A review on global solar radiation prediction with machine learning models in a comprehensive perspective

Y Zhou, Y Liu, D Wang, X Liu, Y Wang - Energy Conversion and …, 2021 - Elsevier
Global solar radiation information is the basis for many solar energy utilizations as well as
for economic and environmental considerations. However, because solar-radiation …

Machine learning models to quantify and map daily global solar radiation and photovoltaic power

Y Feng, W Hao, H Li, N Cui, D Gong, L Gao - Renewable and Sustainable …, 2020 - Elsevier
Global solar radiation (R s) reaching Earth's surface is the primary information for the design
and application of solar energy-related systems. High-resolution R s measurements are …

The spatial distribution of China's solar energy resources and the optimum tilt angle and power generation potential of PV systems

J **g, Y Zhou, L Wang, Y Liu, D Wang - Energy Conversion and …, 2023 - Elsevier
This study aims at filling the gaps and completing the missing global solar radiation and
diffuse solar radiation data considering the limited radiation data of 85 meteorological …

Semi-experimental investigation on the energy performance of photovoltaic double skin façade with different façade materials

X Liu, H Yang, C Wang, C Shen, R Bo, L Hinkle… - Energy, 2024 - Elsevier
The photovoltaic double skin façade (PV-DSF) is a cutting-edge building envelope
renowned for its dynamic nature and power generation capabilities, which attracts …

Estimating 1-min beam and diffuse irradiance from the global irradiance: A review and an extensive worldwide comparison of latest separation models at 126 stations

D Yang - Renewable and Sustainable Energy Reviews, 2022 - Elsevier
Separation models, which are used to split beam and diffuse irradiance components from
the global one, constitute the largest class of radiation models. Over the years, there have …

Comparison of machine-learning models for predicting short-term building heating load using operational parameters

Y Zhou, Y Liu, D Wang, X Liu - Energy and buildings, 2021 - Elsevier
Short-term building energy consumption prediction is of great significance to the optimal
operation of building energy systems and conservation. Machine-learning models are …

A novel combined multi-task learning and Gaussian process regression model for the prediction of multi-timescale and multi-component of solar radiation

Y Zhou, Y Liu, D Wang, G De, Y Li, X Liu… - Journal of Cleaner …, 2021 - Elsevier
A novel combined multi-task learning and Gaussian process regression (MTGPR) model is
proposed to predict the multi-time scale (daily and monthly mean daily) and multi …

Comparison of support vector machine and copula-based nonlinear quantile regression for estimating the daily diffuse solar radiation: A case study in China

Y Liu, Y Zhou, Y Chen, D Wang, Y Wang, Y Zhu - Renewable Energy, 2020 - Elsevier
In this paper, three kinds of models, including support vector machine-firefly algorithm (SVM-
FFA), copula-base nonlinear quantile regression (CNQR) and empirical models were …

Groundwater level estimation using improved deep learning and soft computing methods

A Mirboluki, M Mehraein, O Kisi, A Kuriqi… - Earth Science …, 2024 - Springer
Estimating groundwater level (GWL) is an important issue for planning and managing
available water resources. This study uses monthly data from 86 observation wells from …

Comprehensive investigation on lighting and energy-saving performance of lighting/heating coupled tubular daylighting devices integrated with nanofluids

X Liu, C Shen, H Yang, J Wang - Applied Thermal Engineering, 2024 - Elsevier
Feasible regulation of the solar visible and infrared spectrum is a promising technology for
energy-efficient buildings. Our recently published work has proposed a lighting/heating …