Performance Evaluation of Superstate HMM with Median Filter For Appliance Energy Disaggregation

Information on electricity consumption is one of the essential elements in terms of regulating the distribution of electricity in smart micro grid. Besides, information on electricity consumption can help consumers carry out an evaluation process to reduce electricity bill costs, which indirectly affect overall energy efficiency. One method in the process of monitoring electricity consumption is Non-Intrusive Load Monitoring (NILM). The main problem in NILM is to determine the energy disaggregation consumed by several equipment by merely performing the retrieval of data from only one measuring point. We used the Superstate Hidden Markov Model as the tool for modelling and analysis. A median data filter to the input data is applied to improve the performance of the disaggregation process. Based on the results of tests conducted using the REDD, the lowest accuracy was 96.69% for all tests performed

Universal Non-Intrusive Load Monitoring (UNILM) Using Filter Pipelines, Probabilistic Knapsack, and Labelled Partition Maps

Being able to track appliances energy usage without the need of sensors can help occupants reduce their energy consumption to help save the environment all while saving money. Non-intrusive load monitoring (NILM) tries to do just that. One of the hardest problems NILM faces is the ability to run unsupervised -- discovering appliances without prior knowledge -- and to run independent of the differences in appliance mixes and operational characteristics found in various countries and regions. We propose a solution that can do this with the use of an advanced filter pipeline to preprocess the data, a Gaussian appliance model with a probabilistic knapsack algorithm to disaggregate the aggregate smart meter signal, and partition maps to label which appliances were found and how much energy they use no matter the country/region. Experimental results show that relatively complex appliance signals can be tracked accounting for 93.7% of the total aggregate energy consumed

Non-intrusive electrical energy monitoring based on intelligent system

The demands of electricity are increasing from day to day as many housing and building construction are built to fulfil the demands. Abreast with the development, the electricity is an essential for daily basis, thus the wastage is also a problem that needs to overcome. The electrical wastage problem is focused on the residency college as students are always not aware to turn off the appliances when leaving the room, for instance, the fan and lamp. The first stage to overcome the wastage problem, an approach called “Non-Intrusive Electrical Energy Monitoring (NIEM)” is proposed to this project. NIEM encompass a method of detecting the electrical energy consumption in a building by using a single set of sensor on the main distribution board for each building. This method is in contrast to Intrusive Electrical Energy Monitoring (IEM) where the end-use devices are sensed. To realize the method used, an energy meter is used to measure the electrical consumption by the appliances. The data obtained will be analyzed using a method called Multilayer Perceptron (MLP) technique of Artificial Neural Network (ANN). The technique will firstly implement the event detection to identify the type of loads and the power consumption of the load which is intensified as fan and lamp. The switching ON and OFF events of the loads are made in order and random to test the capability of MLP to classify the type of loads. Then the data were divided to 70% for training, 15% for testing and 15% for validation. The output of the MLP is either ‘1’ for fan or ‘0’ for lamp. The system can be re-train to obtain a good performance, lower Mean Square Error (difference between output and target), and lower percent error (misclassified data). For later stages in future, a Neural Network system can be design to automatically turn off the appliances whenever not in used, so that the electrical wastage and monthly bill can be reduced to strive for a green and energy saving manner

Improving Residential Load Disaggregation for Sustainable Development of Energy via Principal Component Analysis

The useful planning and operation of the energy system requires a sustainability assessment of the system, in which the load model adopted is the most important factor in sustainability assessment. Having information about energy consumption patterns of the appliances allows consumers to manage their energy consumption efficiently. Non-intrusive load monitoring (NILM) is an effective tool to recognize power consumption patterns from the measured data in meters. In this paper, an unsupervised approach based on dimensionality reduction is applied to identify power consumption patterns of home electrical appliances. This approach can be utilized to classify household activities of daily life using data measured from home electrical smart meters. In the proposed method, the power consumption curves of the electrical appliances, as high-dimensional data, are mapped to a low-dimensional space by preserving the highest data variance via principal component analysis (PCA). In this paper, the reference energy disaggregation dataset (REDD) has been used to verify the proposed method. REDD is related to real-world measurements recorded at low-frequency. The presented results reveal the accuracy and efficiency of the proposed method in comparison to conventional procedures of NILM

Nonintrusive Load Monitoring (NILM) Using a Deep Learning Model with a Transformer-Based Attention Mechanism and Temporal Pooling

