Multi-step ahead forecasting of heat load in district heating systems using machine learning algorithms

Predicting next-day heat load curves is essential to guarantee sufficient heat supply and optimal operation of district heat systems (DHSs). Existing studies have mainly investigated one-step ahead forecasting methods, which can predict a single value at a future time step. To predict heat load curv...

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Bibliographic Details
Published in:Energy (Oxford) Vol. 188; p. 116085
Main Authors: Xue, Puning, Jiang, Yi, Zhou, Zhigang, Chen, Xin, Fang, Xiumu, Liu, Jing
Format: Journal Article
Language:English
Published: Oxford Elsevier Ltd 01.12.2019
Elsevier BV
Subjects:
Algorithms
Artificial intelligence
Artificial neural networks
Bluegrass music
case studies
China
Coefficient of variation
Direct strategy
District heating
Forecasting
heat
Heat load forecasting
Heating systems
Learning algorithms
Machine learning
Machine learning algorithms
Mathematical models
Model accuracy
Modelling
Multi-step ahead forecasting
Neural networks
Performance assessment
prediction
Predictions
Recursive functions
Recursive methods
Recursive strategy
regression analysis
Strategy
Support vector machines
China
Multi-step ahead forecasting
Machine learning algorithms
Heat load forecasting
District heating
Recursive strategy
Direct strategy
ISSN:0360-5442, 1873-6785
Online Access:Get full text
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Summary:Predicting next-day heat load curves is essential to guarantee sufficient heat supply and optimal operation of district heat systems (DHSs). Existing studies have mainly investigated one-step ahead forecasting methods, which can predict a single value at a future time step. To predict heat load curves, multi-step ahead forecasting methods are needed. This study proposes a machine learning-based framework for multi-step ahead DHS heat load forecasting. Specifically, support vector regression, deep neural network, and extreme gradient boosting (XGBoost) are respectively used as the base learner to develop forecasting models. Two multi-step ahead forecasting methods, i.e. direct strategy and recursive strategy, adopt the learnt models to generate predictions. A DHS in China is used as the case study to comprehensively assess the performance of these two forecasting strategies. Recursive strategy using the XGBoost-based forecasting model can achieve the most accurate and stable predictions with a value of 10.52% for the coefficient of variation of root mean square error. Furthermore, the modeling process of recursive strategy is much more convenient than that of direct strategy. The research shows that the recursive strategy is a better solution to multi-step ahead forecasting than the direct strategy with respect to accuracy, prediction stability, and modeling process. •A framework for multi-step ahead heat load forecasting is proposed.•Direct and recursive strategies are used to predict daily heat load curves.•Applicability of direct and recursive strategies is assessed from three aspects.•Recursive strategy slightly outperforms direct strategy in accuracy and stability.•Modeling process of recursive strategy is simpler than that of direct strategy.
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ISSN:0360-5442
1873-6785
DOI:10.1016/j.energy.2019.116085