arXiv：cs.AI· Nasser Alkhulaifi·· 4 小时前AI 评分36
AutoEnergy：面向能耗预测的自动化特征工程与决策聚焦学习
Automated Feature Engineering, AutoML, and Decision-Focused Learning for Improved Energy Consumption Forecasting
AI 导读
博士论文提出 AutoEnergy，一种面向能耗预测的自动化特征工程算法，可从时间戳和滞后能耗数据生成可解释特征并集成 AutoML。在覆盖住宅、商业、工业、可再生能源和电网的 18 个真实数据集上，其预测误差较基线 AutoML 和已有自动化特征工程方法降低 19.52%-84.72%，运行速度快 1.31-4.41 倍。
正文
Abstract:The rising cost and demand for energy, together with environmental sustainability goals, create major challenges for energy management. Energy Consumption Forecasting (ECF) supports planning by predicting future consumption, but Machine Learning (ML) models for ECF often depend on expert-driven Feature Engineering (FE). This thesis addresses that dependence through three contributions. First, it establishes and evaluates a comprehensive FE pipeline for ECF and investigates domain-specific features. Second, it introduces AutoEnergy, a domain-tailored automated FE algorithm that generates interpretable features from timestamps and lagged consumption and integrates with AutoML for end-to-end ECF modelling. Across eighteen real-world energy datasets spanning residential, commercial, industrial, renewable, and grid domains, AutoEnergy reduces forecasting error by 19.52%-84.72% relative to baseline AutoML and established automated FE methods, while running 1.31-4.41 times faster, with gains varying by dataset. Third, AutoEnergy is integrated with Decision-Focused Learning (DFL) for a Battery Energy Storage System problem, jointly forecasting electricity prices and demand while optimising charging and discharging decisions. On a real-world UK property dataset, this approach reduces operating costs by 22.9%-56.5% compared with the same DFL models without automated FE. Overall, the results show that domain-specific automated FE can reduce reliance on manual feature design, improve forecasting accuracy, and translate predictive gains into measurable operational benefits in energy management.
| Comments: | PhD thesis, School of Computer Science, University of Nottingha, United Kingdom |
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2609.35013 [cs.AI] |
| (or arXiv:2609.35013v2 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2609.35013 arXiv-issued DOI via DataCite |
Submission history
From: Nasser Alkhulaifi [view email]
[v1]
Mon, 28 Sep 2026 12:14:33 UTC (30,431 KB)
[v2]
Fri, 2 Oct 2026 11:12:55 UTC (30,431 KB)
来源：arXiv：cs.AI · arxiv.org