An integrated D-CNN-LSTM approach for short-term heat demand prediction in district heating systems
نویسندگان
چکیده
Forecasting short-term heat demand is an integral function of district energy management applications. Although some well-known methods, such as support vector machine and artificial neural networks, can be employed, most them require additional variables (such temperature humidity) in addition to the itself order make accurate prediction. In this paper, a differencing-convolutional network-long short term memory (D-CNN-LSTM) approach developed forecast half hourly ahead using only historical data. Firstly, features extraction performed find set model inputs related dynamic behavior consumption. This followed by design D-CNN-LSTM capture different seasonal patterns, which differencing aims convert stationary from non-stationary while CNN-LSTM focuses on accurately predicting future demand. Finally, various experiments are conducted demonstrate effectiveness superiority designed method comparison with existing algorithms.
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ژورنال
عنوان ژورنال: Energy Reports
سال: 2022
ISSN: ['2352-4847']
DOI: https://doi.org/10.1016/j.egyr.2022.08.087