High precision short-term photovoltaic (PV) power prediction can reduce the damage associated with large-scale photovoltaic grid-connection to the power system. In this paper, a combination deep learning forecasting method based on variational mode decomposition (VMD), a fast correlation-based filter (FCBF) and bidirectional long short-term memory (BiLSTM) network is developed to minimize PV power forecasting error. In this model, VMD is used to extract the trend feature of PV power, then FCBF is adopted to select the optimal input-set to reduce the forecasting error caused by the redundant feature. Finally, the input-set is put into the BiLSTM network for training and testing. The performance of this model is tested by a case study using the public data-set provided by a PV station in Australia. Comparisons with common short-term PV power forecasting models are also presented. The results show that under the processing of trend feature extraction and feature selection, the proposed methodology provides a more stable and accurate forecasting effect than other forecasting models.

Traditional electricity price forecasting tends to adopt time-domain forecasting methods based on time series, which fail to make full use of the regional information of the electricity market, and ignore the extra-territorial factors affecting electricity price within the region under cross-regional transmission conditions. In order to improve the accuracy of electricity price forecasting, this paper proposes a novel spatio-temporal prediction model, which is combined with the graph convolutional network (GCN) and the temporal convolutional network (TCN). First, the model automatically extracts the relationships between price areas through the graph construction module. Then, the mix-jump GCN is used to capture the spatial dependence, and the dilated splicing TCN is used to capture the temporal dependence and forecast electricity price for all price areas. The results show that the model outperforms other models in both one-step forecasting and multi-step forecasting, indicating that the model has superior performance in electricity price forecasting.

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