User-Centered Nonintrusive Electricity Load Monitoring for Residential Buildings

2011 ◽  
Vol 25 (6) ◽  
pp. 471-480 ◽  
Author(s):  
Mario Berges ◽  
Ethan Goldman ◽  
H. Scott Matthews ◽  
Lucio Soibelman ◽  
Kyle Anderson
Keyword(s):  
Residential Buildings ◽  
Load Monitoring ◽  
Electricity Load ◽  
User Centered

Time series distance-based methods for non-intrusive load monitoring in residential buildings

2015 ◽  
Vol 96 ◽  
pp. 109-117 ◽  
Author(s):  
Kaustav Basu ◽  
Vincent Debusschere ◽  
Ahlame Douzal-Chouakria ◽  
Seddik Bacha
Keyword(s):  
Time Series ◽  
Residential Buildings ◽  
Load Monitoring

scholarly journals Data on the daily electricity load profile and solar photovoltaic (PV) system components for residential buildings in Lagos, Nigeria

Data in Brief ◽  
2020 ◽  
Vol 30 ◽  
pp. 105531 ◽  
Author(s):  
Kevin Enongene Enongene ◽  
Fonbeyin Henry Abanda ◽  
Iduh Jonathan Joseph Otene ◽  
Sheila Ifeakarochukwu Obi ◽  
Chioma Okafor
Keyword(s):  
Residential Buildings ◽  
Solar Photovoltaic ◽  
Pv System ◽  
Load Profile ◽  
System Components ◽  
Electricity Load

Using Supervised Machine Learning to Explore Energy Consumption Data in Private Sector Housing

2015 ◽  
pp. 320-333 ◽  
Author(s):  
Mariya Sodenkamp ◽  
Konstantin Hopf ◽  
Thorsten Staake
Keyword(s):  
Machine Learning ◽  
Energy Consumption ◽  
Classification Accuracy ◽  
Residential Buildings ◽  
Supervised Machine Learning ◽  
Household Characteristics ◽  
Consumption Data ◽  
Feature Filtering ◽  
Electricity Load ◽  
Class Labels

Smart electricity meters allow capturing consumption load profiles of residential buildings. Besides several other applications, the retrieved data renders it possible to reveal household characteristics including the number of persons per apartment, age of the dwelling, etc., which helps to develop targeted energy conservation services. The goal of this chapter is to develop further related methods of smart meter data analytics that infer such household characteristics using weekly load curves. The contribution of this chapter to the state of the art is threefold. The authors first quadruplicate the number of defined features that describe electricity load curves to preserve relevant structures for classification. Then, they suggest feature filtering techniques to reduce the dimension of the input to a set of a few significant ones. Finally, the authors redefine class labels for some properties. As a result, the classification accuracy is elevated up to 82%, while the runtime complexity is significantly reduced.

scholarly journals Non-intrusive energy disaggregation by detecting similarities in consumption patterns

2020 ◽  
Author(s):  
Juan Pablo Chavat ◽  
Sergio Nesmachnow ◽  
Jorge Graneri
Keyword(s):  
Energy Consumption ◽  
Residential Buildings ◽  
Consumption Patterns ◽  
Electric System ◽  
Electricity Cost ◽  
Energy Meter ◽  
Load Monitoring ◽  
The Difference ◽  
Using Data ◽  
Problem Instances

Breaking down the aggregated energy consumption into a detailed consumption per appliance is a crucial tool for energy efficiency in residential buildings. Non-intrusive load monitoring allows implementing this strategy using just a smart energy meter without installing extra hardware. The obtained information is critical to provide an accurate characterization of energy consumption in order to avoid an overload of the electric system, and also to elaborate special tariffs to reduce the electricity cost for users. This article presents an approach for energy consumption disaggregation in households, based on detecting similar consumption patterns from previously recorded labelled datasets. The experimental evaluation of the proposed method is performed over four different problem instances that model real household scenarios using data from an energy consumption repository. Experimental results are compared with twobuilt-in algorithms provided by the nilmtk framework (combinatorial optimization and factorial hidden Markov model). The proposed algorithm was able to achieve accurate results regarding standard prediction metrics. The accuracy was not affected in a significant manner by the presence of ambiguity between the energy consumption of different appliances or by the difference of consumption between training and test appliances.

scholarly journals Nonintrusive Residential Electricity Load Decomposition Based on Transfer Learning

2021 ◽  
Vol 13 (12) ◽  
pp. 6546
Author(s):  
Mingzhi Yang ◽  
Yue Liu ◽  
Quanlong Liu
Keyword(s):  
Neural Network ◽  
Transfer Learning ◽  
Residential Buildings ◽  
Electricity Consumption ◽  
Electrical Load ◽  
Prediction Ability ◽  
Training Time ◽  
Load Monitoring ◽  
Open Datasets ◽  
Improved Model

Monitoring electricity consumption in residential buildings is an important way to help reduce energy usage. Nonintrusive load monitoring is a technique to separate the total electrical load of a single household into specific appliance loads. This problem is difficult because we aim to extract the energy consumption of each appliance by only using the total electrical load. Deep transfer learning is expected to solve this problem. This paper proposes a deep neural network model based on an attention mechanism. This model improves the traditional sequence-to-sequence model with a time-embedding layer and an attention layer so that it can be better applied in nonintrusive load monitoring. In particular, the improved model abandons the recurrent neural network structure and shortens the training time, which means it is more appropriate for use in model pretraining with large datasets. To verify the validity of the model, we selected three open datasets and compared them with the current leading model. The results show that transfer learning can effectively improve the prediction ability of the model, and the model proposed in this study has a better performance than the most advanced available model.

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