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Presently, there are substantial financial efforts to improve heating plants’ control and system optimisation based on heat load predictions. In this paper two methods for heat load predictions in urban areas are demonstrated. The first one is an in-house Urban Energy Platform called SimStadt, which is based on 3D CityGML buildings models and its characteristics. The second approach uses long-term measurement data processed by an Artificial Neural Network. Both methods show accurate results on the annual basis at citywide scale. Furthermore, high correlation between measured and predicted hourly load profiles enables forecasts to be used as guidelines for efficient energy generation, which will increase overall system efficiency.

Municipal Heating Grid Load Predictions for Improved Control of Heating and Cogeneration Plants