scholarly journals Multi-Label Learning for Appliance Recognition in NILM Using Fryze-Current Decomposition and Convolutional Neural Network

Energies ◽  
2020 ◽  
Vol 13 (16) ◽  
pp. 4154 ◽  
Author(s):  
Anthony Faustine ◽  
Lucas Pereira
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Energy Demand ◽  
Learning Strategy ◽  
Demand Management ◽  
Management Strategies ◽  
Electricity Consumption ◽  
Consumption Pattern ◽  
Similarity Function ◽  
Active Components

The advance in energy-sensing and smart-meter technologies have motivated the use of a Non-Intrusive Load Monitoring (NILM), a data-driven technique that recognizes active end-use appliances by analyzing the data streams coming from these devices. NILM offers an electricity consumption pattern of individual loads at consumer premises, which is crucial in the design of energy efficiency and energy demand management strategies in buildings. Appliance classification, also known as load identification is an essential sub-task for identifying the type and status of an unknown load from appliance features extracted from the aggregate power signal. Most of the existing work for appliance recognition in NILM uses a single-label learning strategy which, assumes only one appliance is active at a time. This assumption ignores the fact that multiple devices can be active simultaneously and requires a perfect event detector to recognize the appliance. In this paper proposes the Convolutional Neural Network (CNN)-based multi-label learning approach, which links multiple loads to an observed aggregate current signal. Our approach applies the Fryze power theory to decompose the current features into active and non-active components and use the Euclidean distance similarity function to transform the decomposed current into an image-like representation which, is used as input to the CNN. Experimental results suggest that the proposed approach is sufficient for recognizing multiple appliances from aggregated measurements.

scholarly journals Attention Enhanced Serial Unet++ Network for Removing Unevenly Distributed Haze

Electronics ◽  
2021 ◽  
Vol 10 (22) ◽  
pp. 2868
Author(s):  
Wenxuan Zhao ◽  
Yaqin Zhao ◽  
Liqi Feng ◽  
Jiaxi Tang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Real World ◽  
Large Scale ◽  
Learning Strategy ◽  
Contextual Information ◽  
Small Scale ◽  
Image Dehazing ◽  
Atmospheric Scattering ◽  
Real World Datasets

The purpose of image dehazing is the reduction of the image degradation caused by suspended particles for supporting high-level visual tasks. Besides the atmospheric scattering model, convolutional neural network (CNN) has been used for image dehazing. However, the existing image dehazing algorithms are limited in face of unevenly distributed haze and dense haze in real-world scenes. In this paper, we propose a novel end-to-end convolutional neural network called attention enhanced serial Unet++ dehazing network (AESUnet) for single image dehazing. We attempt to build a serial Unet++ structure that adopts a serial strategy of two pruned Unet++ blocks based on residual connection. Compared with the simple Encoder–Decoder structure, the serial Unet++ module can better use the features extracted by encoders and promote contextual information fusion in different resolutions. In addition, we take some improvement measures to the Unet++ module, such as pruning, introducing the convolutional module with ResNet structure, and a residual learning strategy. Thus, the serial Unet++ module can generate more realistic images with less color distortion. Furthermore, following the serial Unet++ blocks, an attention mechanism is introduced to pay different attention to haze regions with different concentrations by learning weights in the spatial domain and channel domain. Experiments are conducted on two representative datasets: the large-scale synthetic dataset RESIDE and the small-scale real-world datasets I-HAZY and O-HAZY. The experimental results show that the proposed dehazing network is not only comparable to state-of-the-art methods for the RESIDE synthetic datasets, but also surpasses them by a very large margin for the I-HAZY and O-HAZY real-world dataset.

Residential Load Pattern Analysis for Smart Grid Applications Based on Audio Feature EEUPC

2013 ◽  
pp. 107-121
Author(s):  
Yunzhi Wang ◽  
Xiangdong Wang ◽  
Yueliang Qian ◽  
Haiyong Luo ◽  
Fujiang Ge ◽  
...  
Keyword(s):  
Smart Grid ◽  
Clustering Analysis ◽  
Pattern Analysis ◽  
Electricity Consumption ◽  
Consumption Pattern ◽  
Similarity Function ◽  
Typical Pattern ◽  
Audio Feature ◽  
Load Pattern ◽  
Grid Applications

The smart grid is an important application field of the Internet of things. This paper presents a method of user electricity consumption pattern analysis for smart grid applications based on the audio feature EEUPC. A novel similarity function based on EEUPC is adapted to support clustering analysis of residential load patterns. The EEUPC similarity exploits features of peaks and valleys on curves instead of directly comparing values and obtains better performance for clustering analysis. Moreover, the proposed approach performs load pattern clustering, extracts a typical pattern for each cluster, and gives suggestions toward better power consumption for each typical pattern. Experimental results demonstrate that the EEUPC similarity is more consistent with human judgment than the Euclidean distance and higher clustering performance can be achieved for residential electric load data.

scholarly journals Energy Demand Management and Social Norms

Energies ◽  
2020 ◽  
Vol 13 (15) ◽  
pp. 3779
Author(s):  
Bernadeta Gołębiowska ◽  
Anna Bartczak ◽  
Mikołaj Czajkowski
Keyword(s):  
Social Norms ◽  
Financial Incentives ◽  
Energy Demand ◽  
Consumer Preferences ◽  
Demand Management ◽  
Electricity Consumption ◽  
External Control ◽  
Revealed Preferences ◽  
Personal Norms ◽  
The Impact

