Thermal load forecasting in district heating networks using deep learning and advanced feature selection methods. (15th August 2018)

• Record Type: Journal Article
• Authors: Suryanarayana, Gowri; Lago, Jesus; Geysen, Davy; Aleksiejuk, Piotr; Johansson, Christian
• Is Part Of: Energy. Volume 157(2018)
• Page Start: 141
• Page End: 149
• Publication Date: 2018-08-15
• Subjects: District heating -- Linear models -- Regression -- Deep learning -- Machine learning -- Day ahead forecasting; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2018.05.111
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: Recent research has seen several forecasting methods being applied for heat load forecasting of district heating networks. This paper presents two methods that gain significant improvements compared to the previous works. First, an automated way of handling non-linear dependencies in linear models is presented. In this context, the paper implements a new method for feature selection based on [1], resulting in computationally efficient models with higher accuracies. The three main models used here are linear, ridge, and lasso regression. 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, we compare and contrast the proposed methods with earlier work for day-ahead forecasting of heat load in two different district heating networks. Highlights: Two novel methods to forecast heat loads in district heating networks are presented. First model employs automated feature selection with polynomial linear regressors. Second model is based on a neural network and deep learning techniques. Deep learning model provides the best accuracies albeit with more computation. Linear models outperform benchmark models and those presented previously in [2, 3].