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https://doi.org/10.2139/ssrn.4669405
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Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/s10257-016-0332-9openalex-extracted
Implications of customer value perceptions for the design of electricity efficiency services in times of smart metering
inferredcites →https://doi.org/10.1007/s12053-008-9009-7openalex-extracted
Feedback on household electricity consumption: a tool for saving energy?
inferredcites →https://doi.org/10.1007/s12053-019-09777-zopenalex-extracted
The use of apps to promote energy saving: a study of smart meter–related feedback in the Netherlands
inferredcites →https://doi.org/10.1016/j.apenergy.2010.05.015openalex-extracted
Data-based method for creating electricity use load profiles using large amount of customer-specific hourly measured electricity use data
inferredcites →https://doi.org/10.1016/j.apenergy.2010.11.019openalex-extracted
Visualizing energy consumption activities as a tool for making everyday life more sustainable
inferredcites →https://doi.org/10.1016/j.apenergy.2013.01.059openalex-extracted
Energy consumption feedback devices’ impact evaluation on domestic energy use
inferredcites →https://doi.org/10.1016/j.apenergy.2013.03.048openalex-extracted
Creating an in-home display: Experimental evidence and guidelines for design
inferredcites →https://doi.org/10.1016/j.apenergy.2014.01.049openalex-extracted
Feedback on electricity usage for home energy management: A social experiment in a local village of cold region
inferredcites →https://doi.org/10.1016/j.apenergy.2014.01.060openalex-extracted
Effects of continuous feedback on households’ electricity consumption: Potentials and barriers
inferredcites →https://doi.org/10.1016/j.apenergy.2014.08.111openalex-extracted
Clustering analysis of residential electricity demand profiles
inferredcites →https://doi.org/10.1016/j.apenergy.2016.12.086openalex-extracted
Longitudinal analysis of normative energy use feedback on dormitory occupants
inferredcites →https://doi.org/10.1016/j.enbuild.2018.08.026openalex-extracted
Smart homes, home energy management systems and real-time feedback: Lessons for influencing household energy consumption from a Swedish field study
inferredcites →https://doi.org/10.1016/j.enbuild.2021.111376openalex-extracted
Development of electricity consumption profiles of residential buildings based on smart meter data clustering
inferredcites →https://doi.org/10.1016/j.enbuild.2023.113071openalex-extracted
A review of residential energy feedback studies
inferredcites →https://doi.org/10.1016/j.eneco.2012.11.022openalex-extracted
Smart meter devices and the effect of feedback on residential electricity consumption: Evidence from a natural experiment in Northern Ireland
inferredcites →https://doi.org/10.1016/j.eneco.2014.07.007openalex-extracted
Reducing household electricity demand through smart metering: The role of improved information about energy saving
inferredcites →https://doi.org/10.1016/j.eneco.2015.11.007openalex-extracted
Why does real-time information reduce energy consumption?
inferredcites →https://doi.org/10.1016/j.eneco.2018.03.008openalex-extracted
Information provision and energy consumption: Evidence from a field experiment
inferredcites →https://doi.org/10.1016/j.energy.2013.03.086openalex-extracted
Determinants of residential electricity consumption: Using smart meter data to examine the effect of climate, building characteristics, appliance stock, and occupants' behavior
inferredcites →https://doi.org/10.1016/j.energy.2015.06.130openalex-extracted
Using in-home displays to provide smart meter feedback about household electricity consumption: A randomized control trial comparing kilowatts, cost, and social norms
inferredcites →https://doi.org/10.1016/j.energy.2021.122500openalex-extracted
Machine learning approach to uncovering residential energy consumption patterns based on socioeconomic and smart meter data
inferredcites →https://doi.org/10.1016/j.enpol.2012.03.027openalex-extracted
Keeping energy visible? Exploring how householders interact with feedback from smart energy monitors in the longer term
inferredcites →https://doi.org/10.1016/j.enpol.2013.01.051openalex-extracted
Public attitudes to climate change and carbon mitigation—Implications for energy-associated behaviours
inferredcites →https://doi.org/10.1016/j.enpol.2013.05.109openalex-extracted
Information strategies and energy conservation behavior: A meta-analysis of experimental studies from 1975 to 2012
inferredcites →https://doi.org/10.1016/j.enpol.2014.12.008openalex-extracted
The question of energy reduction: The problem(s) with feedback
inferredcites →https://doi.org/10.1016/j.enpol.2017.05.002openalex-extracted
Persistence of the effects of providing feedback alongside smart metering devices on household electricity demand
inferredcites →https://doi.org/10.1016/j.enpol.2017.06.020openalex-extracted
What do people ‘learn by looking’ at direct feedback on their energy consumption? Results of a field study in Southern France
inferredcites →https://doi.org/10.1016/j.enpol.2017.07.037openalex-extracted
Vulnerability and resistance in the United Kingdom's smart meter transition
inferredcites →https://doi.org/10.1016/j.enpol.2020.111450openalex-extracted
