A Parallel Evolutionary Computing-Embodied Artificial Neural Network Applied to Non-Intrusive Load Monitoring for Demand-Side Management in a Smart Home: Towards Deep Learning

Lin, Yu‐Hsiu

doi:10.3390/s20061649

Cited by 9 publications

(3 citation statements)

References 42 publications

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“…As a result, great effort is being put towards incorporating sensors and microprocessors into various devices to create 'Smart Devices'. These devices size range from large (smart cars [1,2], smart homes [3][4][5]) to minuscule (smart watches [6,7] and other wearable devices [8][9][10]). Consequently, data are being generated and collected at an ever-growing rate, with projections of continual growth.…”

Section: Motivationmentioning

confidence: 99%

Colocalized Sensing and Intelligent Computing in Micro-Sensors

Hasan

Al-Ramini

Abdel‐Rahman

et al. 2020

Sensors

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This work presents an approach to delay-based reservoir computing (RC) at the sensor level without input modulation. It employs a time-multiplexed bias to maintain transience while utilizing either an electrical signal or an environmental signal (such as acceleration) as an unmodulated input signal. The proposed approach enables RC carried out by sufficiently nonlinear sensory elements, as we demonstrate using a single electrostatically actuated microelectromechanical system (MEMS) device. The MEMS sensor can perform colocalized sensing and computing with fewer electronics than traditional RC elements at the RC input (such as analog-to-digital and digital-to-analog converters). The performance of the MEMS RC is evaluated experimentally using a simple classification task, in which the MEMS device differentiates between the profiles of two signal waveforms. The signal waveforms are chosen to be either electrical waveforms or acceleration waveforms. The classification accuracy of the presented MEMS RC scheme is found to be over 99%. Furthermore, the scheme is found to enable flexible virtual node probing rates, allowing for up to 4× slower probing rates, which relaxes the requirements on the system for reservoir signal sampling. Finally, our experiments show a noise-resistance capability for our MEMS RC scheme.

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Section: Motivationmentioning

confidence: 99%

Colocalized Sensing and Intelligent Computing in Micro-Sensors

Hasan

Al-Ramini

Abdel‐Rahman

et al. 2020

Sensors

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“…To solve this problem, nonlinear machine learning models represented by artificial neural networks (ANNs) have been gradually proposed and promoted. Nonlinear autoregressive neural networks (NARNNs), hereafter referred to as NARs, approach nonlinear regression through neural networks and can generalize and deal with high-dimensional nonlinear regression estimation [ 9 – 12 ]. In addition, long short-term memory (LSTM), an improved recurrent neural network (RNN), has brought revolutionary changes to various fields.…”

Section: Introductionmentioning

confidence: 99%

Deep evolutionary fusion neural network: a new prediction standard for infectious disease incidence rates

Yao,

Chen,

Wang

et al. 2024

BMC Bioinformatics

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Background Previously, many methods have been used to predict the incidence trends of infectious diseases. There are numerous methods for predicting the incidence trends of infectious diseases, and they have exhibited varying degrees of success. However, there are a lack of prediction benchmarks that integrate linear and nonlinear methods and effectively use internet data. The aim of this paper is to develop a prediction model of the incidence rate of infectious diseases that integrates multiple methods and multisource data, realizing ground-breaking research. Results The infectious disease dataset is from an official release and includes four national and three regional datasets. The Baidu index platform provides internet data. We choose a single model (seasonal autoregressive integrated moving average (SARIMA), nonlinear autoregressive neural network (NAR), and long short-term memory (LSTM)) and a deep evolutionary fusion neural network (DEFNN). The DEFNN is built using the idea of neural evolution and fusion, and the DEFNN + is built using multisource data. We compare the model accuracy on reference group data and validate the model generalizability on external data. (1) The loss of SA-LSTM in the reference group dataset is 0.4919, which is significantly better than that of other single models. (2) The loss values of SA-LSTM on the national and regional external datasets are 0.9666, 1.2437, 0.2472, 0.7239, 1.4026, and 0.6868. (3) When multisource indices are added to the national dataset, the loss of the DEFNN + increases to 0.4212, 0.8218, 1.0331, and 0.8575. Conclusions We propose an SA-LSTM optimization model with good accuracy and generalizability based on the concept of multiple methods and multiple data fusion. DEFNN enriches and supplements infectious disease prediction methodologies, can serve as a new benchmark for future infectious disease predictions and provides a reference for the prediction of the incidence rates of various infectious diseases.

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“…NILM can be thought as a blind source separation task [ 2 ], with only the mains consumption signal provided as input, and can be an essential tool for both individual consumers and distribution system operators (DSOs). From the consumer side, NILM constitutes a vital part of intelligent home systems, providing insights into reducing energy waste, raising energy awareness [ 3 , 4 ], improving the operational efficiency of installations [ 5 , 6 , 7 ], and creating smart alert mechanisms for residents in need [ 8 , 9 , 10 ]. On the other hand, DSOs can use NILM as a building block for various applications regarding the management and efficient monitoring of the grid [ 11 , 12 ] in combination with more accurate energy consumption forecasts [ 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

Variational Regression for Multi-Target Energy Disaggregation

Gkalinikis

Nalmpantis

Vrakas

2023

Sensors

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Non-intrusive load monitoring systems that are based on deep learning methods produce high-accuracy end use detection; however, they are mainly designed with the one vs. one strategy. This strategy dictates that one model is trained to disaggregate only one appliance, which is sub-optimal in production. Due to the high number of parameters and the different models, training and inference can be very costly. A promising solution to this problem is the design of an NILM system in which all the target appliances can be recognized by only one model. This paper suggests a novel multi-appliance power disaggregation model. The proposed architecture is a multi-target regression neural network consisting of two main parts. The first part is a variational encoder with convolutional layers, and the second part has multiple regression heads which share the encoder’s parameters. Considering the total consumption of an installation, the multi-regressor outputs the individual consumption of all the target appliances simultaneously. The experimental setup includes a comparative analysis against other multi- and single-target state-of-the-art models.

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A Parallel Evolutionary Computing-Embodied Artificial Neural Network Applied to Non-Intrusive Load Monitoring for Demand-Side Management in a Smart Home: Towards Deep Learning

Cited by 9 publications

References 42 publications

Colocalized Sensing and Intelligent Computing in Micro-Sensors

Colocalized Sensing and Intelligent Computing in Micro-Sensors

Deep evolutionary fusion neural network: a new prediction standard for infectious disease incidence rates

Variational Regression for Multi-Target Energy Disaggregation

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