High-precision, short-term power forecasting for photovoltaic systems not only reduces unnecessary energy consumption but also provides power grid security. To this end, in this paper we propose a photovoltaic short-term power forecasting model based on the division of data of the 24 traditional Chinese solar terms and the Adaboost-GA-BP model. The 24 solar terms were condensed from the laws of meteorology, phenology, and seasonal changes to adapt to agricultural times in ancient China and have become intangible cultural heritage. This article first analyzes the numerical characteristics of meteorological factors and demonstrates their close correlation with the turning points of the 24 solar terms. Second, using Standardized Euclidean Distance and Spearman’s Correlation Coefficients to analyze data similarity between the Gregorian half-months and the 24 solar terms divisions for comparative analysis purposes, it is shown that the intragroup data under the division of the 24 solar terms have a higher similarity, leading to an average decrease of 15.68%, 40.57%, 14.68%, and 14.64% in the MAE, MSE, RMSE, and WMAPE of the predicted results, respectively. Finally, based on the data derived from the 24 solar terms, the combined algorithm was compared with the Adaboost-GA-BP model and then was verified. The genetic algorithm and Adaboost were used to optimize the BP neural network algorithm in initial value assignment and neural network structure, resulting in a 23.42%, 18.12%, and 22.28% reduction in the mean values of the MAE, RMSE, and WMAPE of the predicted results, respectively. Analysis of the results show that using the Adaboost-GA-BP model based on the 24 solar terms for short-term photovoltaic power forecasting can improve the accuracy of photovoltaic power forecasting and significantly improve the predictive performance of the model.

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 $R^2$ 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.

Photovoltaic (PV) clusters in deserts such as the Gobi and other regions are frequently affected by sand and dust, which causes great deviation in power prediction and seriously threatens the safe operation of new power systems. For this reason, this paper proposes a short-term cluster PV power prediction method based on statistical upscaling, considering the effect of sand and dust. Firstly, the sand and dust events are identified, and then time series generative adversarial networks (TimeGANs) are used to solve the problem of small sample scarcity in sand and dust and construct a power correction model for sand and dust scenes. Secondly, for different weather scenes, a combination of conventional prediction and correction prediction is used to solve the problem of large differences in the predictability of a single model. Finally, a statistical upscaling method is utilized to calculate the cluster prediction power to solve the prediction difficulties of large-scale newly installed PV field stations. Through a case study and comparison with other models and methods, the cluster prediction method established in this paper effectively improves the prediction accuracy of the power of large-scale PV clusters affected by sand and dust, with the RMSE reduced by 8.28%.

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.