The accurate forecasting of photovoltaic (PV) power generation is critical for smart grids and the renewable energy market. In this paper, we propose a novel short-term PV forecasting technique called the space-time convolutional neural network (STCNN) that exploits the location information of multiple PV sites and historical PV generation data. The proposed structure is simple but effective for multi-site PV forecasting. In doing this, we propose a greedy adjoining algorithm to preprocess PV data into a space-time matrix that captures spatio-temporal correlation, which is learned by a convolutional neural network. Extensive experiments with multi-site PV generation from three typical states in the US (California, New York, and Alabama) show that the proposed STCNN outperforms the conventional methods by up to 33% and achieves fairly accurate PV forecasting, e.g., 4.6–5.3% of the mean absolute percentage error for a 6 h forecasting horizon. We also investigate the effect of PV sites aggregation for virtual power plants where errors from some sites can be compensated by other sites. The proposed STCNN shows substantial error reduction by up to 40% when multiple PV sites are aggregated.

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In an era of high penetration of renewable energy, accurate photovoltaic (PV) power forecasting is crucial for balancing and scheduling power systems. However, PV power output has uncertainty since it depends on stochastic weather conditions. In this paper, we propose a novel short-term PV forecasting technique using Delaunay triangulation, of which the vertices are three weather stations that enclose a target PV site. By leveraging a Transformer encoder and gated recurrent unit (GRU), the proposed TransGRU model is robust against weather forecast error as it learns feature representation from weather data. We construct a framework based on Delaunay triangulation and TransGRU and verify that the proposed framework shows a 7–15% improvement compared to other state-of-the-art methods in terms of the normalized mean absolute error. Moreover, we investigate the effect of PV aggregation for virtual power plants where errors can be compensated across PV sites. Our framework demonstrates 41–60% improvement when PV sites are aggregated and achieves as low as 3–4% of forecasting error on average.

To save energy from cellular networks or to increase user-perceived performance, studying base station (BS) switching on-off is actively ongoing. However, many studies focus on the tradeoff between energy efficiency and user-perceived performance. In this paper, we propose a simple technique called cell flashing. Cell flashing means that base stations are turned on and off periodically and rapidly so that, when one base station is turned on, the adjacent base stations which make interferences are always off. Thus, both energy efficiency and cell edge user performances can be improved. In general, switching off base stations to save energy can lead to longer file download time (or delay) to customers. Using flow-level dynamics, we analyze average delay and energy consumption of cellular networks when cell flashing is used. We show that both of total energy consumption and average flow-level delay decrease in the case of small cells. Extensive simulations confirm that cell flashing can significantly save the energy of the base stations, e.g., by up to 25% and, at the same time, reduce the average delay by up to 75%.