Predicting Levels of Household Electricity Consumption in Low-Access Settings-Reference-Cited by-同舟云学术

Predicting Levels of Household Electricity Consumption in Low-Access Settings

Published:2022-01 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
language:
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Author:

Fobi Simone¹,Mugyenyi Joel²,Williams Nathaniel J.³,Modi Vijay¹,Taneja Jay²

Affiliation:

1. Columbia University,Department of Mechanical Engineering

2. University of Massachusetts Amherst,Department of Electrical & Computer Engineering

3. Rochester Institute of Technology,Golisano Institute for Sustainability

Funder

Rockefeller Foundation

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9706406/9706408/09706653.pdf?arnumber=9706653

Reference45 articles.

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2. Modeling of end-use energy consumption in the residential sector: A review of modeling techniques

3. Devi Parikh, and Dhruv Batra. Grad-cam: Visual explanations from deep networks via gradient-based localization;selvaraju;Proceedings of the IEEE International Conference on Computer Vision,2017

4. A Novel CNN-GRU-Based Hybrid Approach for Short-Term Residential Load Forecasting

5. National electricity planning in settings with low pre-existing grid coverage: Development of a spatial model and case study of Kenya

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