Paper Info
Title
Load Forecasting In District Heating Networks: Model Comparison On A Real-World Case Study
Abstract
District Heating networks (DHNs) are promising technologies for heat distribution in residential and commercial buildings since they enable high efficiency and low emissions. Within the recently proposed paradigm of smart grids, DHNs have acquired intelligent tools able to enhance their efficiency. Among these tools, there are demand forecasting technologies that enable improved planning of heat production and power station maintenance. In this work we propose a comparative study for heat load forecasting methods on a real case study based on a dataset provided by an Italian utility company. We trained and tested three kinds of models, namely non-autoregressive, autoregressive and hybrid models, on the available dataset of heat load and meteorological variables. The optimal model, in terms of root mean squared error of prediction, was selected. It considers the day of the week, the hour of the day, some meteorological variables, past heat loads and social components, such as holidays. Results show that the selected model is able to achieve accurate 48-hours predictions of the heat load in several conditions (e.g., different days of the week, different times, holidays and workdays). Moreover, an analysis of the parameters of the selected models enabled to identify a few informative variables.
Year
DOI
Venue
2019
10.1007/978-3-030-37599-7_46
MACHINE LEARNING, OPTIMIZATION, AND DATA SCIENCE
Keywords
Field
DocType
Heat load forecasting, District heating network, Linear regression models, Model interpretability, Time series analysis
Computer science,Load forecasting,Autoregressive integrated moving average,Artificial intelligence,Machine learning
Conference
Volume
ISSN
Citations 
11943
0302-9743
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Federico Bianchi100.34
Alberto Castellini26014.16
Pietro Tarocco300.34
Alessandro Farinelli466774.16