Long-term urban heating load predictions based on optimized retrofit orders: A cross-scenario analysis

Abstract District heating technologies are essential key elements of future sustainable energy systems. In order to support the design process, further information concerning long-term developments related to urban heat demand are crucial. Since building refurbishments are indispensable for achieving European CO2 reduction objectives, strategies for district retrofit orders are mandatory, which, in consequence, highly affect future energy demand of urban areas. In this paper, a data-driven approach for predicting long-term urban heating loads with Nonlinear Autoregressive Exogenous Recurrent Neural Networks (NARX RNN) based on an economically optimized retrofit order and two conventional retrofit orders is proposed. For demonstration, measured heat power data of a non-residential district in Germany is used for model training and statistical feature scenario generation enables mapping of future heat demand developments.

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