Nonintrusive load monitoring (NILM) is an important technique for energy management and conservation. In this paper, a deep learning model based on an attention mechanism, temporal pooling, residual connections, and transformers is proposed. This article presents a novel approach for NILM to accurately discern energy consumption patterns of individual household appliances. The proposed method entails a sequence of layers, including encoders, transformers, attention, temporal pooling, and residual connections, offering a comprehensive solution for NILM while effectively capturing appliance-specific energy usage in a household. The proposed model was evaluated using UK-DALE, REDD, and REFIT datasets in both seen and unseen cases. It shows that the proposed model in this paper performs better than other methods stated in other papers in terms of F1-score and total error of the results (in terms of SAE). This model achieved an F1-score equal to 92.96 as well as a total SAE equal to −0.036, which shows its effectiveness in accurately diagnosing and estimating the energy consumption of individual home appliances. The findings of this research show that the proposed model can be a tool for energy management in residential and commercial buildings

Pengawasan beban tak mengganggu menggunakan mesin penyokong vektor

Kertas kerja ini membentangkan pembangunan pengawasan beban tak mengganggu (PBTM) untuk mengenal pasti beban dengan menggunakan pengelasan mesin penyokong vektor berbilang (MPVB). Suatu kaedah pengawasan beban diselia dilaksanakan untuk mengenal pasti tiga jenis beban yang kebiasaannya terdapat di bangunan komersial iaitu lampu pendaflour, penghawa dingin dan komputer peribadi. Parameter kuasa asas yang terdapat pada meter pintar dan penyarian sifat kuasa lain yang lebih terperinci dipertimbangkan dalam kertas kerja ini. Sifat kuasa yang berkesan ditentukan dengan melakukan pemilihan sifat mengikut kombinasi yang berpotensi. Selain itu, teknik baru penyarian sifat, iaitu, jelmaan masa-masa (MM) diperkenalkan dalam kajian ini. Suatu kaedah pemilihan sifat kuasa yang sistematik dilaksanakan dengan mempertimbangkan kombinasi terbaik untuk tujuan perbandingan. Berikutan penggunaan meter pintar komersial di sektor pengguna adalah majoriti dengan kadar pensampelan yang rendah, perlaksanaan eksperimen dan kajian yang dilakukan adalah di bawah pengukuran penggunaan yang sebenar dengan pensampelan yang rendah. Kadar pensampelan rendah yang sesuai untuk PBTM dikaji mengikut spesifikasi meter pintar komersial dengan tiga keadaan pensampelan iaitu 1 minit, 10 minit dan 30 minit. Satu set data pengesahsahihan dengan aktiviti beban secara rawak digunakan untuk menguji kemantapan kaedah PBTM yang dibangunkan. Justeru, teknik pengelasan beban menggunakan MPVB dibandingkan dengan teknik lain seperti bayes lurus dan K-kejiranan terdekat (KKT) untuk menilai prestasi MPVB yang dicadangkan untuk PBTM. Menerusi keputusan yang diperolehi, kaedah yang dicadangkan iaitu MPVB menunjukkan keputusan pengelasan yang terbaik dengan 99.94% ketepatan dalam mengenal pasti beban. Justeru, berdasarkan kadar pensampelan yang dikaji pensampelan 1 minit menunjukkan penggunaan pengawasan beban yang terbaik berbanding pensampelan lain yang dikaji untuk tujuan PBTM

A Non Intrusive Low Cost Kit for Electric Power Measuring and Energy Disaggregation

This article presents a kit to collect data of electric loads of single and three phases main power supply of a house and perform the energy disaggregation. To collect the data, we use sensors based on open magnetic core to measure the electromagnetic field induced by the current in the electric conducting wire in a non intrusive way. In particular, each sensor from the three-phase device wraps/encloses each phase without alignment. In order to calibrate the three-phase device, we present a method to calculate the neutral RMS without complex numbers using (Analysis of Variance) ANOVA and post hoc Tukey’s multiple comparison test to assert the differences of measures among phases. We managed to validate the method using a measure reference. Additionally, to perform the energy disaggregation, we use the NILMTK tool. This toll was used, initially, to compare disaggregation algorithms on many public datasets. We use in our system two disaggregation algorithms Combinatorial Optimization and Factorial Hidden Markov Model algorithms. The results show that is possible to collect and perform energy disaggregation through our embedded system