Operational thermal load forecasting in district heating networks using machine learning and expert advice

TL;DR

This paper introduces an expert system that combines multiple machine learning models to improve thermal load forecasting accuracy in district heating networks, validated on real residential building data.

Contribution

It presents a novel ensemble approach that tracks the best individual model in thermal load forecasting for district heating systems.

Findings

The expert system outperforms individual models in forecasting accuracy.

The approach effectively adapts to different conditions by combining multiple models.

Validation on real-world data demonstrates practical applicability.

Abstract

Forecasting thermal load is a key component for the majority of optimization solutions for controlling district heating and cooling systems. Recent studies have analysed the results of a number of data-driven methods applied to thermal load forecasting, this paper presents the results of combining a collection of these individual methods in an expert system. The expert system will combine multiple thermal load forecasts in a way that it always tracks the best expert in the system. This solution is tested and validated using a thermal load dataset of 27 months obtained from 10 residential buildings located in Rottne, Sweden together with outdoor temperature information received from a weather forecast service. The expert system is composed of the following data-driven methods: linear regression, extremely randomized trees regression, feed-forward neural network and support vector machine. The results of the proposed solution are compared with the results of the individual methods.