<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>To develop an advanced control of thermal energy supply for domestic heating, a number of new challenges need to be solved, such as the emerging need to plan operation in accordance with an energy market-based environment. However, to move towards this goal, it is necessary to develop forecasting tools for short- and long-term planning, taking into account data about the operation of existing heating systems. The paper considers the real operational parameters of five different heating networks in Latvia over a period of five years. The application of regression analysis for heating load dependency on ambient temperature results in the formulation of normalized slope for the regression curves of the studied systems. The value of this parameter, the normalized slope, allows describing the performance of particular heating systems. Moreover, a heat load forecasting approach is presented by an application of multiple regression methods. This short-term (day-ahead) forecasting tool is tested on data from a relatively small district heating system with an average load of 20 MW at ambient temperature of 0 °C. The deviations of the actual heat load demand from the one forecasted with various training data set sizes and polynomial orders are evaluated for two testing periods in January of 2018. Forecast accuracy is assessed by two parameters – mean absolute percentage error and normalized mean bias error.</p>
</abstract>

To develop an advanced control of thermal energy supply for domestic heating, a number of new challenges need to be solved, such as the emerging need to plan operation in accordance with an energy market-based environment. However, to move towards this goal, it is necessary to develop forecasting tools for short- and long-term planning, taking into account data about the operation of existing heating systems. The paper considers the real operational parameters of five different heating networks in Latvia over a period of five years. The application of regression analysis for heating load dependency on ambient temperature results in the formulation of normalized slope for the regression curves of the studied systems. The value of this parameter, the normalized slope, allows describing the performance of particular heating systems. Moreover, a heat load forecasting approach is presented by an application of multiple regression methods. This short-term (day-ahead) forecasting tool is tested on data from a relatively small district heating system with an average load of 20 MW at ambient temperature of 0 °C. The deviations of the actual heat load demand from the one forecasted with various training data set sizes and polynomial orders are evaluated for two testing periods in January of 2018. Forecast accuracy is assessed by two parameters – mean absolute percentage error and normalized mean bias error.

Heat Load Numerical Prediction for District Heating System Operational Control

Department of Heat and Power Engineering Systems

Latvian Journal of Physics and Technical Sciences

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Heat Load Numerical Prediction for District Heating System Operational Control"}

To develop an advanced control of thermal energy supply for domestic heating, a number of new challenges need to be solved, such as the emerging need to plan...

Heat Load Numerical Prediction for District Heating System Operational Control

Published Online: Jun 24, 2021

Page range: 121 - 136

DOI: https://doi.org/10.2478/lpts-2021-0021

Keywords
Forecasting, heating demand, heating curve slope, multiple regression

© 2021 D. Rusovs et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Heat Load Numerical Prediction for District Heating System Operational Control

Published Online: Jun 24, 2021

Page range: 121 - 136

DOI: https://doi.org/10.2478/lpts-2021-0021

KeywordsForecasting, heating demand, heating curve slope, multiple regression

© 2021 D. Rusovs et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Keywords
Forecasting, heating demand, heating curve slope, multiple regression