District heating systems (DHS) are today the most efficient and cost-effective method for distributing heat to residential and commercial buildings in urban areas. DHS is an infrastructure for distributing hot fluid from the heating plant or energy center to the consumers, namely DHS substations, which are then in charge for delivering the heat energy to end users, commercial and residential.

The problem

Currently, DHS operation is controlled semi-automatically by SCADA systems, where the operation decisions are made by the DHS plant operator. Operation is considered reactive and simplistic as control decisions are made based only on the real-time ambient temperature.

The XAI4HEAT Solution

The XAI4HEAT aims at facilitating proactive DHS control, by using short and long-term multivariate heat demand forecasting and anomaly detection on end-to-end models encompassing complete heat production-consumption lifecycle, based on modern Artificial Intelligence (AI) architectures. Project vision is to achieve a reduction of minimum 10% in heat production in the small 8MW DHS system which will be used for experimentation. This saving will be achieved by reducing the overheating arising in statically controlled DHS, by implementing dynamic control that directly correspond to actual heat demand in a real time and to some extent, by raising awareness of the end users on the impact of their behavior to the actual heat production. Mapped to the economic and environmental benefits, this reduction corresponds to annual savings of 40.000 EUR and 200 t less CO2 emissions.

Another specific objective is to enable forecasting and anomaly detection models interpretability, by using Explainable AI (XAI) techniques. Importance of XAI in public services is more than significant. Currently, there is no published research on explainable heat demand forecasting.

Besides being first to tackle this topic, we have ambition to create a new paradigm, to start changing how citizens perceive one public service, beyond heating production distribution only, to create a niche for new products that will facilitate legal alignment of public services, but also enable brand new customer experience and thus, increase satisfaction and trust.

Online resources

Learn more about the XAI4HEAT solution

Objectives of the XAI4HEAT project

District heating systems (DHS) are today the most efficient and cost-effective method for distributing heat…

Methodology for XAI4HEAT solution

The main pipeline of the project consists of the following main groups of activities: data…

Concept of XAI4HEAT solution

The concept of the solution is based on two hypotheses. First, DHS plant efficiency can…

Background and the research problem

In a nutshell, DHS plant operation means automated or semi-automated control of primary (plant level)…