Hidden inequality in household electricity consumption: Measurement and determinants based on large-scale smart meter data

2021 ◽  
pp. 101739
Author(s):  
Haitao Chen ◽  
Bin Zhang ◽  
Zhaohua Wang
Keyword(s):  
Large Scale ◽  
Electricity Consumption ◽  
Smart Meter

Occupancy detection of residential buildings using smart meter data: A large-scale study

2019 ◽  
Vol 183 ◽  
pp. 195-208 ◽  
Author(s):  
Rouzbeh Razavi ◽  
Amin Gharipour ◽  
Martin Fleury ◽  
Ikpe Justice Akpan
Keyword(s):  
Large Scale ◽  
Residential Buildings ◽  
Smart Meter ◽  
Large Scale Study ◽  
Occupancy Detection

scholarly journals Electricity Consumption Clustering Using Smart Meter Data

Energies ◽  
2018 ◽  
Vol 11 (4) ◽  
pp. 859 ◽  
Author(s):  
Alexander Tureczek ◽  
Per Nielsen ◽  
Henrik Madsen
Keyword(s):  
Electricity Consumption ◽  
Smart Meter

scholarly journals A Dedicated Mixture Model for Clustering Smart Meter Data: Identification and Analysis of Electricity Consumption Behaviors

Energies ◽  
2017 ◽  
Vol 10 (10) ◽  
pp. 1446 ◽  
Author(s):  
Fateh Melzi ◽  
Allou Same ◽  
Mohamed Zayani ◽  
Latifa Oukhellou
Keyword(s):  
Mixture Model ◽  
Electricity Consumption ◽  
Smart Meter

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

2014 ◽  
Vol 6 (4) ◽  
pp. 207-238 ◽  
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)

scholarly journals Electricity Consumption and Operating Duration of Sensor Network in the Large-Scale Paddy Fields

2018 ◽  
Vol 27 (4) ◽  
pp. 64-74
Author(s):  
Kazutoshi Tanimoto ◽  
Manabu Horiguchi ◽  
Teruaki Nanseki ◽  
Takashi Sasaki ◽  
Dongpo Li
Keyword(s):  
Sensor Network ◽  
Large Scale ◽  
Electricity Consumption ◽  
Paddy Fields

scholarly journals Smart metering system data analytics platform using multicore edge computing

2021 ◽  
Vol 10 (1) ◽  
pp. 11
Author(s):  
Juan C. Olivares-Rojas ◽  
Enrique Reyes-Archundia ◽  
José A. Gutiérrez-Gnecchi ◽  
Ismael Molina-Moreno ◽  
Adriana C. Téllez-Anguiano ◽  
...  
Keyword(s):  
Internet Of Things ◽  
Smart Grid ◽  
Data Processing ◽  
Data Analytics ◽  
Electricity Consumption ◽  
Edge Computing ◽  
Smart Meter ◽  
Smart Metering ◽  
Smart Meters ◽  
System Data

The smart grid revolution has only been possible, thanks to the development and proliferation of smart meters. The increasingly growing computing capabilities for Internet of Things devices have made it possible for data to be processed directly from the devices where it is produced; this has been called edge computing. Edge computing is allowing the smart grid to become increasingly intelligent to solve problems that make electricity consumption more efficient and environmentally friendly. This work presents the implementation of a smart metering system that allows data analytics using a multiprocessing architecture directly on the smart meter. The results show that the development of smart meters with data analytics capabilities at the edge is a reality today, and the use of multiprocessing permits the improvement of data processing.

scholarly journals Interrelated Patterns of Electricity, Gas, and Water Consumption in Large-Scale Buildings

2020 ◽  
Author(s):  
Ali Movahedi ◽  
Sybil Derrible
Keyword(s):  
New York ◽  
Water Consumption ◽  
Large Scale ◽  
Electricity Consumption ◽  
Gradient Boosting ◽  
Effective Strategies ◽  
Extreme Gradient Boosting ◽  
Gas Consumption ◽  
Local Law ◽  
Key Resources

As cities keep growing worldwide, so does the demand for key resources such as energy (electricity and gas) and water that residents consume. Meeting the demand for these resources can be challenging and requires an understanding of their consumptions patterns. In this work, we apply XGBoost (Extreme Gradient Boosting) to predict and analyze water and energy consumption in large-scale buildings in New York City. For this, the New York City’s local law 84 extensive dataset was merged with the Primary Land Use Tax Lot Output (PLUTO) dataset as well as with other socio-economic databases. Specifically, we developed three models: electricity, gas, and water consumption. Seven major lessons were learnt in terms of interrelationships between electricity, gas, and water consumption. In particular, water and gas consumption are highly interrelated with one another (often because gas is used for water heating). Furthermore, electricity consumption is affected by building type, and electricity and water consumption are particularly interrelated in nonresidential buildings. Overall, the knowledge gained from the models and from the SHAP analysis can help planners, engineers, and policymakers develop more effective strategies and help them manage the demand for energy and water in large-scale buildings.

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