Tools for Individuals: Difference between revisions
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Individuals and households constantly make decisions that affect their carbon footprint, and many wish to reduce their impact. ML can help quantify the climate impact of consumer products and actions, estimate the benefits resulting from personal behavior change, provide appliance-level residential energy use data, identify households with high potential for efficiency gain, and optimize appliances to operate when low-carbon electricity is available. ML can also help effectively inform people and provide them constructive opportunities by modeling consumer behavior and simplifying information on climate-relevant laws and policies. |
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TODO: add context regarding contribution to emissions, connection to ML, and selected readings |
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== Data == |
== Data == |
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Many of the existing datasets involve individual or household level information regarding energy consumption, transportation and consumer habits. Some useful datasets include: |
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* [https://data.world/footprint Global Footprint Data]: Ecological Footprint data for different countries and different levels of granularity. |
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* [https://archive.ics.uci.edu/ml/datasets/individual+household+electric+power+consumption Individual household electric power consumption data set.]: Measurements of electric power consumption in one household with a one-minute sampling rate over a period of almost 4 years. |
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* [https://openei.org/datasets/dataset/residential-energy-consumption-survey-results-total-energy-consumption-expenditures-and-intensiti Residential energy use survey results]: results of a 2005 national survey that collects residential energy-related data for over 4,381 households in the United States. |
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* [https://openei.org/datasets/dataset/commercial-and-residential-hourly-load-profiles-for-all-tmy3-locations-in-the-united-states Commercial and Residential Hourly Load Profiles]: contains hourly load profile data for 16 commercial building types and residential buildings in the United States. |
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* [https://openei.org/datasets/dataset/doe-buildings-performance-database-sample-residential-data Buildings Performance Database, sample residential data]: a non-proprietary subset of the Department of Energy's Buildings Performance Database. |
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== Methods and Software == |
== Methods and Software == |
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* [https://www.tmrow.com/ Tomorrow Project] - Calculates impact of your climate behaviors by using ML to read your data. |
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* [https://www.watttime.org/ WattTime] - Predicts marginal emissions cost of energy consumption in real time. |
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* [http://data.footprintnetwork.org/#/ Ecological Footprint Explorer] - an interactive tool to explore the Ecological Footprint and biocapacity for over 200 countries and regions, updated annually. |
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== Recommended Readings == |
== Recommended Readings == |
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=== Overall === |
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* K. Williamson, A. [https://rare.org/wp-content/uploads/2019/02/2018-CCNBC-Report.pdf Climate Change Needs Behavior Change: Making the case for behavioral solutions to reduce global warming.] 2018. |
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=== Individual and Household Consumption === |
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* Swan, L.J. et al., [https://www.sciencedirect.com/science/article/pii/S1364032108001949 Modeling of end-use energy consumption in the residential sector: A review of modeling techniques]. (2009) |
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* Jones, C.M. et al., [https://pubs.acs.org/doi/abs/10.1021/es102221h Quantifying carbon footprint reduction opportunities for US households and communities.] (2011) |
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* Remani, T. et. al., [https://ieeexplore.ieee.org/document/8426048 Residential load scheduling with renewable generation in the smart grid: A reinforcement learning approach.] (2018) |
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=== Behavior Change === |
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* Klenert, D. et al., [https://www.nature.com/articles/s41558-018-0201-2 Making carbon pricing work for citizens.] (2018) |
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* Jones, C. et al., [https://onlinelibrary.wiley.com/doi/abs/10.1111/risa.12601 The future is now: reducing psychological distance to increase public engagement with climate change.] (2017) |
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* Albert, A. and Maasoumy, M. [https://www.researchgate.net/profile/Mehdi_Maasoumy/publication/303895476_PredictiveSegmentationOfEnergyConsumers/links/575b0b9e08aed884620d990a/PredictiveSegmentationOfEnergyConsumers.pdf Predictive segmentation of energy consumers.] (2016) |
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== Community == |
== Community == |
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Revision as of 17:46, 25 August 2020
Individuals and households constantly make decisions that affect their carbon footprint, and many wish to reduce their impact. ML can help quantify the climate impact of consumer products and actions, estimate the benefits resulting from personal behavior change, provide appliance-level residential energy use data, identify households with high potential for efficiency gain, and optimize appliances to operate when low-carbon electricity is available. ML can also help effectively inform people and provide them constructive opportunities by modeling consumer behavior and simplifying information on climate-relevant laws and policies.
Data
Many of the existing datasets involve individual or household level information regarding energy consumption, transportation and consumer habits. Some useful datasets include:
- Global Footprint Data: Ecological Footprint data for different countries and different levels of granularity.
- Individual household electric power consumption data set.: Measurements of electric power consumption in one household with a one-minute sampling rate over a period of almost 4 years.
- Residential energy use survey results: results of a 2005 national survey that collects residential energy-related data for over 4,381 households in the United States.
- Commercial and Residential Hourly Load Profiles: contains hourly load profile data for 16 commercial building types and residential buildings in the United States.
- Buildings Performance Database, sample residential data: a non-proprietary subset of the Department of Energy's Buildings Performance Database.
Methods and Software
- Tomorrow Project - Calculates impact of your climate behaviors by using ML to read your data.
- WattTime - Predicts marginal emissions cost of energy consumption in real time.
- Ecological Footprint Explorer - an interactive tool to explore the Ecological Footprint and biocapacity for over 200 countries and regions, updated annually.
Recommended Readings
Overall
- K. Williamson, A. Climate Change Needs Behavior Change: Making the case for behavioral solutions to reduce global warming. 2018.
Individual and Household Consumption
- Swan, L.J. et al., Modeling of end-use energy consumption in the residential sector: A review of modeling techniques. (2009)
- Jones, C.M. et al., Quantifying carbon footprint reduction opportunities for US households and communities. (2011)
- Remani, T. et. al., Residential load scheduling with renewable generation in the smart grid: A reinforcement learning approach. (2018)
Behavior Change
- Klenert, D. et al., Making carbon pricing work for citizens. (2018)
- Jones, C. et al., The future is now: reducing psychological distance to increase public engagement with climate change. (2017)
- Albert, A. and Maasoumy, M. Predictive segmentation of energy consumers. (2016)