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Thermal load forecasting in district heating networks using deep learning and advanced feature selection methods
This paper (2018) presents two methods that gain significant improvements compared to the previous works of several forecasting methods being applied for heat load forecasting of district heating networks. First, an automated way of handling non-linear dependencies in linear models is presented. In the second approach, a deep learning method is presented. Although computationally more intensive, the deep learning model provides higher accuracy than the linear models with automated feature selection. Finally, the proposed methods will be compared and contrasted with earlier work for day-ahead forecasting of heat load in two different district heating networks.
Author: Horizon 2020 STORM project
Keywords: optimisation, district heating, district cooling, heat load forecast, data, digitalisation, 4th generation district heating, energy efficiency, Horizon 2020, buildings, model, prediction algorithms
Type of Content
- Scientific article