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[IEEE 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC) - Chongqing (2018.6.27-2018.6.29)] 2018 IEEE 3rd International Conference on Image, Vision and Computing (ICIVC) - Distributed Compressive Video Sensing with Adaptive Reconstruction Based on Temporal Correlation
摘要： Aiming at enhance reconstruction quality, this paper proposes an adaptive reconstruction scheme with no feedback channel for distributed compressive video sensing, effectively exploiting temporal correlation. Specifically, the proposed scheme divides each block of non-key frames into different classifications based on temporal correlation in the encoding side and selects corresponding reconstruction mode which adaptively utilizes side information in the decoding side. The simulation results show that the proposed scheme achieves superior performance over existing methods in terms of reconstruction quality and computation cost.
关键词： adaptive reconstruction,distributed compressive video sensing,temporal correlation
更新于2025-09-23 15:22:29
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Data-Driven Photovoltaic Generation Forecasting based on Bayesian Network with Spatial-Temporal Correlation Analysis
摘要： Spatio-temporal analysis has been recognized as one of the most promising techniques to improve the accuracy of photovoltaic (PV) generation forecasts. In recent years, PV generation data of a number of PV systems distributed in a geographical locale have become increasingly available. This paper conducts a thorough investigation of the spatial-temporal correlation amongst PV generation data of distributed PV systems. PV generation data of different PV systems located at different sites may exhibit similar time varying patterns. To quantify such spatial correlation, a suitable spatial similarity metric is chosen and its applicability is examined. To evaluate the temporal correlations amongst PV generation data collected from distributed PV systems, a shape-based distance metric is proposed. A data-driven inference model, built on a Bayesian network, is developed for a very short-term PV generation forecast (less than 30 minutes). The model utilizes historic PV generation and weather data, and incorporates the above spatial similarity and temporal correlation to support the PV output forecast. The experiment results show that the proposed method achieves a promising performance compared to a number of baseline methods.
关键词： Photovoltaic (PV) output,Spatial and temporal correlation,Bayesian networks,Forecast
更新于2025-09-11 14:15:04
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Multi-Site Photovoltaic Forecasting Exploiting Space-Time Convolutional Neural Network
摘要： 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.
关键词： CNN,spatio-temporal correlation,multi-site photovoltaic forecasting,space-time matrix
更新于2025-09-11 14:15:04