Haoyuan Ma
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Haoyuan Ma has a total of 13 co-authors. Here are the top co-authors by number of shared publications:
- Hongqing Wang
College of Science, Beijing Forestry University, Beijing 100083, China
2 shared publications - Bo Wang
State Key Laboratory of Renewable Energy Grid-Integration, China Electric Power Research Institute, Beijing 100192, China
1 shared publication - Chen Chen
Department of Mathematics, College of Science, Beijing Forestry University, Beijing 100083, China
1 shared publication - Hongbin Liu
Department of Mathematics, College of Science, Beijing Forestry University, Beijing 100083, China
1 shared publication - Jie Zhou
Department of Mathematics, College of Science, Beijing Forestry University, Beijing 100083, China
1 shared publication - Mengke Wang
College of Science, Beijing Forestry University, Beijing 100083, China
1 shared publication
Latest Publications
- Open Access
Wind–Solar Resource Assessment and Optimal Siting in Desert–Gobi–Wilderness Regions: A Case Study of the Badain Jaran and Kumtag Deserts
With the advancement of China’s “dual carbon” targets, Desert–Gobi–Wilderness (DGW) regions have become strategic areas for large-scale renewable energy deployment. However, the intermittency and variability of wind and solar resources pose challenges to power system stability, necessitating systematic evaluation of their characteristics and complementarity. This study uses ERA5 reanalysis data (2013–2023) to assess wind and solar resources in the Badain Jaran and Kumtag Deserts. A multi-dimensional framework is developed, incorporating availability, intermittency, variability, and complementarity, and a GIS-based multi-criteria decision-making method is applied for site selection. Results show that the Badain Jaran Desert is characterised by strong wind resources (average wind power density: 235.16 W/m2) and is suitable for wind-dominated development, whereas the Kumtag Desert exhibits superior solar resources (221.08 W/m2), favouring photovoltaic deployment. Significant wind–solar complementarity is identified, particularly in the central-western Badain Jaran and northeastern Kumtag regions. Three high-suitability sites were identified, including two in the Badain Jaran Desert and one in the Kumtag Desert, all characterised by favourable topographic conditions and high engineering feasibility. This study provides a scientific basis and a methodological framework for the planning of wind–solar hybrid systems and coordinated ecological development in DGW regions.
Open AccessWind–Solar Resource Assessment and Optimal Siting in Desert–Gobi–Wilderness Regions: A Case Study of the Badain Jaran and Kumtag Deserts
With the advancement of China’s “dual carbon” targets, Desert–Gobi–Wilderness (DGW) regions have become strategic areas for large-scale renewable energy deployment. However, the intermittency and variability of wind and solar resources pose challenges to power system stability, necessitating systematic evaluation of their characteristics and complementarity. This study uses ERA5 reanalysis data (2013–2023) to assess wind and solar resources in the Badain Jaran and Kumtag Deserts. A multi-dimensional framework is developed, incorporating availability, intermittency, variability, and complementarity, and a GIS-based multi-criteria decision-making method is applied for site selection. Results show that the Badain Jaran Desert is characterised by strong wind resources (average wind power density: 235.16 W/m2) and is suitable for wind-dominated development, whereas the Kumtag Desert exhibits superior solar resources (221.08 W/m2), favouring photovoltaic deployment. Significant wind–solar complementarity is identified, particularly in the central-western Badain Jaran and northeastern Kumtag regions. Three high-suitability sites were identified, including two in the Badain Jaran Desert and one in the Kumtag Desert, all characterised by favourable topographic conditions and high engineering feasibility. This study provides a scientific basis and a methodological framework for the planning of wind–solar hybrid systems and coordinated ecological development in DGW regions.
- Open Access
A Transformer-Based Hybrid Neural Network Integrating Multiresolution Turbulence Intensity and Independent Modeling of Multiple Meteorological Features for Wind Speed Forecasting
Aiming at the nonlinear, nonstationary, and multiscale fluctuation characteristics of wind speed series, this study proposes a wind speed-forecasting framework that integrates multi-resolution turbulence intensity features and a Transformer-based hybrid neural network. Firstly, based on multi-resolution turbulence intensity and stationary wavelet transform (SWT), the original wind speed series is decomposed into eight pairs of mean wind speeds and turbulence intensities at different time scales, which are then modeled and predicted in parallel using eight independent LSTM sub-models. Unlike traditional methods treating meteorological variables such as air pressure, temperature, and wind direction as static input features, WaveNet, LSTM, and TCN neural networks are innovatively adopted here to independently model and forecast these meteorological series, thoroughly capturing their dynamic influences on wind speed. Finally, a Transformer-based self-attention mechanism dynamically integrates multiple outputs from the four sub-models to generate final wind speed predictions. Experimental results averaged over three datasets demonstrate superior accuracy and robustness, with MAE, RMSE, MAPE, and values around 0.65, 0.87, 23.24%, and 0.92, respectively, for a 6 h forecast horizon. Moreover, the proposed framework consistently outperforms all baselines across four categories of comparative experiments, showing strong potential for practical applications in wind power dispatching.Open AccessA Transformer-Based Hybrid Neural Network Integrating Multiresolution Turbulence Intensity and Independent Modeling of Multiple Meteorological Features for Wind Speed Forecasting
Aiming at the nonlinear, nonstationary, and multiscale fluctuation characteristics of wind speed series, this study proposes a wind speed-forecasting framework that integrates multi-resolution turbulence intensity features and a Transformer-based hybrid neural network. Firstly, based on multi-resolution turbulence intensity and stationary wavelet transform (SWT), the original wind speed series is decomposed into eight pairs of mean wind speeds and turbulence intensities at different time scales, which are then modeled and predicted in parallel using eight independent LSTM sub-models. Unlike traditional methods treating meteorological variables such as air pressure, temperature, and wind direction as static input features, WaveNet, LSTM, and TCN neural networks are innovatively adopted here to independently model and forecast these meteorological series, thoroughly capturing their dynamic influences on wind speed. Finally, a Transformer-based self-attention mechanism dynamically integrates multiple outputs from the four sub-models to generate final wind speed predictions. Experimental results averaged over three datasets demonstrate superior accuracy and robustness, with MAE, RMSE, MAPE, and values around 0.65, 0.87, 23.24%, and 0.92, respectively, for a 6 h forecast horizon. Moreover, the proposed framework consistently outperforms all baselines across four categories of comparative experiments, showing strong potential for practical applications in wind power dispatching.