The main objective of our study was investigating the impact of norms and financial motivation on the disutility of energy management for Polish households. We analyzed consumer preferences and willingness to accept demand-side management (DSM) programs. Choice experiment was applied for electricity contracts including external control of electricity consumption. Ajzen’s theory of planned behavior provided the theoretical framework of the study, which tested hypotheses about the impact of social norms on consumer choices of electricity contracts. We show that people with higher descriptive social norms about electricity consumption are less sensitive to the level of compensation and more responsive to the number of blackouts. People willing to sign a contract for financial reasons were less sensitive to the external control of electricity consumption and less inclined toward the status quo option. Injunctive social norms and personal norms had a non-significant impact on consumer decisions. We conclude that financial incentives can reduce the effect of the norms. Social and personal norms seem to be more important when we analyze the revealed preferences. European countries face significant challenges related to changes in energy policy. This study contributes to understanding the decisions of households and provides insights into the implementation of DSM.

scholarly journals A Robust and Novel Approach for Brain Tumor Classification Using Convolutional Neural Network

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Tahia Tazin ◽  
Sraboni Sarker ◽  
Punit Gupta ◽  
Fozayel Ibn Ayaz ◽  
Sumaia Islam ◽  
...  
Keyword(s):  
Neural Network ◽  
Brain Tumors ◽  
Convolutional Neural Network ◽  
Learning Strategy ◽  
Severe Form ◽  
Ultrasound Images ◽  
X Ray ◽  
Novel Approach ◽  
Magnetic Resonance Imaging Mri ◽  
Deep Feature Extraction

Brain tumors are the most common and aggressive illness, with a relatively short life expectancy in their most severe form. Thus, treatment planning is an important step in improving patients’ quality of life. In general, image methods such as computed tomography (CT), magnetic resonance imaging (MRI), and ultrasound images are used to assess tumors in the brain, lung, liver, breast, prostate, and so on. X-ray images, in particular, are utilized in this study to diagnose brain tumors. This paper describes the investigation of the convolutional neural network (CNN) to identify brain tumors from X-ray images. It expedites and increases the reliability of the treatment. Because there has been a significant amount of study in this field, the presented model focuses on boosting accuracy while using a transfer learning strategy. Python and Google Colab were utilized to perform this investigation. Deep feature extraction was accomplished with the help of pretrained deep CNN models, VGG19, InceptionV3, and MobileNetV2. The classification accuracy is used to assess the performance of this paper. MobileNetV2 had the accuracy of 92%, InceptionV3 had the accuracy of 91%, and VGG19 had the accuracy of 88%. MobileNetV2 has offered the highest level of accuracy among these networks. These precisions aid in the early identification of tumors before they produce physical adverse effects such as paralysis and other impairments.

scholarly journals Improving the energy efficiency of sorting centers by identifying objects and digit-letter information with neural networks

2021 ◽  
Vol 279 ◽  
pp. 01017
Author(s):  
Evgeniy Ivliev ◽  
Pavel Obukhov ◽  
Viktor Ivliev ◽  
Denis Medvedev ◽  
Viktor Martynov
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Energy Efficiency ◽  
Convolutional Neural Network ◽  
Electricity Consumption ◽  
Dynamic Objects

The article is devoted to the development and analysis of methods of identifying dynamic objects. A neural network with the architecture of SSD MobileNetV2 has been developed to solve the problem of detecting baggage tags and barcodes. Several approaches are considered to solve the problem of identifying digital-letter information: Tesseract, SSD InceptionV2, OpenCV and a convolutional neural network. The efficiency of the methods on real images was checked. It was concluded that electricity consumption can be reduced by 49.43%.

scholarly journals An IOT based smart metering development for energy management system

2019 ◽  
Vol 9 (4) ◽  
pp. 3041 ◽  
Author(s):  
S.G Priyadharshini ◽  
C. Subramani ◽  
J. Preetha Roselyn
Keyword(s):  
Remote Monitoring ◽  
Energy Demand ◽  
Demand Management ◽  
Electricity Consumption ◽  
Smart Meter ◽  
End User ◽  
Labview Software ◽  
The Past ◽  
To Come ◽  
Smart Grid Technology

<p>The worldwide energy demand is increasing and hence necessity measures need to be taken to reduce the energy wastage with proper metering infrastructure in the buildings. A Smart meter can be used to monitor electricity consumption of customers in the smart grid technology. For allocating the available resources proper energy demand management is required. During the past years, various methods are being utilized for energy demand management to precisely calculate the requirements of energy that is yet to come. A large system presents a potential esteem to execute energy conservation as well as additional services linked to energy services, extended as a competent with end user is executed. The supervising system at the utilities determines the interface of devices with significant advantages, while the communication with the household is frequently proposing particular structures for appropriate buyer-oriented implementation of a smart meter network. Also, this paper concentrates on the estimation of vitality utilization. In this paper energy is measured in units and also product arrangement is given to create bill for energy consumption and implementing in LabVIEW software. An IOT based platform is created for remote monitoring of the metering infrastructure in the real time. The data visualization is also carried out in webpage and the data packet loss is investigated in the remote monitoring of the parameters.</p>

Sign in / Sign up

Export Citation Format

Share Document