Low-carbon urban–rural modern energy systems with energy resilience under climate change and extreme events in China—A state-of-the-art review

Y Zhou - Energy and Buildings, 2024 - Elsevier
Climate-adaptive energy resilience and low-carbon transformation are mainstreams to
combat with climate change uncertainty, rural energy poverty, and urban modern energy …

Improved multistep ahead photovoltaic power prediction model based on LSTM and self-attention with weather forecast data

Z Hu, Y Gao, S Ji, M Mae, T Imaizumi - Applied Energy, 2024 - Elsevier
Accurate predictions of photovoltaic power generation (PV power) are essential for the
integration of renewable energy into grids, markets, and building energy management …

A state-of-the-art review on the utilization of machine learning in nanofluids, solar energy generation, and the prognosis of solar power

SK Singh, AK Tiwari, HK Paliwal - Engineering Analysis with Boundary …, 2023 - Elsevier
In the contemporary data-driven era, the fields of machine learning, deep learning, big data,
statistics, and data science are essential for forecasting outcomes and getting insights from …

Adversarial discriminative domain adaptation for solar radiation prediction: A cross-regional study for zero-label transfer learning in Japan

Y Gao, Z Hu, S Shi, WA Chen, M Liu - Applied Energy, 2024 - Elsevier
Deep learning models are increasingly applied in the field of solar radiation prediction.
However, the substantial demand for labeled data limits their rapid application in newly …

A multihead LSTM technique for prognostic prediction of soil moisture

P Datta, SA Faroughi - Geoderma, 2023 - Elsevier
Prognostic prediction of soil moisture is a critical step in various fields such as geotechnical
engineering, agriculture, geology, hydrology, and climatology. For example, in agricultural …

A multi-step ahead global solar radiation prediction method using an attention-based transformer model with an interpretable mechanism

Y Zhou, Y Li, D Wang, Y Liu - International Journal of Hydrogen Energy, 2023 - Elsevier
The conventional multi-step ahead solar radiation prediction method ignores the time-
dependence of a future solar radiation time series. Therefore, according to sequence-to …

Spatio-temporal interpretable neural network for solar irradiation prediction using transformer

Y Gao, S Miyata, Y Matsunami, Y Akashi - Energy and Buildings, 2023 - Elsevier
Deep learning models have been increasingly applied in the field of solar radiation
prediction. However, the characteristics of a deep learning black box model restrict its …

Model predictive control of a building renewable energy system based on a long short-term hybrid model

Y Gao, Y Matsunami, S Miyata, Y Akashi - Sustainable Cities and Society, 2023 - Elsevier
Considering solar photovoltaic (PV) usage in building energy systems (BES), energy
systems that combine batteries and solar PV (PVB) have been widely used in buildings …

Multi-agent reinforcement learning dealing with hybrid action spaces: A case study for off-grid oriented renewable building energy system

Y Gao, Y Matsunami, S Miyata, Y Akashi - Applied Energy, 2022 - Elsevier
With the application of renewable energy in building energy systems (BES), an increasing
number of power grids require building energy systems coupled to realize off-grid operation …

Deep learning model for the deformation prediction of concrete dams under multistep and multifeature inputs based on an improved autoformer

K Tian, J Yang, L Cheng - Engineering Applications of Artificial Intelligence, 2024 - Elsevier
The long-term prediction of deformation in concrete dams is a critical requirement for
maintaining their structural integrity over time in practical management scenarios. While …