Electrical Appliance Usage and Electricity Consumption Pattern at household level

Energy Management at household level is multifaceted issue due to factors involving gender, education and awareness to energy usage. This study was conducted in Peshawar city, Pakistan which is capital city of the province and densely populated. This study identifies the role of women in energy management at household level keeping in view household’s characteristics in an underdeveloped country. The key factors included were (1) education and job status (2) characteristics of nuclear and joint family system (3) energy consumption pattern of the households and (4) awareness of energy management and its implementation amongst the females in the household. One-way ANOVA test shows that women spend more than three hours while utilizing energy appliances. In addition, awareness of high billing cost per unit, electricity consumption during peak hours’ unit, idea of renewable energy sources and their use cum awareness level was found to be extremely low. The results show that the education of husband and wife is indistinguishable and the females are the major decision makers in carrying the household chores. Males are the sole bread winners of the house and majority of the females are housewives despite of attaining higher education. Despite the fact that women are aware of household energy management, there is still a need for full implementation and awareness among women in the household. Lastly, trend of nuclear family system is making pace in the Pakistan and energy management and utility bills are handled independently. The results can be used for policy making in developing countries.

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A Household-based analysis of domestic energy consumption for lighting in Jaipur City

International Journal of Built Environment and Sustainability ◽

10.11113/ijbes.v3.n2.129 ◽

2016 ◽

Vol 3 (2) ◽

Author(s):

Nand Kumar ◽

V Devadas

Keyword(s):

Energy Consumption ◽

Rural Areas ◽

Urban Areas ◽

Electricity Consumption ◽

Household Survey ◽

Multiple Regression Model ◽

Consumption Pattern ◽

Household Level ◽

Domestic Energy ◽

Jaipur City

India being the third largest economy of the world, more than two third of the total population lives in villages and started to consuming more quantity of energy in the recent years. Though the electricity consumption in the domestic sector has increased up to 22 per cent of the total electricity consumption, electricity consumption in villages is very less, since good number of villages in the rural system are not even electrified. In urban areas almost 90 percent of the household use electricity for lighting and just 10 percent use kerosene for the said purpose, whereas in the rural areas still more number of households use kerosene for lighting purposes. In this paper an attempt is made to analyze the domestic energy consumption for lighting in Jaipur city. Good amount of literature collected pertaining to domestic energy consumption for lighting purposes across the globe, analyzed thoroughly and presented. Further, a household survey was conducted among 684 households in Jaipur city by employing pre-tested schedule. The schedule has few variables including identification particulars, economic conditions, demographic pattern, domestic lighting appliances at the household level; and the energy consumption pattern. Further the collected data are analyzed and a multiple regression model was developed by considering the total electricity consumption as dependent variable ‘Y’ and the electrical appliances for lighting purposes, such as the number of incandescent bulbs, tube lights, CFL, and LED are considered as ‘X’ variables; and this study conclude with plausible findings and recommendations.

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Electricity Usage Monitoring Based On Internet of Things

ELEKTRIKA- Journal of Electrical Engineering ◽

10.11113/elektrika.v20n3.284 ◽

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.

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

Global Applications of Pervasive and Ubiquitous Computing ◽

10.4018/978-1-4666-2645-4.ch013 ◽

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.

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

Energies ◽

10.3390/en13164154 ◽

2020 ◽

Vol 13 (16) ◽

pp. 4154 ◽

Cited By ~ 4

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.

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

Energies ◽

10.3390/en12183452 ◽

2019 ◽

Vol 12 (18) ◽

pp. 3452 ◽

Cited By ~ 7

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.

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A Versatile Clustering Method for Electricity Consumption Pattern Analysis in Households

IEEE Transactions on Smart Grid ◽

10.1109/tsg.2013.2240319 ◽

2013 ◽

Vol 4 (2) ◽

pp. 1048-1057 ◽

Cited By ~ 34

Author(s):

Hideitsu Hino ◽

Haoyang Shen ◽

Noboru Murata ◽

Shinji Wakao ◽

Yasuhiro Hayashi

Keyword(s):

Pattern Analysis ◽

Electricity Consumption ◽

Consumption Pattern ◽

Clustering Method

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

International Journal of Energy Sector Management ◽

10.1108/ijesm-02-2016-0005 ◽

2017 ◽

Vol 11 (2) ◽

pp. 295-310 ◽

Cited By ~ 1

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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Cash for Coolers: Evaluating a Large-Scale Appliance Replacement Program in Mexico

American Economic Journal Economic Policy ◽

10.1257/pol.6.4.207 ◽

2014 ◽

Vol 6 (4) ◽

pp. 207-238 ◽

Cited By ~ 82

Author(s):

Lucas W. Davis ◽

Alan Fuchs ◽

Paul Gertler

Keyword(s):

Energy Efficient ◽

Carbon Dioxide Emissions ◽

Air Conditioning ◽

Large Scale ◽

Energy Use ◽

Electricity Consumption ◽

Program Cost ◽

Air Conditioners ◽

Household Level ◽

The Universe

This paper evaluates a large-scale appliance replacement program in Mexico that from 2009 to 2012 helped 1.9 million households replace their old refrigerators and air conditioners with energy-efficient models. Using household-level billing records from the universe of Mexican residential customers, we find that refrigerator replacement reduces electricity consumption by 8 percent, about one-quarter of what was predicted by ex ante analyses. Moreover, we find that air conditioning replacement actually increases electricity consumption. Overall, we find that the program is an expensive way to reduce externalities from energy use, reducing carbon dioxide emissions at a program cost of over $500 per ton. (JEL L68, L94, O12, O13, Q41, Q54)

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Parallel Evolutionary Biclustering of Short-term Electric Energy Consumption

10.5121/csit.2021.111110 ◽

2021 ◽

Author(s):

Diego P. Pinto-Roa ◽

Hernán Medina ◽

Federico Román ◽

Miguel García-Torres ◽

Federico Divina ◽

...

Keyword(s):

Energy Consumption ◽

Electric Energy ◽

Electricity Consumption ◽

Search Space ◽

Consumption Pattern ◽

Electric Energy Consumption ◽

System A ◽

Time Period ◽

Computation Scheme ◽

Significant Patterns

The discovery and description of patterns in electric energy consumption time series is fundamental for timely management of the system. A bicluster describes a subset of observation points in a time period in which a consumption pattern occurs as abrupt changes or instabilities homogeneously. Nevertheless, the pattern detection complexity increases with the number of observation points and samples of the study period. In this context, current bi-clustering techniques may not detect significant patterns given the increased search space. This study develops a parallel evolutionary computation scheme to find biclusters in electric energy. Numerical simulations show the benefits of the proposed approach, discovering significantly more electricity consumption patterns compared to a state-of-the-art non-parallel competitive algorithm.

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