Tools for Individuals: Difference between revisions

*'''Modeling of end-use energy consumption in the residential sector: A review of modeling techniques (2009)'''<ref>{{Cite journal|last=Swan|first=Lukas G.|last2=Ugursal|first2=V. Ismet|date=2009-102019|title=Modeling of end-use energy consumption in the residential sector: A review of modeling techniques|url=https://linkinghub.elsevier.com/retrieve/pii/S1364032108001949|journal=Renewable and Sustainable Energy Reviews|language=en|volume=13|issue=8|pages=1819–1835|doi=10.1016/j.rser.2008.09.033|via=}}</ref>: a review of the various modeling techniques used for modeling residential sector energy consumption.

*'''Quantifying carbon footprint reduction opportunities for US households and communities (2011)'''<ref>{{Cite journal|last=Jones|first=Christopher M.|last2=Kammen|first2=Daniel M.|date=2011-05|title=Quantifying Carbon Footprint Reduction Opportunities for U.S. Households and Communities|url=https://pubs.acs.org/doi/10.1021/es102221h|journal=Environmental Science & Technology|language=en|volume=45|issue=9|pages=4088–4095|doi=10.1021/es102221h|issn=0013-936X|via=}}</ref> ''':''' this study uses consumption-based life cycle accounting techniques to quantify the carbon footprints of typical U.S. households and to quantify greenhouse gas and financial savings from 13 potential mitigation actions. [https://pubs.acs.org/doi/abs/10.1021/es102221h Available here.]

*'''Residential load scheduling with renewable generation in the smart grid: A reinforcement learning approach (2018)'''<ref>{{Cite journal|last=Remani|first=T.|last2=Jasmin|first2=E.A.|last3=Ahamed|first3=T.P. Imthias|date=2019-09|title=Residential Load Scheduling With Renewable Generation in the Smart Grid: A Reinforcement Learning Approach|url=https://ieeexplore.ieee.org/document/8426048/|journal=IEEE Systems Journal|volume=13|issue=3|pages=3283–3294|doi=10.1109/JSYST.2018.2855689|issn=1932-8184|via=}}</ref>: this paper proposes of a comprehensive, Reinforcement Learning-based model with implementable solution considering consumer comfort, stochastic renewable power, and tariffs. [https://ieeexplore.ieee.org/document/8426048 Available here.]

*'''Making carbon pricing work for citizens (2018)''' <ref>{{Cite journal|last=Klenert|first=David|last2=Mattauch|first2=Linus|last3=Combet|first3=Emmanuel|last4=Edenhofer|first4=Ottmar|last5=Hepburn|first5=Cameron|last6=Rafaty|first6=Ryan|last7=Stern|first7=Nicholas|date=2018-08|title=Making carbon pricing work for citizens|url=http://www.nature.com/articles/s41558-018-0201-2|journal=Nature Climate Change|language=en|volume=8|issue=8|pages=669–677|doi=10.1038/s41558-018-0201-2|issn=1758-678X|via=}}</ref>: this paper synthesizes findings regarding the optimal use of carbon revenues from both traditional economic analyses and studies in behavioural and political science. [https://www.nature.com/articles/s41558-018-0201-2 Available here.]

*'''The future is now: reducing psychological distance to increase public engagement with climate change (2017)'''<ref>{{Cite journal|last=Jones|first=Charlotte|last2=Hine|first2=Donald W.|last3=Marks|first3=Anthony D. G.|date=2017-02|title=The Future is Now: Reducing Psychological Distance to Increase Public Engagement with Climate Change: Reducing Psychological Distance|url=http://doi.wiley.com/10.1111/risa.12601|journal=Risk Analysis|language=en|volume=37|issue=2|pages=331–341|doi=10.1111/risa.12601|via=}}</ref> : a study that looks at climate change communication intervention and how it can increase public engagement by reducing the psychological distance of climate change. [https://onlinelibrary.wiley.com/doi/abs/10.1111/risa.12601 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|via=}}</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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