Deep learning for time series forecasting: Advances and open problems
A time series is a sequence of time-ordered data, and it is generally used to describe how a
phenomenon evolves over time. Time series forecasting, estimating future values of time …
phenomenon evolves over time. Time series forecasting, estimating future values of time …
[HTML][HTML] Urban building energy performance prediction and retrofit analysis using data-driven machine learning approach
Stakeholders such as urban planners and energy policymakers use building energy
performance modeling and analysis to develop strategic sustainable energy plans with the …
performance modeling and analysis to develop strategic sustainable energy plans with the …
[HTML][HTML] Federated-WDCGAN: A federated smart meter data sharing framework for privacy preservation
Energy consumption data are crucial for various smart energy management applications,
such as demand forecasting, customer segmentation, and energy efficiency analysis …
such as demand forecasting, customer segmentation, and energy efficiency analysis …
Filling time-series gaps using image techniques: Multidimensional context autoencoder approach for building energy data imputation
Building energy prediction and management has become increasingly important in recent
decades, driven by the growth of Internet of Things (IoT) devices and the availability of more …
decades, driven by the growth of Internet of Things (IoT) devices and the availability of more …
[HTML][HTML] Building simulation in adaptive training of machine learning models
Combining building performance simulation (BPS) and artificial intelligence (AI) provides
smart buildings with the ability to adapt by utilizing BPS's data synthesis and training …
smart buildings with the ability to adapt by utilizing BPS's data synthesis and training …
Creating synthetic energy meter data using conditional diffusion and building metadata
Advances in machine learning and increased computational power have driven progress in
energy-related research. However, limited access to private energy data from buildings …
energy-related research. However, limited access to private energy data from buildings …
Fast prediction of indoor airflow distribution inspired by synthetic image generation artificial intelligence
Prediction of indoor airflow distribution often relies on high-fidelity, computationally intensive
computational fluid dynamics (CFD) simulations. Artificial intelligence (AI) models trained by …
computational fluid dynamics (CFD) simulations. Artificial intelligence (AI) models trained by …
A data transfer method based on one dimensional convolutional neural network for cross-building load prediction
Y Zhang, Z Zhou, Y Du, J Shen, Z Li, J Yuan - Energy, 2023 - Elsevier
Load prediction is one of the basic tasks in energy system operation and management. With
the development of data mining and artificial intelligence, data driven-based prediction …
the development of data mining and artificial intelligence, data driven-based prediction …
A scenario framework for electricity grid using Generative Adversarial Networks
B Yilmaz - Sustainable Energy, Grids and Networks, 2023 - Elsevier
This study proposes a synthetic data framework to decrease the cost of smart metering. It
offers clustering of real-time buses based on their electricity consumption levels and …
offers clustering of real-time buses based on their electricity consumption levels and …
Performance comparison on improved data-driven building energy prediction under data shortage scenarios in four perspectives: data generation, incremental …
G Li, L Zhan, X Fang, J Gao, C Xu, X He, J Deng… - Energy, 2024 - Elsevier
Accurate building energy predictions (BEPs) are crucial for maintaining a built environment's
sustainability and energy systems. Many data-driven BEPs rely heavily on sufficient data …
sustainability and energy systems. Many data-driven BEPs rely heavily on sufficient data …