Multi-step-ahead prediction of thermal load in regional energy system using deep learning method

[Display omitted] Accurate and reliable multi-step-ahead thermal load prediction is generally considered as the basis of model predictive control and multi-energy dispatch of building energy systems. Conventional approaches to future load time series prediction rely either on iterative one-step-ahea...

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Bibliographic Details
Published in:Energy and buildings Vol. 233; p. 110658
Main Authors: Lu, Yakai, Tian, Zhe, Zhou, Ruoyu, Liu, Wenjing
Format: Journal Article
Language:English
Published: Lausanne Elsevier B.V 15.02.2021
Elsevier BV
Subjects:
Attention mechanism
Coders
Deep learning
Encoders-Decoders
Energy
Iterative methods
Load
Long short-term memory
Model accuracy
Multi-step-ahead prediction
Predictions
Predictive control
Sequence-to-sequence
Sequences
Temporal dependency
Thermal analysis
Time series
Deep learning
Attention mechanism
Sequence-to-sequence
Multi-step-ahead prediction
Temporal dependency
ISSN:0378-7788, 1872-6178
Online Access:Get full text
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Summary:[Display omitted] Accurate and reliable multi-step-ahead thermal load prediction is generally considered as the basis of model predictive control and multi-energy dispatch of building energy systems. Conventional approaches to future load time series prediction rely either on iterative one-step-ahead predictors or direct predictors, which ignores the temporal dependency between successive loads in time-delay building energy systems. This study, therefore, proposes a temporal attention encoder-decoder network (TA-EDN) model to improve the accuracy of multi-step-ahead thermal load prediction with the following three functional modules: long short-term memory (LSTM) network, which is to process the intrinsic temporal relationships among input and output variables, encoder-decoder network (EDN), which is to realize the multi-input multi-output modeling, and attention mechanism, which is to improve the ability of processing variables with long sequences. An actual regional energy system is selected to perform the 24-hour-ahead prediction using the proposed model as a validation experiment. The results suggest that the TA-EDN model significantly improves the prediction accuracy of the future thermal loads time series, achieving a mean absolute percentage error of 7.4%, compared to that of 9.1% of EDN model, 12.4% of LSTM-IS (a model combining LSTM with iterative strategy) and 12.7% of LSTM-DS (a model combining LSTM with direct strategy). In addition, compared with the ideal benchmark of multi-step-ahead prediction, the proposed TA-EDN model has room for improvement in the prediction of low load or large fluctuation load.
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ISSN:0378-7788
1872-6178
DOI:10.1016/j.enbuild.2020.110658