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

TL;DR

HyperEnergy introduces a hypernetwork-based energy forecasting model with learnable kernels, effectively capturing complex patterns across diverse consumer types and outperforming existing methods.

Contribution

The paper presents HyperEnergy, a novel consumer energy forecasting approach using hypernetworks and learnable kernels, applicable across various consumer categories.

Findings

HyperEnergy outperforms 10 existing models across diverse consumer types.

The model effectively captures complex and sudden consumption variations.

It demonstrates consistent accuracy improvements over state-of-the-art techniques.

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.