Data-Driven Model For Heat Load Prediction In Buildings Connected To District Heating Networks

TL;DR

This paper presents a novel hybrid forecasting model combining ARX and seasonally adaptable climate models for accurate short-term heat load prediction in buildings connected to district heating networks, especially effective in winter.

Contribution

The study introduces the S-TOW-C-ARX model, a new hybrid approach that improves short-term heat load forecasting accuracy over existing methods.

Findings

Model achieves 4-20% error in winter predictions

Proposed model outperforms traditional methods in accuracy

Effective for short-term heat load forecasting

Abstract

In this study we investigate the heat load patterns in one building using multi-step forecasting model. We combine the Autoregressive models that use multiple eXogenous variables (ARX) with Seasonally adaptable Time of Week and Climate dependent models (S-TOW-C) (to correct model inaccuracies), to obtain a robust and accurate regression model that we called S-TOW-C-ARX used in time series forecasting. Based on the experiment results, it has been shown that the proposed model is suitable for short term heat load forecasting. The best forecasting performance is achieved in winter term where the prediction values are from 4 to 20 % away from the targets, which are commonly seen as very good values.