Predictive Model Generation for Load Forecasting in District Heating Networks

District heating networks (DHNs) are promising technologies to increase the efficiency and reduce emissions of heat distribution to residential and commercial buildings. The advent of the smart grid paradigm has introduced the usage of heating load forecasting tools in DHNs. They provide estimates of future heating load, improving the planning of heat production and power station maintenance. In this article, we propose a methodology based on the integrated use of regularized regression and clustering for generating predictive models of future heating load in DHNs. The methodology is tested on a real case study based on a dataset provided by AGSM, an Italian utility company that manages a DHN in the city of Verona, Italy. We generate a set of multiple-equation models having different degrees of complexity and show that models generated by the proposed approach outperform those trained by standard methods. Moreover, we provide an interpretation of patterns encoded by these models, and show that they identify real operational states of the network. The approach is completely data driven.