# Machine Learning Approach to Uncovering Residential Energy Consumption Patterns Based on Socioeconomic and Smart Meter Data

arXiv: https://arxiv.org/abs/2104.05154 · HTML: https://mlsanity.com/paper/2104.05154/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 48 references that are in the map.
- First posted on arXiv 12 Apr 2021.

## Builds on (most cited first, 4 of 4)

- [Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges](https://mlsanity.com/paper/1802.04117/) (2018)
- [A Sub-Quadratic Exact Medoid Algorithm](https://mlsanity.com/paper/1605.06950/) (2016)
- [Leveraging Socioeconomic Information and Deep Learning for Residential Load Pattern Prediction](https://mlsanity.com/paper/1907.05695/) (2019)
- [The Role of Big Data on Smart Grid Transition](https://mlsanity.com/paper/1703.02497/) (2017)

## Built on by (most cited first, 4 of 4)

- [Hybrid Transformer-RNN Architecture for Household Occupancy Detection Using Low-Resolution Smart Meter Data](https://mlsanity.com/paper/2308.14114/) (2023)
- [Learning and Generating Diverse Residential Load Patterns Using GAN with Weakly-Supervised Training and Weight Selection](https://mlsanity.com/paper/2504.14300/) (2025)
- [Cross-household Transfer Learning Approach with LSTM-based Demand Forecasting](https://mlsanity.com/paper/2602.14267/) (2026)
- [Uncovering Residential PV-EV Co-Adoption from Smart-Meter Data: Load Archetypes and Detection for Demand-Side Planning](https://mlsanity.com/paper/2609.28578/) (2026)

## Abstract (arXiv)

The smart meter data analysis contributes to better planning and operations for the power system. This study aims to identify the drivers of residential energy consumption patterns from the socioeconomic perspective based on the consumption and demographic data using machine learning. We model consumption patterns by representative loads and reveal the relationship between load patterns and socioeconomic characteristics. Specifically, we analyze the real-world smart meter data and extract load patterns by clustering in a robust way. We further identify the influencing socioeconomic attributes on load patterns to improve our method's interpretability. The relationship between consumers' load patterns and selected socioeconomic features is characterized via machine learning models. The findings are as follows. (1) Twelve load clusters, consisting of six for weekdays and six for weekends, exhibit a diverse pattern of lifestyle and a difference between weekdays and weekends. (2) Among various socioeconomic features, age and education level are suggested to influence the load patterns. (3) Our proposed analytical model using feature selection and machine learning is proved to be more effective than XGBoost and conventional neural network model in mapping the relationship between load patterns and socioeconomic features.