用可学习的超网络提升跨类型家庭用电预测精度
Leveraging Hypernetworks and Learnable Kernels for Consumer Energy Forecasting Across Diverse Consumer Types
- 用超网络动态生成LSTM参数，捕捉复杂用电模式
- 融合多项式与径向基函数核，提升模型适应性
- 在学生公寓、独栋住宅等4类用户中均优于10种方法
消费者能源预测对优化能源使用、降低成本和实现可持续发展至关重要。近年来，深度学习如LSTM和Transformer在该领域表现优异，但难以捕捉复杂突变行为，且通常仅针对单一用户类型（如仅办公室或学校）测试。为此，本文提出HyperEnergy，通过超网络动态生成主预测网络（本研究中为LSTM）的参数，并引入可学习的自适应核（包含多项式与径向基函数核）以增强性能。实验在学生公寓、独栋住宅、带电动车充电的住宅及联排别墅四类用户上进行，结果表明，HyperEnergy在所有类型中均持续优于10种其他方法，包括LSTM、AttentionLSTM和Transformer等先进模型。
原文摘要 · Abstract (English)
Consumer energy forecasting is essential for managing energy consumption and planning, directly influencing operational efficiency, cost reduction, personalized energy management, and sustainability efforts. In recent years, deep learning techniques, especially LSTMs and transformers, have been greatly successful in the field of energy consumption forecasting. Nevertheless, these techniques have difficulties in capturing complex and sudden variations, and, moreover, they are commonly examined only on a specific type of consumer (e.g., only offices, only schools). Consequently, this paper proposes HyperEnergy, a consumer energy forecasting strategy that leverages hypernetworks for improved modeling of complex patterns applicable across a diversity of consumers. Hypernetwork is responsible for predicting the parameters of the primary prediction network, in our case LSTM. A learnable adaptable kernel, comprised of polynomial and radial basis function kernels, is incorporated to enhance performance. The proposed HyperEnergy was evaluated on diverse consumers including, student residences, detached homes, a home with electric vehicle charging, and a townhouse. Across all consumer types, HyperEnergy consistently outperformed 10 other techniques, including state-of-the-art models such as LSTM, AttentionLSTM, and transformer.
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