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Register a new userDeep Photovoltaic Nowcasting
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Added by
Jinsong Zhang
Publication date
2018
fields
Informatics Engineering
and research's language is
English
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Predicting the short-term power output of a photovoltaic panel is an important task for the efficient management of smart grids. Short-term forecasting at the minute scale, also known as nowcasting, can benefit from sky images captured by regular cameras and installed close to the solar panel. However, estimating the weather conditions from these images---sun intensity, cloud appearance and movement, etc.---is a very challenging task that the community has yet to solve with traditional computer vision techniques. In this work, we propose to learn the relationship between sky appearance and the future photovoltaic power output using deep learning. We train several variants of convolutional neural networks which take historical photovoltaic power values and sky images as input and estimate photovoltaic power in a very short term future. In particular, we compare three different architectures based on: a multi-layer perceptron (MLP), a convolutional neural network (CNN), and a long short term memory (LSTM) module. We evaluate our approach quantitatively on a dataset of photovoltaic power values and corresponding images gathered in Kyoto, Japan. Our experiments reveal that the MLP network, already used similarly in previous work, achieves an RMSE skill score of 7% over the commonly-used persistence baseline on the 1-minute future photovoltaic power prediction task. Our CNN-based network improves upon this with a 12% skill score. In contrast, our LSTM-based model, which can learn the temporal dependencies in the data, achieves a 21% RMSE skill score, thus outperforming all other approaches.
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Precipitation nowcasting, the high-resolution forecasting of precipitation up to two hours ahead, supports the real-world socio-economic needs of many sectors reliant on weather-dependent decision-making. State-of-the-art operational nowcasting methods typically advect precipitation fields with radar-based wind estimates, and struggle to capture important non-linear events such as convective initiations. Recently introduced deep learning methods use radar to directly predict future rain rates, free of physical constraints. While they accurately predict low-intensity rainfall, their operational utility is limited because their lack of constraints produces blurry nowcasts at longer lead times, yielding poor performance on more rare medium-to-heavy rain events. To address these challenges, we present a Deep Generative Model for the probabilistic nowcasting of precipitation from radar. Our model produces realistic and spatio-temporally consistent predictions over regions up to 1536 km x 1280 km and with lead times from 5-90 min ahead. In a systematic evaluation by more than fifty expert forecasters from the Met Office, our generative model ranked first for its accuracy and usefulness in 88% of cases against two competitive methods, demonstrating its decision-making value and ability to provide physical insight to real-world experts. When verified quantitatively, these nowcasts are skillful without resorting to blurring. We show that generative nowcasting can provide probabilistic predictions that improve forecast value and support operational utility, and at resolutions and lead times where alternative methods struggle.
Could social media data aid in disaster response and damage assessment? Countries face both an increasing frequency and intensity of natural disasters due to climate change. And during such events, citizens are turning to social media platforms for disaster-related communication and information. Social media improves situational awareness, facilitates dissemination of emergency information, enables early warning systems, and helps coordinate relief efforts. Additionally, spatiotemporal distribution of disaster-related messages helps with real-time monitoring and assessment of the disaster itself. Here we present a multiscale analysis of Twitter activity before, during, and after Hurricane Sandy. We examine the online response of 50 metropolitan areas of the United States and find a strong relationship between proximity to Sandys path and hurricane-related social media activity. We show that real and perceived threats -- together with the physical disaster effects -- are directly observable through the intensity and composition of Twitters message stream. We demonstrate that per-capita Twitter activity strongly correlates with the per-capita economic damage inflicted by the hurricane. Our findings suggest that massive online social networks can be used for rapid assessment (nowcasting) of damage caused by a large-scale disaster.
At Paranal Observatory, the least predictable parameter affecting the short-term scheduling of astronomical observations is the optical turbulence, especially the seeing, coherence time and ground layer fraction. These are critical variables driving the performance of the instruments of the Very Large Telescope (VLT), especially those fed with adaptive optics systems. Currently, the night astronomer does not have a predictive tool to support him/her in decision-making at night. As most service-mode observations at the VLT last less than two hours, it is critical to be able to predict what will happen in this time frame, to avoid time losses due to sudden changes in the turbulence conditions, and also to enable more aggressive scheduling. We therefore investigate here the possibility to forecast the turbulence conditions over the next two hours. We call this turbulence nowcasting, analogously with weather nowcasting, a term already used in meteorology coming from the contraction of now and forecasting. We present here the results of a study based on historical data of the Paranal Astronomical Site Monitoring combined with ancillary data, in a machine learning framework. We show the strengths and shortcomings of such an approach, and present some perspectives in the context of the Extremely Large Telescope.
We develop a novel Bayesian framework for dynamic modeling of mixed frequency data to nowcast quarterly U.S. GDP growth. The introduced framework utilizes foundational Bayesian theory and treats data sampled at different frequencies as latent factors that are later synthesized, allowing flexible methodological specifications based on interests and utility. Time-varying inter-dependencies between the mixed frequency data are learnt and effectively mapped onto easily interpretable parameters. A macroeconomic study of nowcasting quarterly U.S. GDP growth using a number of monthly economic variables demonstrates improvements in terms of nowcast performance and interpretability compared to the standard in the literature. The study further shows that incorporating information during a quarter markedly improves the performance in terms of both point and density nowcasts.
Spintronics, which aims at exploiting the spin degree of freedom of carriers inside electronic devices, has a huge potential for quantum computation and dissipationless interconnects. Ideally, spin currents in spintronic devices should be powered by a spin voltage generator able to drive spins out of equilibrium and produce two spatially well-separated populations with opposite spin orientation. Such a generator should work at room temperature, be highly integrable with existing semiconductor technology, and work with neither ferromagnetic materials nor externally applied magnetic fields. We have matched these requirements by realizing the spintronic equivalent of a photovoltaic cell. While the latter spatially separates photoexcited electron and hole charges, our device exploits circularly polarized light to produce two spatially well-defined electron populations with opposite spin. This is achieved by modulating the phase and amplitude of the light wavefronts entering a semiconductor (germanium) with a patterned metal overlayer (platinum). This allows creating a light diffraction pattern with spatially-modulated chirality inside the semiconductor, which locally excites spin-polarized electrons thanks to electric dipole selection rules.
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