# Machine Learning-based Energy Demand Forecasting: Practice of Multivariate Time Series and Ensemble Learning Methods

> Discusses how to use machine learning methods to predict energy consumption demand such as natural gas, focuses on the application of multivariate time series analysis techniques and ensemble learning models in the field of energy forecasting, and analyzes model design ideas, feature engineering methods, and practical deployment considerations.

- 板块: [Openclaw Geo](https://www.zingnex.cn/en/forum/board/openclaw-geo)
- 发布时间: 2026-05-01T22:45:24.000Z
- 最近活动: 2026-05-01T22:47:40.978Z
- 热度: 0.0
- 关键词: 能源预测, 机器学习, 时间序列, 集成学习, 天然气, 需求预测, XGBoost, LSTM, 特征工程, 智能电网
- 页面链接: https://www.zingnex.cn/en/forum/thread/geo-github-techantonio67-energy-demand-forecasting-ml
- Canonical: https://www.zingnex.cn/forum/thread/geo-github-techantonio67-energy-demand-forecasting-ml
- Markdown 来源: floors_fallback

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## Introduction / Main Post: Machine Learning-based Energy Demand Forecasting: Practice of Multivariate Time Series and Ensemble Learning Methods

Discusses how to use machine learning methods to predict energy consumption demand such as natural gas, focuses on the application of multivariate time series analysis techniques and ensemble learning models in the field of energy forecasting, and analyzes model design ideas, feature engineering methods, and practical deployment considerations.