Abstract: According to some aspects, a system is provided comprising at least one computer readable storage medium storing a cache of flight information comprising a plurality of flight solutions, the cache capable of being accessed to obtain flight solutions that meet a criteria specified in one or more flight search queries, and at least one computer programmed to apply at least one machine learning model to at least some of the flight information in the flight information cache to classify at least one of the plurality of flight solutions according to an assessed fidelity of the at least one flight solution, and perform at least one action based on the classified at least one flight solution.

Abstract: The present invention is directed to a system and method for emulating smart grid devices in a smart grid for demand-response program analysis and optimization. Smart grid devices may be emulated in a virtual environment on a server, and can also be emulated individually on smart grid devices themselves. Demand-response programs can be simulated in a virtual environment with virtual emulated smart grid devices, or they can be simulated in a hybrid real-virtual environment with both real smart grid devices and virtual emulated smart grid devices. Demand-response programs can be simulated serially or in parallel. Additionally, such hybrid demand-response program simulations can be enhanced and optimized by including data obtained from the real smart grid devices into the simulation feed-back loop.

Abstract: According to some aspects, a system is provided comprising at least one computer readable storage medium storing a cache of flight information comprising a plurality of flight solutions, the cache capable of being accessed to obtain flight solutions that meet a criteria specified in one or more flight search queries, and at least one computer programmed to apply at least one machine learning model to at least some of the flight information in the flight information cache to classify at least one of the plurality of flight solutions according to an assessed fidelity of the at least one flight solution, and perform at least one action based on the classified at least one flight solution.

Abstract: According to some aspects, a system is provided comprising at least one computer readable storage medium storing a cache of flight information comprising a plurality of flight solutions, the cache capable of being accessed to obtain flight solutions that meet a criteria specified in one or more flight search queries, and at least one computer programmed to apply at least one machine learning model to at least some of the flight information in the flight information cache to classify at least one of the plurality of flight solutions according to an assessed fidelity of the at least one flight solution, and perform at least one action based on the classified at least one flight solution.

Abstract: The present invention is directed to a system and method for emulating smart grid devices in a smart grid for demand-response program analysis and optimization. Smart grid devices may be emulated in a virtual environment on a server, and can also be emulated individually on smart grid devices themselves. Demand-response programs can be simulated in a virtual environment with virtual emulated smart grid devices, or they can be simulated in a hybrid real-virtual environment with both real smart grid devices and virtual emulated smart grid devices. Demand-response programs can be simulated serially or in parallel. Additionally, such hybrid demand-response program simulations can be enhanced and optimized by including data obtained from the real smart grid devices into the simulation feed-back loop.

Abstract: According to some aspects, a system is provided comprising at least one computer readable storage medium storing a cache of flight information comprising a plurality of flight solutions, the cache capable of being accessed to obtain flight solutions that meet a criteria specified in one or more flight search queries, and at least one computer programmed to apply at least one machine learning model to at least some of the flight information in the flight information cache to classify at least one of the plurality of flight solutions according to an assessed fidelity of the at least one flight solution, and perform at least one action based on the classified at least one flight solution.