House ◽  
The Earth ◽  
Passive Houses ◽  
The Individual ◽  
Geographical Locations ◽  
Over Time

Abstract In our days, energy issues belong to the most important problems facing the Earth and the solution may be expected partly from decreasing the amount of the energy used and partly from the increased utilisation of renewable energy resources. A substantial part of energy consumption is related to buildings and includes, inter alia, the use for cooling/heating, lighting and cooking purposes. In the view of the above, special attention has been paid to minimising the energy consumption of buildings since the late 1980s. Within the framework of that, the passive house was created, a building in which the thermal comfort can be achieved solely by postheating or postcooling of the fresh air mass without a need for recirculated air. The aim of the paper is to study the changes in the construction of passive houses over time. In addition, the differences between the geographical locations and the observable peculiarities with regard to the individual building types are also presented.

SMART METER CONFIGURATION FOR BETTER ENERGY CONSUMPTION WITH FAULT DETECTION

2019 ◽  
Vol 7 (1) ◽  
pp. 12
Author(s):  
PARAMANIK SAYAN ◽  
KUSHARY INDRANIL ◽  
SARKER KRISHNA ◽  
◽  
◽  
...  
Keyword(s):  
Energy Consumption ◽  
Fault Detection ◽  
Smart Meter
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