针对学生宿舍的季节性用电波动，提出更适应变化的预测模型。
An Investigation into Seasonal Variations in Energy Forecasting for Student Residences
- 用LSTM、Transformer等模型对比分析季节性用电
- 夏季用电突变时，新提出的双模型表现最优
- 适合需要精准分季节预测的校园能源管理
本研究深入评估了多种机器学习模型在学生宿舍能源预测中的表现，聚焦季节变化带来的独特挑战。通过对比基础模型（如LSTM、GRU）与先进方法（如自回归前馈神经网络、Transformer及混合模型），重点解决假期、气象变化和不规律行为引发的用电突变问题。结果表明，无单一模型在全年各季节均表现最佳，强调需根据季节选择或定制模型。特别地，提出的基于超网络的LSTM和MiniAutoEncXGBoost模型，在夏季用电骤变场景中展现出优异适应性，有效捕捉突发波动。研究凸显季节动态与模型特异性对精准预测的关键作用，推动能源预测技术发展。
原文摘要 · Abstract (English)
This research provides an in-depth evaluation of various machine learning models for energy forecasting, focusing on the unique challenges of seasonal variations in student residential settings. The study assesses the performance of baseline models, such as LSTM and GRU, alongside state-of-the-art forecasting methods, including Autoregressive Feedforward Neural Networks, Transformers, and hybrid approaches. Special attention is given to predicting energy consumption amidst challenges like seasonal patterns, vacations, meteorological changes, and irregular human activities that cause sudden fluctuations in usage. The findings reveal that no single model consistently outperforms others across all seasons, emphasizing the need for season-specific model selection or tailored designs. Notably, the proposed Hyper Network based LSTM and MiniAutoEncXGBoost models exhibit strong adaptability to seasonal variations, effectively capturing abrupt changes in energy consumption during summer months. This study advances the energy forecasting field by emphasizing the critical role of seasonal dynamics and model-specific behavior in achieving accurate predictions.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。