A Hybrid Data-Driven Online Solar Energy Disaggregation System from the Grid Supply Point

The integration of small-scale PV systems (such as roof-top PVs) decreases the visibility of the power system since the real demand load is masked. Most of the rooftop systems are behind-the-meter and cannot be measured by the household smart meter. To overcome the challenges mentioned above, this paper proposes an online solar energy disaggregation system to decouple the solar energy generated by the roof-top PV systems and ground truth demand load from the net measurements. A 1D CNN bidirectional long short-term memory (CNN-BiLSTM) deep learning method is used as the core algorithm of the proposed system. The system takes a wide range of online information (AMI data, meteorological data, satellite-driven irradiance, and temporal information) as inputs to evaluate the PV generation, and the system also enables online and offline modes. The effectiveness of the proposed algorithm is evaluated by comparing it to baselines. The results show that the proposed method reaches good performance under different penetration rates and different feeder levels. Finally, a transfer learning process is introduced to verify the proposed system has good robustness and can be applied to anywhere else easily.

A Hybrid Data-Driven Online Solar Energy Disaggregation System from the Grid Supply Point

The integration of small-scale PV systems (such as roof-top PVs) decreases the visibility of the power system since the real demand load is masked. Most of the rooftop systems are behind-the-meter and cannot be measured by the household smart meter. To overcome the challenges mentioned above, this paper proposes an online solar energy disaggregation system to decouple the solar energy generated by the roof-top PV systems and ground truth demand load from the net measurements. A 1D CNN bidirectional long short-term memory (CNN-BiLSTM) deep learning method is used as the core algorithm of the proposed system. The system takes a wide range of online information (AMI data, meteorological data, satellite-driven irradiance, and temporal information) as inputs to evaluate the PV generation, and the system also enables online and offline modes. The effectiveness of the proposed algorithm is evaluated by comparing it to baselines. The results show that the proposed method reaches good performance under different penetration rates and different feeder levels. Finally, a transfer learning process is introduced to verify the proposed system has good robustness and can be applied to anywhere else easily.

Chaperones directly and efficiently disperse stress-triggered biomolecular condensates

Heat shock triggers formation of intracellular protein aggregates and induction of a molecular disaggregation system. Although this system (Hsp100/Hsp70/Hsp40 in most cellular life) can disperse aggregates of model misfolded proteins, its activity on these model substrates is puzzlingly weak, and its endogenous heat-induced substrates have largely eluded biochemical study. Recent work has revealed that several cases of apparent heat-induced aggregation instead reflect evolved, adaptive biomolecular condensation. In budding yeast Saccharomyces cerevisiae, the resulting condensates depend on molecular chaperones for timely dispersal in vivo, hinting that condensates may be major endogenous substrates of the disaggregation system. Here, we show that the yeast disaggregation system disperses heat-induced biomolecular condensates of poly(A)-binding protein (Pab1) orders of magnitude more rapidly than aggregates of the most commonly used model substrate, firefly luciferase. Pab1 condensate dispersal also differs from aggregate dispersal in its molecular requirements, showing no dependence on small heat-shock proteins and a strict requirement for type II Hsp40. Unlike luciferase, Pab1 is not fully threaded (and thus not fully unfolded) by the disaggregase Hsp104 during dispersal, which we show can contribute to the extreme differences in dispersal efficiency. The Hsp70-related disaggregase Hsp110 shows some Pab1 dispersal activity, a potentially important link to animal systems, which lack cytosolic Hsp104. Finally, we show that the long-observed dependence of the disaggregation system on excess Hsp70 stems from the precise mechanism of the disaggregation system, which depends on the presence of multiple, closely spaced Hsp70s for Hsp104 recruitment and activation. Our results establish heat-induced biomolecular condensates of Pab1 as a direct endogenous substrate of the disaggregation machinery which differs markedly from previously studied foreign substrates, opening a crucial new window into the native mechanistic behavior and biological roles of this ancient system.