Impacts of Weather Conditions on District Heat System

TL;DR

This study uses an Elman neural network to analyze how weather factors like wind speed and solar irradiance influence heat demand prediction accuracy in district heating systems, highlighting the importance of these parameters.

Contribution

It investigates the specific impacts of wind speed and solar irradiance on heat demand prediction accuracy using an Elman neural network, providing insights for model improvement.

Findings

Including wind speed reduces mean absolute percentage error.

Including solar irradiance lowers maximum deviation.

Combining both parameters yields the best overall accuracy.

Abstract

Using artificial neural network for the prediction of heat demand has attracted more and more attention. Weather conditions, such as ambient temperature, wind speed and direct solar irradiance, have been identified as key input parameters. In order to further improve the model accuracy, it is of great importance to understand the influence of different parameters. Based on an Elman neural network (ENN), this paper investigates the impact of direct solar irradiance and wind speed on predicting the heat demand of a district heating network. Results show that including wind speed can generally result in a lower overall mean absolute percentage error (MAPE) (6.43%) than including direct solar irradiance (6.47%); while including direct solar irradiance can achieve a lower maximum absolute deviation (71.8%) than including wind speed (81.53%). In addition, even though including both wind speed and direct solar irradiance shows the best overall performance (MAPE=6.35%).