Estimating residential energy consumption in metropolitan areas: A microsimulation approach

Zhang, Wenwen; Robinson, Caleb; Guhathakurta, Subhrajit; Garikapati, Venu; Dilkina, Bistra; Brown, Marilyn A.; Pendyala, Ram M.

doi:10.1016/j.energy.2018.04.161

Cited by 40 publications

(22 citation statements)

References 33 publications

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“…Based on energy data disclosed by residents of New York City, Kontokosta and colleagues used various ML methods to predict the energy use of the city's 1.1 million buildings [448], analyzed the effect of energy disclosure on the demand [610], and developed a system for ranking buildings based on energy efficiency [611]. Zhang et al [879] [689].…”

Section: Modeling Energy Use Across Buildingsmentioning

confidence: 99%

Tackling Climate Change with Machine Learning

et al. 2022

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Climate change is one of the greatest challenges facing humanity, and we, as machine learning (ML) experts, may wonder how we can help. Here we describe how ML can be a powerful tool in reducing greenhouse gas emissions and helping society adapt to a changing climate. From smart grids to disaster management, we identify high impact problems where existing gaps can be filled by ML, in collaboration with other fields. Our recommendations encompass exciting research questions as well as promising business opportunities. We call on the ML community to join the global effort against climate change.

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Section: Modeling Energy Use Across Buildingsmentioning

confidence: 99%

Tackling Climate Change with Machine Learning

et al. 2022

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“…The tension between the need of case-specific studies and scalable solutions for deep decarbonizationparticularly exacerbated in the research on buildingscould be partly alleviated by ML applications that provide new tools to identify and transfer individual solutions between different contexts (Ma & Cheng, 2017;Mocanu et al, 2016;Zhang et al, 2018). ML methods can accelerate spatially explicit assessments of the energy and retrofit potential to entire building stocks, by imputing building level data where none is available (Zhang et al, 2018). Going beyond micro-optimization with large-scale and precise assessments is crucial to leverage the full potential of ML for mitigating the emissions from the building sector.…”

Section: Infrastructure Managementmentioning

confidence: 99%

Machine learning for geographically differentiated climate change mitigation in urban areas

Milojevic-Dupont

Creutzig

2021

Sustainable Cities and Society

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Artificial intelligence and machine learning are transforming scientific disciplines, but their full potential for climate change mitigation remains elusive. Here, we conduct a systematic review of applied machine learning studies that are of relevance for climate change mitigation, focusing specifically on the fields of remote sensing, urban transportation, and buildings. The relevant body of literature spans twenty years and is growing exponentially. We show that the emergence of big data and machine learning methods enables climate solution research to overcome generic recommendations and provide policy solutions at urban, street, building and household scale, adapted to specific contexts, but scalable to global mitigation potentials. We suggest a meta-algorithmic architecture and framework for using machine learning to optimize urban planning for accelerating, improving and transforming urban infrastructure provision. climate change mitigation a distant goal, but also making comparisons between local-scale studies difficult (Lamb, Callaghan, Creutzig, Khosla, & Minx, 2018). The IPCC's AR5 reports knowledge gaps on urban climate action (IPCC, 2014): there is too little understanding of the magnitude of the emissions reductions from altering urban form, and emissions savings from integrated infrastructure and land use planning. New analyses are required both to understand relationships (

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“…Zhang et al [51] have researched forecasting the total domestic energy demand within the United States. In which they used the data from the Residential Energy Consumption Survey (RECS), Public Use Microdata Sample (PUMS), and American Community Survey (ACS) data as inputs.…”

Section: Domestic Energy Consumptionmentioning

confidence: 99%

Energy Consumption and Price Forecasting Through Data-Driven Analysis Methods: A Review

Patel

Shah

2021

SN COMPUT. SCI.

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Prediction of energy consumption and price is crucial in formatting policies related to the global energy market, demand, and supply. Data-driven analysis methods are giving rise to innovations in the world energy sector, including energy finance and economics. This paper has critically evaluated expand writings committed to Energy finance and economics applications of data-driven analysis. This paper comes up with an extensive view of state of the art in the area, which is already discussed with a different procedure. This review recognizes the applications of data-driven analysis methods in various areas such as forecasting domestic, nationwide, and transport energy consumption and price forecasting of multiple commodities, including crude oil, natural gas, and electricity. We have investigated certain research papers and given conclusion based on their researched and proposed model's prediction results and accuracies in respective areas. Our study suggests that Artificial Neural Network (ANN), Support Vector Machine (SVM), and other proposed neural network models are the most effective methods among other statistical and machine learning methods.

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Estimating residential energy consumption in metropolitan areas: A microsimulation approach

Abstract: Highlights Machine learning algorithms can address energy consumption data gaps Residential Energy Consumption Survey matched with synthesized households to generate neighborhood energy footprints Validation uses zip code power consumption data provided by the energy provider

Cited by 40 publications

References 33 publications

Tackling Climate Change with Machine Learning

Tackling Climate Change with Machine Learning

Machine learning for geographically differentiated climate change mitigation in urban areas

Energy Consumption and Price Forecasting Through Data-Driven Analysis Methods: A Review

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