Load forecasting is critical for effective scheduling and operation of power systems, which are becoming increasingly complex and uncertain, especially with the penetration of distributed power. This paper proposes a data-driven deep learning framework to forecast the short-term power load. First, the load data is processed by Box-Cox transformation. The tail-dependence of the power load on electricity price and temperature is then investigated by fitting the parametric Copula models and computing the threshold of peak load. Next, a deep belief network is built to forecast the hourly load of the power system. One-year grid load data collected from urban areas in both Texas and Arkansas, in the United States, is utilized in the case studies on short-term load forecasting (day-ahead and week-ahead) is conducted for four seasons independently. The proposed framework is compared with classical neural networks, support vector regression machine, extreme learning machine, and classical deep belief networks. The load forecasting performance is evaluated using mean absolute percentage error, root mean square error, and hit rate. The proposed framework outperforms the tested state-of-the-art algorithms, with respect to the accuracies of both day-ahead and week-ahead forecasting. Overall, the computational results confirm the effectiveness of the proposed data-driven deep learning framework.

Wind power is a rapidly growing source of clean energy. Accurate short-term forecasting of wind power is essential for reliable energy generation. In this study, we propose a novel wind power forecasting approach using spatiotemporal analysis to enhance forecasting performance. First, the wind power time-series data from the target turbine and adjacent neighboring turbines were utilized to form a graph structure using graph neural networks (GNN). The graph structure was used to compute the spatiotemporal correlation between the target turbine and adjacent turbines. Then, the prediction models were trained using a deep residual network (DRN) for short-term wind power prediction. Considering the wind speed, the historic wind power, air density, and historic wind power in adjacent wind turbines within the supervisory control and data acquisition (SCADA) system were utilized. A comparative analysis was performed using conventional machine-learning approaches. Industrial data collected from Hami County, Xinjiang, China, were used for the case study. The computational results validate the superiority of the proposed approach for short-term wind-power forecasting.

Landslide displacement prediction is crucial for the early warning of slope failure but remains a challenging task due to its spatiotemporal complexity. Although temporal dependency has been well studied and discussed, spatial dependence is relatively less explored due to its significant variations of the spatial structure of landslides. In this study, a novel graph convolutional incorporating GRU network (GC-GRU-N) is proposed and applied to landslide displacement forecasts. The model conducts attribute-augmented graph convolution (GC) operations on GNSS displacement data with weighted adjacency matrices and an attribute-augmented unit to combine features, including the displacements, the distance, and other external influence factors to capture spatial dependence. The output of multi-weight graph convolution is then applied to the gated recurrent unit (GRU) network to learn temporal dependencies. The related optimal hyper-parameters are determined by comparison experiments. When applied to two typical landslide sites in the Three Gorge Reservoir (TGR), China, GC-GRU-N outperformed the comparative models in both cases. The ablation experiment results also show that the attribute augmentation, which considers external factors of landslide displacement, can further improve the model’s prediction performance. We conclude that the GC-GRU-N model can provide robust landslide displacement forecasting with high efficiency.