Abstract: A system and method for installing solar panels are provided. The method obtains an image of a solar panel during an in-progress solar installation and estimates features of the solar panel based on a first image using distance simulation, geometric correction, and/or angular adjustment. The method also generates control signals, based on the estimated features, for operating a robotic controller for picking the solar panel. The method also obtains a second image of the solar panel when the solar panel is in a perspective view and detects placement of the solar panel based on the image by determining if the solar panel is co-planar with and at a predetermined offset from a fixed solar panel. Based on the detected placement, control signals are generated for operating a second robotic controller for aligning the solar panel with the fixed solar panel.

Abstract: A method and system for operating a wind farm by reconciling performance and operational constraints is disclosed. A wind farm may be subject to wakes, thereby reducing performance In order to lessen the effects of wakes, wake steering of the wind turbines may be performed. Specifically, both an operationally-independent analysis (such as by using a computational fluid dynamic model) and an operationally-dependent analysis may be performed, and the outputs of each analysis may be reconciled in order to determine whether (and how much) to wake steer.

Abstract: Various embodiments described herein relate to techniques for forecasting with state transitions and confidence factors. In this regard, a system is configured to segment data associated with one or more assets to determine a set of classifications for one or more attributes related to the one or more assets. The system is also configured to generate a state machine associated with a Markov chain model based on the set of classifications for the data. Furthermore, the system is configured to perform a machine learning process associated with the state machine to determine one or more behavior changes associated with the one or more attributes related to the one or more assets. The system is also configured to predict, based on the one or more behavior changes associated with the one or more attributes related to the one or more assets, a change in demand data for the one or more assets during a future interval of time.

Abstract: Various embodiments described herein relate to techniques for forecasting with state transitions and confidence factors. In this regard, a system is configured to segment data associated with one or more assets to determine a set of classifications for one or more attributes related to the one or more assets. The system is also configured to generate a state machine associated with a Markov chain model based on the set of classifications for the data. Furthermore, the system is configured to perform a machine learning process associated with the state machine to determine one or more behavior changes associated with the one or more attributes related to the one or more assets. The system is also configured to predict, based on the one or more behavior changes associated with the one or more attributes related to the one or more assets, a change in demand data for the one or more assets during a future interval of time.

Abstract: A system for determining a most probable cause of an event is presented. The system includes a processing subsystem coupled to a smart lighting subsystem and is configured to receive an event signature from the smart lighting subsystem, determine an event based on the event signature, determine an entropy of the event, connect with a plurality of historical data documents, determine information gain of at least a first subset of the plurality of historical data documents, identify and index a plurality of relevant documents from the first subset of the plurality of historical data documents based on the information gain and a determined threshold, determine one or more probable reasons of the event based on the plurality of relevant documents, and determine the most probable cause of the event at least based on the probable reasons, the plurality of relevant documents and the plurality of historical data documents.