用机器学习提升电网短期用电量预测精度，助力电力供需平衡
Load Forecasting in the Era of Smart Grids: Opportunities and Advanced Machine Learning Models
- 对比四种机器学习模型，包括梯度提升树与循环神经网络
- 相比传统ARIMA模型，预测误差显著降低，性能更优
- 适合电力系统优化、智能电网研究者参考
电能难以储存，必须严格控制其生成、传输和分配。电力系统持续面临的挑战是保持用电需求与供给的实时平衡。供过于求导致资源浪费，供不应求则会加重电网负担、提高运营成本，甚至影响服务可靠性。为保障电网稳定，需进行负荷预测。准确的负荷预测通过预估未来用电量来实现发电与需求的平衡。本论文评估了四种机器学习框架在短期负荷预测中的表现，包括极端梯度提升（XGBoost）和轻量梯度提升（LightGBM）等梯度提升决策树方法，同时设计并实现了长短期记忆网络（LSTM）与门控循环单元（GRU）两种循环神经网络架构。采用皮尔逊相关系数分析用电量与外部变量之间的关系。实验结果表明，在本研究的具体数据集与预测任务下，基于机器学习的模型相比经典ARIMA基准模型取得了更优的预测性能。
原文摘要 · Abstract (English)
Electric energy is difficult to store, requiring stricter control over its generation, transmission, and distribution. A persistent challenge in power systems is maintaining real-time equilibrium between electricity demand and supply. Oversupply contributes to resource wastage, while undersupply can strain the grid, increase operational costs, and potentially impact service reliability. To maintain grid stability, load forecasting is needed. Accurate load forecasting balances generation and demand by striving to predict future electricity consumption. This thesis examines and evaluates four machine learning frameworks for short term load forecasting, including gradient boosting decision tree methods such as Extreme Gradient Boosting (XGBoost) and Light Gradient Boosting Machine (LightGBM). A hybrid framework is also developed. In addition, two recurrent neural network architectures, Long Short Term Memory (LSTM) networks and Gated Recurrent Units (GRU), are designed and implemented. Pearson Correlation Coefficient is applied to assess the relationships between electricity demand and exogenous variables. The experimental results show that, for the specific dataset and forecasting task in this study, machine learning-based models achieved improved forecasting performance compared to a classical ARIMA baseline.
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