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District heating is an efficient method of distributing heat in densely populated areas at a low cost. The heat is usually produced at central production plants and then distributed to consumers through large networks of pipes. However, district heating is gradually becoming more decentralised with additional heat sources, e.g. heat pumps, solar thermal farms, and industrial waste heat connected to the network. Therefore, the system is changing from a system with centralised heat sources to a more decentralised system with several different heat sources within the network, including also still a large production area. Operationally this is more complex than the previous setup, especially in terms of temperature optimisation. Typically, the temperature must be adjusted for each area in order to work efficiently with the decentralised heat sources, so a forecast of the local heat load is required. It is relatively easy to make a forecast for each area, but they are usually made independently and are therefore not necessarily coherent. In this paper, we propose a methodology to spatially reconcile hierarchies of individual localised heat load forecasts with a coherency constraint. This results in coherent reconciled forecasts. Enhancing forecast accuracy and making them coherent are essential for future decentralised systems as temperature and production optimisation need accurate information to yield optimal operation. We will use two different case studies to illustrate the proposed method. One case study has a few areas, while the other case study will have more areas, and here it is proposed to add a new level of aggregation to the hierarchy to increase accuracy. The results in this paper show that the reconciled forecast, where information is shared between areas through the spatial hierarchy, improves forecast accuracy by 1% to 20%, depending on the prediction horizon. • Spatial hierarchies are used to improve state-of-the-art heat load forecasts. • Recursive and adaptive covariance matrix estimation is used for reconciled forecasts. • Using multi-step forecasting base errors show higher accuracy than using only one-step. • Simulation study to investigate where the accuracy improvements origin from. [ABSTRACT FROM AUTHOR]

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. • Estimating network temperature using a group of smart meter data. • System of stochastic differential equations of thermodynamics in a pipe is developed. • A fast computational method of estimating the likelihood is used for a large system. • The presented method creates the possibility for multi-temperature zones. [ABSTRACT FROM AUTHOR]

Heat load forecasts are crucial for energy operators in order to optimize the energy production at district heating plants for the coming hours. Furthermore, forecasts of heat load are needed for optimized control of the district heating network since a lower temperature reduces the heat loss, but the required heat supply at the end-users puts a lower limit on the temperature level. Consequently, improving the accuracy of heat load forecasts leads to savings and reduced heat loss by enabling improved control of the network and an optimized production schedule at the plant. This paper proposes the use of temporal hierarchies to enhance the accuracy of heat load forecasts in district heating. Usually, forecasts are only made at the temporal aggregation level that is the most important for the system. However, forecasts for multiple aggregation levels can be reconciled and lead to more accurate forecasts at essentially all aggregation levels. Here it is important that the auto- and cross-covariance between forecast errors at the different aggregation levels are taken into account. This paper suggests a novel framework using temporal hierarchies and adaptive estimation to improve heat load forecast accuracy by optimally combining forecasts from multiple aggregation levels using a reconciliation process. The weights for the reconciliation are computed using an adaptively estimated covariance matrix with a full structure, enabling the process to share time-varying information both within and between aggregation levels. The case study shows that the proposed framework improves the heat load forecast accuracy by 15% compared to commercial state-of-the-art operational forecasts. • Temporal hierarchies are used to improve state-of-the-art heat load forecasts. • Adaptive forecast models using numerical weather predictions as inputs are created. • Recursive and adaptive covariance estimation is suggested for reconciliation. • It is shown how the optimal shrinkage intensity can be computed recursively. [ABSTRACT FROM AUTHOR]