scholarly journals Data analysis approach for characterizing residential energy consumption based on statistics of household appliances ownership

2021 ◽  
Author(s):  
J.P. Chavat ◽  
S. Nesmachnow
Keyword(s):  
Data Analysis ◽  
Census Data ◽  
Electricity Consumption ◽  
Least Square ◽  
Analysis Approach ◽  
Residential Energy ◽  
The West ◽  
Data Set ◽  
Consumption Data ◽  
Residential Electricity

Worldwide, residential electricity demand has increased constantly, expecting to double in 2050 the demand of 2010. Different policies have been proposed to achieve a smart use of electricity. This article presents a data-analysis approach to evaluate the potential household electricity consumption from statistical data. The main axis of the study are statistics of appliance ownership and information of the appliance characteristics, gathered from census surveys and local shops. An index to estimate the electricity consumption is performed. The validation of the proposed index is carried out using real consumption data from the Electricity Consumption Data set of Uruguay and Ordinary Least Square linear regressions. Jupyter notebooks, Python language and well-know libraries such as Pandas and Numpy were used during the implementation. The main results show that administrative regions located on the West/Southwest coastlines present the highest index scores. In turn, census sections/segments on the West/Southwest coastlines of Montevideo performed the highest scores while the lowest scores can be found at the outskirts of the city. The proposed methodology can be applied for electricity consumption estimation in other regions/countries where census data is publicly available.

scholarly journals The ENERTALK dataset, 15 Hz electricity consumption data from 22 houses in Korea

2019 ◽  
Vol 6 (1) ◽  
Author(s):  
Changho Shin ◽  
Eunjung Lee ◽  
Jeongyun Han ◽  
Jaeryun Yim ◽  
Wonjong Rhee ◽  
...  
Keyword(s):  
Energy Consumption ◽  
Sampling Rate ◽  
Electricity Consumption ◽  
Consumption Pattern ◽  
Additional Information ◽  
Consumption Data ◽  
Energy Disaggregation ◽  
Residential Electricity ◽  
Residential Electricity Consumption

Abstract AMI has been gradually replacing conventional meters because newer models can acquire more informative energy consumption data. The additional information has enabled significant advances in many fields, including energy disaggregation, energy consumption pattern analysis and prediction, demand response, and user segmentation. However, the quality of AMI data varies significantly across publicly available datasets, and low sampling rates and numbers of houses monitored seriously limit practical analyses. To address these challenges, we herein present the ENERTALK dataset, which contains both aggregate and per-appliance measurements sampled at 15 Hz from 22 houses. Among the publicly available datasets with both aggregate and per-appliance measurements, 15 Hz was the highest sampling rate. The number of houses (22) was the second-largest where the largest one had a sampling rate of 1 Hz. The ENERTALK dataset is also the first Korean open dataset on residential electricity consumption.

scholarly journals Challenge for Planning by Using Cluster Methodology: The Case Study of the Algarve Region

2020 ◽  
Vol 12 (4) ◽  
pp. 1536 ◽  
Author(s):  
David Bienvenido-Huertas ◽  
Fátima Farinha ◽  
Miguel José Oliveira ◽  
Elisa M. J. Silva ◽  
Rui Lança
Keyword(s):  
Decision Making ◽  
Cluster Analysis ◽  
Data Analysis ◽  
Subject Area ◽  
Environmental Indicators ◽  
Sustainability Indicators ◽  
Analysis Approach ◽  
Data Set ◽  
Indicator Approach

This study analyses the most appropriate methodology to make similarity classifications among the cities of the Algarve (Portugal) according to 105 sustainability indicators monitored with the Observatory of Sustainability of the Algarve Region for Tourism (OBSERVE). The methodology used to establish the similarities was the cluster analysis with 4 different approaches which reduced the dimensions of the data set: total approach, pillar approach, subject area approach, and indicator approach. By combining the approaches, a total of 620 different cluster analyses were performed. The results reflected that the data analysis approaches with less dimensions were those that performed the best groups among cities. In this sense, the approaches with a high number of indicators (e.g., the total or the pillar approach) were characterised by misclassifying cities in more than 30% of the indicators. Thus, the most acceptable cluster analysis approach was that with a low number of indicators. Through this approach, it was possible to make correct groups of the sustainability level of the cities of the Algarve. These results provided an appropriate methodology for the decision-making regarding the sustainability of a region and could be extrapolated to other regions to assess sustainability or environmental indicators.

