用LSTM预测用电量，比传统方法更准。
Forecasting Energy Consumption using Recurrent Neural Networks: A Comparative Analysis
- 结合历史用电数据与温湿度、时间等外部因素建模
- LSTM误差比前馈神经网络低，MAE和RMSE均更优
- 适合电力调度、能源管理等实际场景应用
精准的短期用电量预测对电网高效管理、资源调配和市场稳定至关重要。传统时间序列模型难以捕捉用电需求中的复杂非线性依赖关系及外部影响因素。本文提出基于循环神经网络（RNN）及其改进版本长短期记忆网络（LSTM）的预测方法，融合历史用电数据与温度、湿度、时间特征等外部变量。在公开数据集上训练并评估LSTM模型，其性能与传统的前馈神经网络基线进行对比。实验结果表明，LSTM模型显著优于基线，实现更低的平均绝对误差（MAE）和均方根误差（RMSE）。这些发现证明深度学习模型在提供可靠且精确的短期用电预测方面具有有效性，适用于实际应用。
原文摘要 · Abstract (English)
Accurate short-term energy consumption forecasting is essential for efficient power grid management, resource allocation, and market stability. Traditional time-series models often fail to capture the complex, non-linear dependencies and external factors affecting energy demand. In this study, we propose a forecasting approach based on Recurrent Neural Networks (RNNs) and their advanced variant, Long Short-Term Memory (LSTM) networks. Our methodology integrates historical energy consumption data with external variables, including temperature, humidity, and time-based features. The LSTM model is trained and evaluated on a publicly available dataset, and its performance is compared against a conventional feed-forward neural network baseline. Experimental results show that the LSTM model substantially outperforms the baseline, achieving lower Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). These findings demonstrate the effectiveness of deep learning models in providing reliable and precise short-term energy forecasts for real-world applications.
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