Machine learning approach to uncovering residential energy consumption patterns based on socioeconomic and smart meter data

Author:

Tang Wenjun,Wang Hao,Lee Xian-Long,Yang Hong-Tzer

Funder

Ministry of Science and Technology, Taiwan

Publisher

Elsevier BV

Subject

General Energy,Pollution,Mechanical Engineering,Building and Construction,Electrical and Electronic Engineering,Industrial and Manufacturing Engineering,Civil and Structural Engineering

Reference42 articles.

1. Forecasting the usage of household appliances through power meter sensors for demand management in the smart grid;Barbato,2011

2. Data driven prediction models of energy use of appliances in a low-energy house;Candanedo;Energy Build,2017

3. Deep learning for household load forecasting—a novel pooling deep RNN;Shi;IEEE Transactions on Smart Grid,2017

4. Review of smart meter data analytics: applications, methodologies, and challenges;Wang;IEEE Transactions on Smart Grid,2018

5. Clustering load profiles for demand response applications;Lin;IEEE Transactions on Smart Grid,2019

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