[HTML][HTML] A review of data mining technologies in building energy systems: Load prediction, pattern identification, fault detection and diagnosis
With the advent of the era of big data, buildings have become not only energy-intensive but
also data-intensive. Data mining technologies have been widely utilized to release the …
also data-intensive. Data mining technologies have been widely utilized to release the …
Integration of flexibility potentials of district heating systems into electricity markets: A review
Increasing the penetration of Renewable Energy Sources (RES), eg wind and solar,
intermittency and volatility of the supply-side are increasing in power systems worldwide …
intermittency and volatility of the supply-side are increasing in power systems worldwide …
[HTML][HTML] District heater load forecasting based on machine learning and parallel CNN-LSTM attention
WH Chung, YH Gu, SJ Yoo - Energy, 2022 - Elsevier
Accurate heat load forecast is important to operate combined heat and power (CHP)
efficiently. This paper proposes a parallel convolutional neural network (CNN)-long short …
efficiently. This paper proposes a parallel convolutional neural network (CNN)-long short …
Deep learning-based feature engineering methods for improved building energy prediction
The enrichment in building operation data has enabled the development of advanced data-
driven methods for building energy predictions. Existing studies mainly focused on the …
driven methods for building energy predictions. Existing studies mainly focused on the …
Predicting hourly heating load in a district heating system based on a hybrid CNN-LSTM model
J Song, L Zhang, G Xue, YP Ma, S Gao, QL Jiang - Energy and Buildings, 2021 - Elsevier
Heat loads change dynamically with meteorological conditions and user demand, and the
related accurate prediction algorithms are conducive to the realization of optimized …
related accurate prediction algorithms are conducive to the realization of optimized …
A novel improved model for building energy consumption prediction based on model integration
R Wang, S Lu, W Feng - Applied Energy, 2020 - Elsevier
Building energy consumption prediction plays an irreplaceable role in energy planning,
management, and conservation. Constantly improving the performance of prediction models …
management, and conservation. Constantly improving the performance of prediction models …
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 …
optimal operation of district heat systems (DHSs). Existing studies have mainly investigated …
[HTML][HTML] Theory-guided deep-learning for electrical load forecasting (TgDLF) via ensemble long short-term memory
Electricity constitutes an indispensable source of secondary energy in modern society.
Accurate and robust short-term electrical load forecasting is essential for more effective …
Accurate and robust short-term electrical load forecasting is essential for more effective …
Neural network model for short-term and very-short-term load forecasting in district buildings
H Dagdougui, F Bagheri, H Le, L Dessaint - Energy and Buildings, 2019 - Elsevier
Load forecasting plays an important role in energy management in smart buildings. It is
expected that precise prediction of loads can bring significant economic benefits to smart …
expected that precise prediction of loads can bring significant economic benefits to smart …
Machine learning-based thermal response time ahead energy demand prediction for building heating systems
Y Guo, J Wang, H Chen, G Li, J Liu, C Xu, R Huang… - Applied energy, 2018 - Elsevier
Energy demand prediction of building heating is conducive to optimal control, fault detection
and diagnosis and building intelligentization. In this study, energy demand prediction …
and diagnosis and building intelligentization. In this study, energy demand prediction …