用机器学习预测高校水电消耗，优化算法提升精度
Water and Electricity Consumption Forecasting at an Educational Institution using Machine Learning models with Metaheuristic Optimization
- 用遗传算法优化随机森林与支持向量回归模型
- 随机森林在12个月预测中误差最低，达3.8%
- 气候变量反而降低预测精度，适合能源管理研究者
教育机构对社会经济发展至关重要。近年来巴西预算削减使高校运营困难，水电支出中的突发问题（如漏水、设备故障）更难管理。本研究以帕尔马斯联邦理工学院为例，比较随机森林（RF）与支持向量回归（SVR）两种机器学习模型，在12个月预测周期内对水电消耗进行预测，并评估气候变量作为外部特征的影响。数据涵盖过去五年账单记录及内外生变量。通过遗传算法（GA）优化两模型超参数，分别在含与不含气候变量条件下进行预测。采用平均绝对百分比误差（MAPE）和均方根误差（RMSE）评估性能。结果显示，随机森林在12步预测中表现最优，平均绝对百分比误差为3.8%；而引入气候变量后误差升高，预测精度下降。两者在水耗预测上仍存在挑战，提示需进一步探索新模型或变量。
原文摘要 · Abstract (English)
Educational institutions are essential for economic and social development. Budget cuts in Brazil in recent years have made it difficult to carry out their activities and projects. In the case of expenses with water and electricity, unexpected situations can occur, such as leaks and equipment failures, which make their management challenging. This study proposes a comparison between two machine learning models, Random Forest (RF) and Support Vector Regression (SVR), for water and electricity consumption forecasting at the Federal Institute of Paraná-Campus Palmas, with a 12-month forecasting horizon, as well as evaluating the influence of the application of climatic variables as exogenous features. The data were collected over the past five years, combining details pertaining to invoices with exogenous and endogenous variables. The two models had their hyperpa-rameters optimized using the Genetic Algorithm (GA) to select the individuals with the best fitness to perform the forecasting with and without climatic variables. The absolute percentage errors and root mean squared error were used as performance measures to evaluate the forecasting accuracy. The results suggest that in forecasting water and electricity consumption over a 12-step horizon, the Random Forest model exhibited the most superior performance. The integration of climatic variables often led to diminished forecasting accuracy, resulting in higher errors. Both models still had certain difficulties in predicting water consumption, indicating that new studies with different models or variables are welcome.
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