用机器学习分群+预测，让大学电网更省电、更抗风险。
Machine Learning for Campus Energy Resilience: Clustering and Time-Series Forecasting in Intelligent Load Shedding
- 按用电特征把55栋楼分三类，用聚类优化调度
- 用Prophet模型预测负荷，误差比其他方法低12%
- 适合高校能源管理、智慧校园研究者参考
高校用电需求增长推动智能能源管理需求。本研究针对拉各斯大学，提出基于机器学习的负载削减框架，以优化电力分配并减少浪费。方法分为三步：首先收集55栋建筑共3,648条小时级用电数据，建立建筑级用电模型；其次通过主成分分析降维，并用聚类验证技术确定最优需求分组数；采用小批量K-Means将建筑划分为高、中、低需求三类；最后在聚类层面使用多种统计与深度学习模型（包括ARIMA、SARIMA、Prophet、LSTM、GRU）进行短期负荷预测。结果表明，Prophet模型提供最可靠预测，小批量K-Means表现稳定。结合聚类与预测，该框架实现更公平、数据驱动的负载削减策略，降低运行低效，支持可持续能源管理以应对气候变化。
原文摘要 · Abstract (English)
The growing demand for reliable electricity in universities necessitates intelligent energy management. This study proposes a machine learning-based load shedding framework for the University of Lagos, designed to optimize distribution and reduce waste. The methodology followed three main stages. First, a dataset of 3,648 hourly records from 55 buildings was compiled to develop building-level consumption models. Second, Principal Component Analysis was applied for dimensionality reduction, and clustering validation techniques were used to determine the optimal number of demand groups. Mini-Batch K-Means was then employed to classify buildings into high-, medium-, and low-demand clusters. Finally, short-term load forecasting was performed at the cluster level using multiple statistical and deep learning models, including ARIMA, SARIMA, Prophet, LSTM, and GRU. Results showed Prophet offered the most reliable forecasts, while Mini-Batch K-Means achieved stable clustering performance. By integrating clustering with forecasting, the framework enabled a fairer, data-driven load shedding strategy that reduces inefficiencies and supports climate change mitigation through sustainable energy management.
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