scholarly journals Electricity consumption of Singaporean households reveals proactive community response to COVID-19 progression

2021 ◽  
Vol 118 (34) ◽  
pp. e2026596118
Author(s):  
Gururaghav Raman ◽  
Jimmy Chih-Hsien Peng
Keyword(s):  
Influencing Factor ◽  
Electricity Consumption ◽  
Community Response ◽  
Energy Meter ◽  
Consumption Data ◽  
Government Mandates ◽  
Residential Electricity ◽  
Residential Electricity Consumption ◽  
Positive Correlations ◽  
City State

Understanding how populations’ daily behaviors change during the COVID-19 pandemic is critical to evaluating and adapting public health interventions. Here, we use residential electricity-consumption data to unravel behavioral changes within peoples’ homes in this period. Based on smart energy-meter data from 10,246 households in Singapore, we find strong positive correlations between the progression of the pandemic in the city-state and the residential electricity consumption. In particular, we find that the daily new COVID-19 cases constitute the most dominant influencing factor on the electricity demand in the early stages of the pandemic, before a lockdown. However, this influence wanes once the lockdown is implemented, signifying that residents have settled into their new lifestyles under lockdown. These observations point to a proactive response from Singaporean residents—who increasingly stayed in or performed more activities at home during the evenings, despite there being no government mandates—a finding that surprisingly extends across all demographics. Overall, our study enables policymakers to close the loop by utilizing residential electricity usage as a measure of community response during unprecedented and disruptive events, such as a pandemic.

scholarly journals The Aggregate and Residential Electricity Demand in Indonesia

2018 ◽  
Vol 26 (1) ◽  
pp. 1-20
Author(s):  
Muhammad Zulfizal Arnaz
Keyword(s):  
Fixed Effects ◽  
Dynamic Models ◽  
Electricity Consumption ◽  
Electricity Demand ◽  
Point Of View ◽  
Least Square ◽  
Price Elasticities ◽  
Long Run ◽  
Residential Electricity ◽  
Number Of Customers

This paper presents an empirical analysis on electricity demand in Indonesia applying a double-log demand equation for aggregate and residential. This proposes static and dynamic models employing fixed effects and bias-corrected least square dummy variable estimators, respectively. Particular attention is paid to the effects of income, price, and the numbers of customers. The paper concludes that all regressors function as the determinants of electricity consumption. Price elasticities are inelastically negative as expected, and further, profound inelastic for residential. Meanwhile, income level and the number of customers are quite elastic for both models. In addition, interregional analysis reports the differential impacts of the price on energy consumption between Java Bali and non-Java Bali regions, showing less responsiveness of consumption to price in Java Bali. The long-run estimates give information on modest values of price elasticities for aggregate and residential. From an energy policy point of view, electricity price would be moderately effective in achieving efficiency and conservation programs. On the other hand, it gives an economic rationale for tariff adjustment and region-based tariff restructuring.

Census Data Analysis and Visualization Using R Tool

2019 ◽  
pp. 137-161
Author(s):  
Veena Gadad ◽  
Sowmyarani C. N.
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Data Analysis ◽  
Data Visualization ◽  
Census Data ◽  
University Of California ◽  
Visual Images ◽  
Data Set ◽  
Resultant Data ◽  
Major Decision

As a result of increased usage of internet, a huge amount of data is collected from variety of sources like surveys, census, and sensors in internet of things. This resultant data is coined as big data and analysis of this leads to major decision making. Since the collected data is in raw form, it is difficult to understand inherent properties and it becomes just a liability if not analyzed, summarized, and visualized. Although text can be used to articulate the relation between facts and to explain the findings, presenting it in the form of tables and graphs conveys information effectively. Presentation of data using tools to create visual images in order to gain more insights into data is called as data visualization. Data analysis is processing and interpretation of data to discover useful information and to deduce certain inferences based on the values. This chapter concerns usage of R tool and understanding its effectiveness for data analysis and intelligent data visualization by experimenting on data set obtained from University of California Irvine Machine Learning Repository.

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