Estimating temperatures in a district heating network using smart meter data

Smart meters at consumers create opportunities to improve operation of the district heating sector using data-driven methods. Information from these meter measurements carries the potential to increase the energy efficiency of both individual houses and the utility network, for example by identifying buildings with too high return temperature, or by detecting leakage in the network. This paper proposes a method for using meter data to estimate network temperatures. Network temperatures can subsequently be used to estimate the network characteristics, namely the nonlinear relationship between network temperature and the plants' temperature and flow. A description of the network characteristics is needed for most temperature-optimisation methods to keep the supply temperature as low as possible without violating the system constraints. Traditionally, measurement wells located in the network have been used. These wells are located at critical points in the network where the largest temperature losses occur. Since the lowest temperature often varies over time, multiple critical points are necessary. The method presented in this paper eliminates the need for these physical critical points in the network. It also makes it possible to change the location of the critical points if needed. The network temperature is estimated using a stochastic state-space model of the heat dynamics from the street level distribution pipe over the service pipe and into individual houses. The parameters in the model are estimated using a maximum likelihood approach, and the Kalman Filter is used to evaluate the likelihood function. The estimation process takes advantage of automatic differentiation using the R package Template Model Builder (TMB) to reduce the computational workload. The proposed method is validated by comparing the estimated temperature with the temperature measured from a measurement well.

Automated summary

This paper proposes a method for estimating network temperatures using smart meter data, and using these temperatures to estimate network characteristics. Compared to traditional physical measurement methods at critical points, this method eliminates the need for actual physical critical points and allows for changing the location of critical points if needed. The proposed method utilizes a stochastic state-space model and maximum likelihood estimation, with the Kalman Filter used to evaluate the likelihood function.