How to effectively implement an incentive-based residential electricity demand response policy? Experience from large-scale trials and matching questionnaires
inferredcites →https://doi.org/10.1016/j.enpol.2020.111610openalex-extracted
Smart meters and consumer behaviour: Insights from the empirical literature
inferredcites →https://doi.org/10.1016/j.enpol.2020.111657openalex-extracted
Reducing household electricity consumption during evening peak demand times: Evidence from a field experiment
inferredcites →https://doi.org/10.1016/j.enpol.2020.111742openalex-extracted
Appliance-specific feedback and social comparisons: Evidence from a field experiment on energy conservation
inferredcites →https://doi.org/10.1016/j.envsci.2020.07.003openalex-extracted
Can smart energy information interventions help householders save electricity? A SVR machine learning approach
inferredcites →https://doi.org/10.1016/j.erss.2016.12.011openalex-extracted
Between the technology acceptance model and sustainable energy technology acceptance model: Investigating smart meter acceptance in the United States
inferredcites →https://doi.org/10.1016/j.erss.2017.04.008openalex-extracted
Making ‘Smart Meters’ smarter? Insights from a behavioural economics pilot field experiment in Copenhagen, Denmark
inferredcites →https://doi.org/10.1016/j.erss.2018.08.025openalex-extracted
A synthetic view of acceptance and engagement with smart meters in the United States
inferredcites →https://doi.org/10.1016/j.erss.2019.05.003openalex-extracted
The social dimensions of smart meters in the United States: Demographics, privacy, and technology readiness
inferredcites →https://doi.org/10.1016/j.erss.2019.101238openalex-extracted
Energy and behaviour at home: A review of intervention methods and practices
inferredcites →https://doi.org/10.1016/j.erss.2020.101430openalex-extracted
From intention to action: Habits, feedback and optimizing energy consumption in South Korea
inferredcites →https://doi.org/10.1016/j.erss.2022.102770openalex-extracted
How do households perceive electricity consumption? Evidence from smart metering and survey data in South Korea
inferredcites →https://doi.org/10.1016/j.jclepro.2016.12.031openalex-extracted
Smart meter deployment in Europe: A comparative case study on the impacts of national policy schemes
inferredcites →https://doi.org/10.1016/j.jeem.2017.09.001openalex-extracted
Do energy retrofits work? Evidence from commercial and residential buildings in Phoenix
inferredcites →https://doi.org/10.1016/j.jenvp.2019.04.007openalex-extracted
The role of environmental attitude in the efficacy of smart-meter-based feedback interventions
inferredcites →https://doi.org/10.1016/j.jenvp.2020.101459openalex-extracted
Feedback devices help only environmentally concerned people act pro-environmentally over time
inferredcites →https://doi.org/10.1016/j.jup.2022.101446openalex-extracted
Predicting residential electricity consumption patterns based on smart meter and household data: A case study from the Republic of Ireland
inferredcites →https://doi.org/10.1016/j.resconrec.2017.07.004openalex-extracted
Impact of information feedback on residential electricity demand in China
inferredcites →https://doi.org/10.1016/j.reseneeco.2018.02.001openalex-extracted
Information acquisition and residential electricity consumption: Evidence from a field experiment
inferredcites →https://doi.org/10.1016/j.reseneeco.2020.101201openalex-extracted
The impact of information-based interventions on conservation behavior: A meta-analysis
inferredcites →https://doi.org/10.1016/j.rser.2013.03.023openalex-extracted
A review of electric load classification in smart grid environment
inferredcites →https://doi.org/10.1016/j.rser.2020.110187openalex-extracted
A systematic review on feedback research for residential energy behavior change through mobile and web interfaces
inferredcites →https://doi.org/10.1016/j.tej.2014.11.002openalex-extracted
Smart Meters and Federal Law: What Is the Role of Federal Law in the United States in the Deployment of Smart Electricity Metering?
inferredcites →https://doi.org/10.1037/a0039650openalex-extracted
The effects of feedback on energy conservation: A meta-analysis.
inferredcites →https://doi.org/10.1037/xap0000042openalex-extracted
Advertising energy saving programs: The potential environmental cost of emphasizing monetary savings.
inferredcites →https://doi.org/10.1038/s41560-018-0282-1openalex-extracted
Real-time feedback promotes energy conservation in the absence of volunteer selection bias and monetary incentives
inferredcites →https://doi.org/10.1038/s41560-021-00866-xopenalex-extracted
A multi-country meta-analysis on the role of behavioural change in reducing energy consumption and CO2 emissions in residential buildings
inferredcites →https://doi.org/10.1038/s41592-019-0686-2openalex-extracted
SciPy 1.0: fundamental algorithms for scientific computing in Python
inferredcites →https://doi.org/10.1057/s41599-019-0254-5openalex-extracted
Making the smart meter social promotes long-term energy conservation
inferredcites →https://doi.org/10.1073/pnas.1401880112openalex-extracted
Nonprice incentives and energy conservation
inferredcites →https://doi.org/10.1080/09613218.2010.492660openalex-extracted
Smart metering: what potential for householder engagement?
