Browsing by Author "Angammana, C. J"

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Comprehensive Analysis of Convolutional Neural Network Models for Non-Instructive Load Monitoring
(IEEE, 2020-10-20) Herath, G. M; Thilakanayake, T. D; Liyanage, M. H; Angammana, C. J
Non-Instructive Load Monitoring (NILM) schemes have become more popular in recent years with the availability of smart meters. It provides energy use data to utilities and per-appliance energy consumption details to end users. This study carries out a comprehensive analysis of existing Convolutional Neural Network (CNN) architectures that have been used for NILM. Nevertheless, it provides an unbiased comparison of the existing architectures thereby helping to select the best performing model for NILM applications. The commonly used CNN disaggregation models were categorized into distinctive groups based on their architectures which considered structure of the Neural Network (NN) and outputs. It considers regression-based sequence to sequence and sequence to point mapping, classification-based sequence to point hard association and soft association-based mapping. The CNN models are improved and modified to bring them onto a common platform for comparison. Thereafter, a rigorous comparison was performed using indices which included accuracy, precision, F-measure and recall. The results reveal interesting relationships between architectures, appliances and measures.
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An Image Based Approach of Energy Signal Disaggregation Using Artificial Intelligence
(IEEE, 2021-12-09) Senarathna, M; Herath, M; Thilakanayake, H. D; Liyanage, M. H; Angammana, C. J
Non-Intrusive Load Monitoring (NILM) is the real-time monitoring of energy consumption data of individual appliances through the decomposition of composite energy signal captured at the household smart energy meter. Most of the existing NILM techniques utilize one-dimensional (1D) time-series signal analysis to predict the individual appliance energy signals. The utilization of image-based methods for the disaggregation of energy signals is a relatively new approach in the NILM domain. This paper presents a study of a novel computer vision-based Artificial Intelligence (AI) approach when compared to the traditional time series-based NILM methods. Gramian Angular Fields (GAF) and Recurrence Plots (RP) have been widely used in recent literature to encode time series signals as images. Novel image classification techniques with the use of Convolutional Neural Networks (CNN) simplify the extraction of nuclear load features from encoded two-dimensional (2D) images. The results considered the indices validation accuracy and validation loss in comparing the performance of different vision-based AI approaches. The results reveal that Gramian Angular Difference Field (GADF) outperforms both Gramian Angular Summation Field (GASF) and RP with a training accuracy of 97.9% and a validation accuracy of 94.2%. A comprehensive analysis and comparison are presented with an in-depth evaluation using multi-state appliances and it was concluded that GADF is the most suitable 1D to 2D conversion method for the representation of time series energy data for disaggregation purposes.