Integrating Expert and Physics Knowledge for Modeling Heat Load in District Heating Systems

TL;DR

This paper introduces HELIOS, an AI model that combines physical principles and expert knowledge to accurately predict heat load in district heating systems, enhancing efficiency and sustainability.

Contribution

The paper presents HELIOS, a novel AI model integrating physical laws and expert insights, outperforming existing models in heat load prediction for district heating systems.

Findings

HELIOS outperforms ten state-of-the-art models in accuracy.

HELIOS is fully explainable, improving accountability.

The model supports sustainable energy management.

Abstract

New residential neighborhoods are often supplied with heat via district heating systems (DHS). Improving the energy efficiency of a DHS is critical for increasing sustainability and satisfying user requirements. In this paper, we present HELIOS, a dedicated artificial intelligence (AI) model designed specifically for modeling the heat load in DHS. HELIOS leverages a combination of established physical principles and expert knowledge, resulting in superior performance compared to existing state-of-the-art models. HELIOS is explainable, enabling enhanced accountability and traceability in its predictions. We evaluate HELIOS against ten state-of-the-art data-driven models in modeling the heat load in a DHS case study in the Netherlands. HELIOS emerges as the top-performing model while maintaining complete accountability. The applications of HELIOS extend beyond the present case study, potentially supporting the adoption of AI by DHS and contributing to sustainable energy management on a larger scale.