inferredcites →https://doi.org/10.1080/09613218.2020.1807896openalex-extracted
Getting smarter about household energy: the who and what of demand for smart meters
inferredcites →https://doi.org/10.1080/17512549.2017.1354782openalex-extracted
Smart metering and systems for low-energy households: challenges, issues and benefits
inferredcites →https://doi.org/10.1086/697050openalex-extracted
The Incentive to Overinvest in Energy Efficiency: Evidence from Hourly Smart-Meter Data
inferredcites →https://doi.org/10.1088/1748-9326/ac71b8openalex-extracted
Effectiveness of behavioural interventions to reduce household energy demand: a scoping review
inferredcites →https://doi.org/10.1088/1757-899x/105/1/012020openalex-extracted
Analysis Clustering of Electricity Usage Profile Using K-Means Algorithm
inferredcites →https://doi.org/10.1093/jleo/ews020openalex-extracted
Evidence from Two Large Field Experiments that Peer Comparison Feedback Can Reduce Residential Energy Usage
inferredcites →https://doi.org/10.1108/sasbe-05-2021-0080openalex-extracted
What improves smart meters' implementation? A statistical meta-analysis on smart meters' acceptance
inferredcites →https://doi.org/10.1109/access.2020.2977060openalex-extracted
An Empirical Study of the Acceptance of IoT-Based Smart Meter in Malaysia: The Effect of Electricity-Saving Knowledge and Environmental Awareness
inferredcites →https://doi.org/10.1109/ciasg.2011.5953331openalex-extracted
Methods for generating TLPs (typical load profiles) for smart grid-based energy programs
inferredcites →https://doi.org/10.1109/eem.2012.6254761openalex-extracted
Electricity customer characterization based on different representative load curves
inferredcites →https://doi.org/10.1109/tsg.2013.2278477openalex-extracted
Household Energy Consumption Segmentation Using Hourly Data
inferredcites →https://doi.org/10.1111/ijcs.12603openalex-extracted
Electricity awareness and consumer demand for information
inferredcites →https://doi.org/10.1111/j.1467-9280.2007.01917.xopenalex-extracted
The Constructive, Destructive, and Reconstructive Power of Social Norms
inferredcites →https://doi.org/10.1111/j.1470-6431.2010.00967.xopenalex-extracted
Feedback on household electricity consumption: learning and social influence processes
inferredcites →https://doi.org/10.1145/2531602.2531626openalex-extracted
Understanding factors of successful engagement around energy consumption between and among households
inferredcites →https://doi.org/10.1162/003355300554917openalex-extracted
Pay Enough or Don't Pay at All*
inferredcites →https://doi.org/10.1257/aer.104.4.1417openalex-extracted
Knowledge is (Less) Power: Experimental Evidence from Residential Energy Use
inferredcites →https://doi.org/10.1287/mnsc.2016.2646openalex-extracted
Overcoming Salience Bias: How Real-Time Feedback Fosters Resource Conservation
inferredcites →https://doi.org/10.17700/jai.2015.6.3.196openalex-extracted
Comparison of K-Means and Fuzzy C-Means Algorithms on Different Cluster Structures
inferredcites →https://doi.org/10.18489/sacj.v32i2.845openalex-extracted
Clustering Residential Electricity Consumption Data to Create Archetypes that Capture Household Behaviour in South Africa
inferredcites →https://doi.org/10.2139/ssrn.1434950openalex-extracted
Evidence from Two Large Field Experiments that Peer Comparison Feedback can Reduce Residential Energy Usage
inferredcites →https://doi.org/10.22696/jkiaebs.20210045openalex-extracted
Electricity Consumption Pattern Analysis of Nationwide Apartment using Clustering Method based on Open Data
inferredcites →https://doi.org/10.23919/splitech55088.2022.9854217openalex-extracted
The potential of Demand Response as a tool for decarbonization in the energy transition
inferredcites →https://doi.org/10.31695/ijasre.2021.34050openalex-extracted
Comprehensive Review of K-Means Clustering Algorithms
inferredcites →https://doi.org/10.3386/w18344openalex-extracted
Knowledge is (Less) Power: Experimental Evidence from Residential Energy Use
inferredcites →https://doi.org/10.3389/fenvs.2021.626890openalex-extracted
Analysis of Information Feedback on Residential Energy Conservation and the Implications: The Case of China
inferredcites →https://doi.org/10.3390/electronics9081295openalex-extracted
The k-means Algorithm: A Comprehensive Survey and Performance Evaluation
inferredcites →https://doi.org/10.3390/en11040859openalex-extracted
Electricity Consumption Clustering Using Smart Meter Data
inferredcites →https://doi.org/10.3390/ijerph19127222openalex-extracted
Impact of Climate Change Beliefs on Youths’ Engagement in Energy-Conservation Behavior: The Mediating Mechanism of Environmental Concerns
inferredcites →https://doi.org/10.3390/su12208590openalex-extracted
Determining the Relationship between Residential Electricity Consumption and Factors: Case of Seoul
inferredcites →https://doi.org/10.48550/arxiv.1201.0490openalex-extracted
Scikit-learn: Machine Learning in Python
inferredcites →https://doi.org/10.5547/01956574.34.1.4openalex-extracted
Real-time Feedback and Electricity Consumption: A Field Experiment Assessing the Potential for Savings and Persistence
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