scholarly journals District household electricity consumption pattern analysis based on auto-encoder algorithm

2019 ◽  
Vol 609 ◽  
pp. 072028
Author(s):  
Yuan Jin ◽  
Da Yan ◽  
Xingxing Zhang ◽  
Mengjie Han ◽  
Xuyuan Kang ◽  
...  
Keyword(s):  
Pattern Analysis ◽  
Electricity Consumption ◽  
Consumption Pattern

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.

A Versatile Clustering Method for Electricity Consumption Pattern Analysis in Households

2013 ◽  
Vol 4 (2) ◽  
pp. 1048-1057 ◽  
Author(s):  
Hideitsu Hino ◽  
Haoyang Shen ◽  
Noboru Murata ◽  
Shinji Wakao ◽  
Yasuhiro Hayashi
Keyword(s):  
Pattern Analysis ◽  
Electricity Consumption ◽  
Consumption Pattern ◽  
Clustering Method

scholarly journals District Household Electricity Consumption Pattern Analysis Based on Auto-Encoder Algorithm

2021 ◽  
pp. 387-398
Author(s):  
Yuan Jin ◽  
Da Yan ◽  
Xingxing Zhang ◽  
Mengjie Han ◽  
Xuyuan Kang ◽  
...  
Keyword(s):  
Pattern Analysis ◽  
Electricity Consumption ◽  
Consumption Pattern

Electricity Usage Monitoring Based On Internet of Things

2021 ◽  
Vol 20 (3) ◽  
pp. 37-42
Author(s):  
Mohd Ridzuan Ahmad ◽  
Hishamuddin Hashim
Keyword(s):  
Internet Of Things ◽  
Electricity Consumption ◽  
Current Transformer ◽  
Consumption Pattern ◽  
Process Scheduling ◽  
Time Data ◽  
Domestic Use ◽  
Electrical Consumption ◽  
The Internet Of Things ◽  
A Current

Electricity monitoring systems have long been used in industrial scenarios such as process scheduling and distribution. This monitoring system needs to be developed for domestic use such as in homes and shops. In recent times, the electricity demand has increased in households with the use of different appliances. The advent of technologies such as the Internet of Things (IoT) has made real-time data acquisition and analysis possible. This project is designed to control and monitor household electricity consumption via smartphones using the ESP8266 Wi-Fi module as a communication protocol and the Blynk application as a private server. The used wifi module provides notification through the Blynk application. The system uses an Arduino Mega2560 microcontroller to control all devices in this project. For monitoring the energy usage, a current sensor type Split Core Current Transformer (SCT013) was used. From the experimental results, it is confirmed that the system is capable of monitoring the whole house’s electrical usage easily. With this system in place, end-users are provided with proper awareness and able to plan their home’s electrical consumption pattern effectively.

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 Knowledge Embedded Semi-Supervised Deep Learning for Detecting Non-Technical Losses in the Smart Grid

Energies ◽  
2019 ◽  
Vol 12 (18) ◽  
pp. 3452 ◽  
Author(s):  
Xiaoquan Lu ◽  
Yu Zhou ◽  
Zhongdong Wang ◽  
Yongxian Yi ◽  
Longji Feng ◽  
...  
Keyword(s):  
Deep Learning ◽  
Electricity Consumption ◽  
Small Samples ◽  
Consumption Pattern ◽  
Feature Models ◽  
One Dimensional ◽  
Electricity Theft ◽  
Sample Data ◽  
Related Feature ◽  
Deep Learning Model

Non-technical losses (NTL) caused by fault or electricity theft is greatly harmful to the power grid. Industrial customers consume most of the power energy, and it is important to reduce this part of NTL. Currently, most work concentrates on analyzing characteristic of electricity consumption to detect NTL among residential customers. However, the related feature models cannot be adapted to industrial customers because they do not have a fixed electricity consumption pattern. Therefore, this paper starts from the principle of electricity measurement, and proposes a deep learning-based method to extract advanced features from massive smart meter data rather than artificial features. Firstly, we organize electricity magnitudes as one-dimensional sample data and embed the knowledge of electricity measurement in channels. Then, this paper proposes a semi-supervised deep learning model which uses a large number of unlabeled data and adversarial module to avoid overfitting. The experiment results show that our approach can achieve satisfactory performance even when trained by very small samples. Compared with the state-of-the-art methods, our method has achieved obvious improvement in all metrics.

Design of electricity tariff plans using gap statistic for K-means clustering based on consumers monthly electricity consumption data

2017 ◽  
Vol 11 (2) ◽  
pp. 295-310 ◽  
Author(s):  
Ravindra R. Rathod ◽  
Rahul Dev Garg
Keyword(s):  
Design Method ◽  
Electricity Consumption ◽  
Consumption Pattern ◽  
Content Type ◽  
Gap Statistic ◽  
Use Of Data ◽  
Daily Data ◽  
Consumption Data ◽  
Practical Implications ◽  
Monthly Electricity Consumption

Purpose Electricity consumption around the world and in India is continuously increasing over the years. Presently, there is a huge diversity in electricity tariffs across states in India. This paper aims to focus on development of new tariff design method using K-means clustering and gap statistic. Design/methodology/approach Numbers of tariff plans are selected using gap-statistic for K-means clustering and regression analysis is used to deduce new tariffs from existing tariffs. The study has been carried on nearly 27,000 residential consumers from Sangli city, Maharashtra State, India. Findings These tariff plans are proposed with two objectives: first, possibility to shift consumer’s from existing to lower tariff plan for saving electricity and, second, to increase revenue by increasing tariff charges using Pay-by-Use policy. Research limitations/implications The study can be performed on hourly or daily data using automatic meter reading and to introduce Time of Use or demand based tariff. Practical implications The proposed study focuses on use of data mining techniques for tariff planning based on consumer’s electricity usage pattern. It will be helpful to detect abnormalities in consumption pattern as well as forecasting electricity usage. Social implications Consumers will be able to decide own monthly electricity consumption and related tariff leading to electricity savings, as well as high electricity consumption consumers have to pay more tariff charges for extra electricity usage. Originality/value To remove the disparity in various tariff plans across states and country, proposed method will help to provide a platform for designing uniform tariff for entire country based on consumer’s electricity consumption data.

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