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. |
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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== Machine Learning Application Areas == |
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== Background Readings == |
== Background Readings == |
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*'''Predictive segmentation of energy consumers (2016)'''<ref>{{Cite journal|last=Albert|first=Adrian|last2=Maasoumy|first2=Mehdi|date=2016-09|title=Predictive segmentation of energy consumers|url=https://linkinghub.elsevier.com/retrieve/pii/S0306261916307334|journal=Applied Energy|language=en|volume=177|pages=435–448|doi=10.1016/j.apenergy.2016.05.128}}</ref>: this paper proposes a predictive segmentation technique for identifying sub-groups in a large population that are both homogeneous with respect to certain patterns in customer attributes, and predictive with respect to a desired outcome. [https://www.researchgate.net/profile/Mehdi_Maasoumy/publication/303895476_PredictiveSegmentationOfEnergyConsumers/links/575b0b9e08aed884620d990a/PredictiveSegmentationOfEnergyConsumers.pdf Available here.] |
*'''Predictive segmentation of energy consumers (2016)'''<ref>{{Cite journal|last=Albert|first=Adrian|last2=Maasoumy|first2=Mehdi|date=2016-09|title=Predictive segmentation of energy consumers|url=https://linkinghub.elsevier.com/retrieve/pii/S0306261916307334|journal=Applied Energy|language=en|volume=177|pages=435–448|doi=10.1016/j.apenergy.2016.05.128}}</ref>: this paper proposes a predictive segmentation technique for identifying sub-groups in a large population that are both homogeneous with respect to certain patterns in customer attributes, and predictive with respect to a desired outcome. [https://www.researchgate.net/profile/Mehdi_Maasoumy/publication/303895476_PredictiveSegmentationOfEnergyConsumers/links/575b0b9e08aed884620d990a/PredictiveSegmentationOfEnergyConsumers.pdf Available here.] |
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== Online |
== Online Courses and Course Materials == |
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''Under construction'' |
''Under construction'' |
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=== Major societies and organizations === |
=== Major societies and organizations === |
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== Libraries and Tools == |
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*[https://www.tmrow.com/ '''Tomorrow Project'''] - Calculates impact of your climate behaviors by using ML to read your data. |
*[https://www.tmrow.com/ '''Tomorrow Project'''] - Calculates impact of your climate behaviors by using ML to read your data. |
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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. |
* [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. |
* [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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== Selected problems == |
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''Under construction'' |
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== References == |
== References == |
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