Leveraging Hypernetworks and Learnable Kernels for Consumer Energy Forecasting Across Diverse Consumer Types

2025-02-01

IEEE Transactions on Power Delivery

AI Summary

Existing LSTM and Transformer models struggle to capture abrupt load changes and exhibit limited generalization across heterogeneous residential energy consumers—specifically students in dormitories, single-family homes, EV-integrated households, and townhouses. Method: We propose HyperEnergy, the first framework to employ a hypernetwork for dynamically generating LSTM parameters, coupled with a learnable hybrid kernel combining polynomial and radial basis function (RBF) components to enable adaptive modeling across diverse, non-stationary scenarios. The architecture integrates the hypernetwork, hybrid kernel, and LSTM backbone, trained jointly on real-world data from all four user types. Results: HyperEnergy consistently outperforms ten state-of-the-art baselines—including AttentionLSTM and Transformer—achieving an average 18.7% reduction in mean absolute error (MAE). It demonstrates superior prediction accuracy, cross-scenario generalization, and robustness to load volatility.

Technology Category

• Machine Learning: Mixture of Experts (MoE)
• Search and Optimization: Learning to Search
• Natural Language Processing: Learning & Optimization for NLP

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

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.

Problem

• Improves consumer energy forecasting accuracy.
• Addresses diverse consumer type variations.
• Utilizes hypernetworks and learnable kernels.

Innovation

• Hypernetworks for diverse consumer modeling
• Learnable adaptable kernel integration
• Outperforms state-of-the-art forecasting models