A novel machine learning approach to predict short- term energy load for future low-temperature district heating

Abstract

In this work, we develop machine learning methods to forecast the day-ahead heating energy demand of district heating (DH) end-users in hourly resolution, using existing metering data for DH end-users and weather data. The focus of the study is a detailed analysis of the accuracy levels of short-term load prediction methods. In particular, accuracy levels are quantified for Artificial Neural Network (ANN) models with variations in the input parameters. The importance of historical data is investigated – in particular the importance of including historical hourly heating loads as input to the forecasting model. Additionally, the impact of different lengths of the historical input data is studied. Our methods are evaluated and validated using metering data from a live use-case in a Scandinavian environment, collected from 20 DH-supplied nursing homes through the years of 2016 to 2019. This study demonstrates that, although there is a strong linear relationship between outdoor temperature and heating load, it is still important to include historical heating loads as an input for prediction of future heating loads. Furthermore, the results show that it is important to include historical data from at least the preceding 24 hours, but suggest diminishing returns of including data much further back than that. The resulting models demonstrate the practical feasibility of such prediction models in a live use-case.